categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.18845 | null | null | http://arxiv.org/abs/2405.18845v1 | 2024-05-29T07:56:08Z | 2024-05-29T07:56:08Z | Simulation, Modelling and Classification of Wiki Contributors: Spotting
The Good, The Bad, and The Ugly | Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adversarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage - a free worldwide wiki travel guide open to contribution from the general public - as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92 %. | [
"['Silvia García Méndez' 'Fátima Leal' 'Benedita Malheiro'\n 'Juan Carlos Burguillo Rial' 'Bruno Veloso' 'Adriana E. Chis'\n 'Horacio González Vélez']"
] |
null | null | 2405.18848 | null | null | http://arxiv.org/pdf/2405.18848v1 | 2024-05-29T07:59:06Z | 2024-05-29T07:59:06Z | Anomaly Detection by Context Contrasting | Anomaly Detection focuses on identifying samples that deviate from the norm. When working with high-dimensional data such as images, a crucial requirement for detecting anomalous patterns is learning lower-dimensional representations that capture normal concepts seen during training. Recent advances in self-supervised learning have shown great promise in this regard. However, many of the most successful self-supervised anomaly detection methods assume prior knowledge about the structure of anomalies and leverage synthetic anomalies during training. Yet, in many real-world applications, we do not know what to expect from unseen data, and we can solely leverage knowledge about normal data. In this work, we propose Con2, which addresses this problem by setting normal training data into distinct contexts while preserving its normal properties, letting us observe the data from different perspectives. Unseen normal data consequently adheres to learned context representations while anomalies fail to do so, letting us detect them without any knowledge about anomalies during training. Our experiments demonstrate that our approach achieves state-of-the-art performance on various benchmarks while exhibiting superior performance in a more realistic healthcare setting, where knowledge about potential anomalies is often scarce. | [
"['Alain Ryser' 'Thomas M. Sutter' 'Alexander Marx' 'Julia E. Vogt']"
] |
null | null | 2405.18861 | null | null | http://arxiv.org/pdf/2405.18861v1 | 2024-05-29T08:22:33Z | 2024-05-29T08:22:33Z | Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts | This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts. It is motivated by the inconsistent convergence degree of SAM across different domains, which induces optimization bias towards certain domains and thus impairs the overall convergence. To address this issue, we consider the domain-level convergence consistency in the sharpness estimation to prevent the overwhelming (deficient) perturbations for less (well) optimized domains. Specifically, DISAM introduces the constraint of minimizing variance in the domain loss, which allows the elastic gradient calibration in perturbation generation: when one domain is optimized above the averaging level textit{w.r.t.} loss, the gradient perturbation towards that domain will be weakened automatically, and vice versa. Under this mechanism, we theoretically show that DISAM can achieve faster overall convergence and improved generalization in principle when inconsistent convergence emerges. Extensive experiments on various domain generalization benchmarks show the superiority of DISAM over a range of state-of-the-art methods. Furthermore, we show the superior efficiency of DISAM in parameter-efficient fine-tuning combined with the pretraining models. The source code is released at https://github.com/MediaBrain-SJTU/DISAM. | [
"['Ruipeng Zhang' 'Ziqing Fan' 'Jiangchao Yao' 'Ya Zhang' 'Yanfeng Wang']"
] |
null | null | 2405.18869 | null | null | http://arxiv.org/pdf/2405.18869v1 | 2024-05-29T08:30:34Z | 2024-05-29T08:30:34Z | Towards Data-Driven Electricity Management: Multi-Region Harmonized Data
and Knowledge Graph | Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing. The residential sector makes up 25% of global electricity consumption and has great potential to increase efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development. | [
"['Vid Hanžel' 'Blaž Bertalanič' 'Carolina Fortuna']"
] |
null | null | 2405.18871 | null | null | http://arxiv.org/pdf/2405.18871v1 | 2024-05-29T08:31:34Z | 2024-05-29T08:31:34Z | DFAMiner: Mining minimal separating DFAs from labelled samples | We propose DFAMiner, a passive learning tool for learning minimal separating deterministic finite automata (DFA) from a set of labelled samples. Separating automata are an interesting class of automata that occurs generally in regular model checking and has raised interest in foundational questions of parity game solving. We first propose a simple and linear-time algorithm that incrementally constructs a three-valued DFA (3DFA) from a set of labelled samples given in the usual lexicographical order. This 3DFA has accepting and rejecting states as well as don't-care states, so that it can exactly recognise the labelled examples. We then apply our tool to mining a minimal separating DFA for the labelled samples by minimising the constructed automata via a reduction to solving SAT problems. Empirical evaluation shows that our tool outperforms current state-of-the-art tools significantly on standard benchmarks for learning minimal separating DFAs from samples. Progress in the efficient construction of separating DFAs can also lead to finding the lower bound of parity game solving, where we show that DFAMiner can create optimal separating automata for simple languages with up to 7 colours. Future improvements might offer inroads to better data structures. | [
"[\"Daniele Dell'Erba\" 'Yong Li' 'Sven Schewe']"
] |
null | null | 2405.18877 | null | null | http://arxiv.org/pdf/2405.18877v1 | 2024-05-29T08:36:09Z | 2024-05-29T08:36:09Z | Continuous Product Graph Neural Networks | Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science. However, current methods are mostly limited to discrete graph filtering operations. Tensorial partial differential equations on graphs (TPDEGs) provide a principled framework for modeling structured data across multiple interacting graphs, addressing the limitations of the existing discrete methodologies. In this paper, we introduce Continuous Product Graph Neural Networks (CITRUS) that emerge as a natural solution to the TPDEG. CITRUS leverages the separability of continuous heat kernels from Cartesian graph products to efficiently implement graph spectral decomposition. We conduct thorough theoretical analyses of the stability and over-smoothing properties of CITRUS in response to domain-specific graph perturbations and graph spectra effects on the performance. We evaluate CITRUS on well-known traffic and weather spatiotemporal forecasting datasets, demonstrating superior performance over existing approaches. | [
"['Aref Einizade' 'Fragkiskos D. Malliaros' 'Jhony H. Giraldo']"
] |
null | null | 2405.18878 | null | null | http://arxiv.org/pdf/2405.18878v1 | 2024-05-29T08:36:42Z | 2024-05-29T08:36:42Z | Privacy Preserving Data Imputation via Multi-party Computation for
Medical Applications | Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and effective methods, thereby improving sample size and accuracy. However, these methods require accessing the whole dataset, which contradicts the privacy regulations when the data is distributed among multiple sources. Especially in the medical and healthcare domain, such access reveals sensitive information about patients. This study addresses privacy-preserving imputation methods for sensitive data using secure multi-party computation, enabling secure computations without revealing any party's sensitive information. In this study, we realized the mean, median, regression, and kNN imputation methods in a privacy-preserving way. We specifically target the medical and healthcare domains considering the significance of protection of the patient data, showcasing our methods on a diabetes dataset. Experiments on the diabetes dataset validated the correctness of our privacy-preserving imputation methods, yielding the largest error around $3 times 10^{-3}$, closely matching plaintext methods. We also analyzed the scalability of our methods to varying numbers of samples, showing their applicability to real-world healthcare problems. Our analysis demonstrated that all our methods scale linearly with the number of samples. Except for kNN, the runtime of all our methods indicates that they can be utilized for large datasets. | [
"['Julia Jentsch' 'Ali Burak Ünal' 'Şeyma Selcan Mağara' 'Mete Akgün']"
] |
null | null | 2405.18879 | null | null | http://arxiv.org/pdf/2405.18879v1 | 2024-05-29T08:37:48Z | 2024-05-29T08:37:48Z | Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural
Networks | Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes in a graph. However, current methods often rely on Recurrent Neural Networks (RNNs), leading to increased runtimes and memory use. Moreover, these methods typically operate within 1-hop neighborhoods, exacerbating the reduction of the receptive field. Causal Graph Processes (CGPs) offer an alternative, using graph filters instead of MLP layers to reduce parameters and minimize memory consumption. This paper introduces the Causal Graph Process Neural Network (CGProNet), a non-linear model combining CGPs and GNNs for spatiotemporal forecasting. CGProNet employs higher-order graph filters, optimizing the model with fewer parameters, reducing memory usage, and improving runtime efficiency. We present a comprehensive theoretical and experimental stability analysis, highlighting key aspects of CGProNet. Experiments on synthetic and real data demonstrate CGProNet's superior efficiency, minimizing memory and time requirements while maintaining competitive forecasting performance. | [
"['Aref Einizade' 'Fragkiskos D. Malliaros' 'Jhony H. Giraldo']"
] |
null | null | 2405.18881 | null | null | http://arxiv.org/pdf/2405.18881v2 | 2024-07-03T05:45:45Z | 2024-05-29T08:39:39Z | Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization | In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives for downstream tasks, such as improving human preference. The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization (DNO), that optimizes the injected noise during the sampling process of diffusion models. By design, DNO is tuning-free and prompt-agnostic, as the alignment occurs in an online fashion during generation. We rigorously study the theoretical properties of DNO and also propose variants to deal with non-differentiable reward functions. Furthermore, we identify that naive implementation of DNO occasionally suffers from the out-of-distribution reward hacking problem, where optimized samples have high rewards but are no longer in the support of the pretrained distribution. To remedy this issue, we leverage classical high-dimensional statistics theory and propose to augment the DNO loss with certain probability regularization. We conduct extensive experiments on several popular reward functions trained on human feedback data and demonstrate that the proposed DNO approach achieves state-of-the-art reward scores as well as high image quality, all within a reasonable time budget for generation. | [
"['Zhiwei Tang' 'Jiangweizhi Peng' 'Jiasheng Tang' 'Mingyi Hong' 'Fan Wang'\n 'Tsung-Hui Chang']"
] |
null | null | 2405.18886 | null | null | http://arxiv.org/pdf/2405.18886v1 | 2024-05-29T08:42:30Z | 2024-05-29T08:42:30Z | Compressing Large Language Models using Low Rank and Low Precision
Decomposition | The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inherent low-rank structure of a weight matrix $mathbf{W}$ by approximating it via a low-rank, low-precision decomposition as $mathbf{W} approx mathbf{Q} + mathbf{L}mathbf{R}$. Here, $mathbf{L}$ and $mathbf{R}$ are low rank factors, and the entries of $mathbf{Q}$, $mathbf{L}$ and $mathbf{R}$ are quantized. The model is compressed by substituting each layer with its $mathbf{Q} + mathbf{L}mathbf{R}$ decomposition, and the zero-shot performance of the compressed model is evaluated. Additionally, $mathbf{L}$ and $mathbf{R}$ are readily amenable to low-rank adaptation, consequently enhancing the zero-shot performance. $rm CALDERA$ obtains this decomposition by formulating it as an optimization problem $min_{mathbf{Q},mathbf{L},mathbf{R}}lVert(mathbf{Q} + mathbf{L}mathbf{R} - mathbf{W})mathbf{X}^toprVert_{rm F}^2$, where $mathbf{X}$ is the calibration data, and $mathbf{Q}, mathbf{L}, mathbf{R}$ are constrained to be representable using low-precision formats. Theoretical upper bounds on the approximation error of $rm CALDERA$ are established using a rank-constrained regression framework, and the tradeoff between compression ratio and model performance is studied by analyzing the impact of target rank and quantization bit budget. Results illustrate that compressing LlaMa-$2$ $7$B/$70$B and LlaMa-$3$ $8$B models obtained using $rm CALDERA$ outperforms existing post-training LLM compression techniques in the regime of less than $2.5$ bits per parameter. The implementation is available at: href{https://github.com/pilancilab/caldera}{https://github.com/pilancilab/caldera}. | [
"['Rajarshi Saha' 'Naomi Sagan' 'Varun Srivastava' 'Andrea J. Goldsmith'\n 'Mert Pilanci']"
] |
null | null | 2405.18888 | null | null | http://arxiv.org/pdf/2405.18888v1 | 2024-05-29T08:45:04Z | 2024-05-29T08:45:04Z | Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in
Residential Households based on Deep Reinforcement Learning | Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper proposes a novel deep reinforcement learning-based load-shaping algorithm (PLS-DQN) designed to protect user privacy by proactively creating artificial load signatures that mislead potential attackers. We evaluate our proposed algorithm against a non-intrusive load monitoring (NILM) adversary. The results demonstrate that our approach not only effectively conceals real energy usage patterns but also outperforms state-of-the-art methods in enhancing user privacy while maintaining cost efficiency. | [
"['Ruichang Zhang' 'Youcheng Sun' 'Mustafa A. Mustafa']"
] |
null | null | 2405.18890 | null | null | http://arxiv.org/pdf/2405.18890v1 | 2024-05-29T08:46:21Z | 2024-05-29T08:46:21Z | Locally Estimated Global Perturbations are Better than Local
Perturbations for Federated Sharpness-aware Minimization | In federated learning (FL), the multi-step update and data heterogeneity among clients often lead to a loss landscape with sharper minima, degenerating the performance of the resulted global model. Prevalent federated approaches incorporate sharpness-aware minimization (SAM) into local training to mitigate this problem. However, the local loss landscapes may not accurately reflect the flatness of global loss landscape in heterogeneous environments; as a result, minimizing local sharpness and calculating perturbations on client data might not align the efficacy of SAM in FL with centralized training. To overcome this challenge, we propose FedLESAM, a novel algorithm that locally estimates the direction of global perturbation on client side as the difference between global models received in the previous active and current rounds. Besides the improved quality, FedLESAM also speed up federated SAM-based approaches since it only performs once backpropagation in each iteration. Theoretically, we prove a slightly tighter bound than its original FedSAM by ensuring consistent perturbation. Empirically, we conduct comprehensive experiments on four federated benchmark datasets under three partition strategies to demonstrate the superior performance and efficiency of FedLESAM. | [
"['Ziqing Fan' 'Shengchao Hu' 'Jiangchao Yao' 'Gang Niu' 'Ya Zhang'\n 'Masashi Sugiyama' 'Yanfeng Wang']"
] |
null | null | 2405.18894 | null | null | http://arxiv.org/pdf/2405.18894v1 | 2024-05-29T08:53:16Z | 2024-05-29T08:53:16Z | Few-Shot Testing: Estimating Uncertainty of Memristive Deep Neural
Networks Using One Bayesian Test Vector | The performance of deep learning algorithms such as neural networks (NNs) has increased tremendously recently, and they can achieve state-of-the-art performance in many domains. However, due to memory and computation resource constraints, implementing NNs on edge devices is a challenging task. Therefore, hardware accelerators such as computation-in-memory (CIM) with memristive devices have been developed to accelerate the most common operations, i.e., matrix-vector multiplication. However, due to inherent device properties, external environmental factors such as temperature, and an immature fabrication process, memristors suffer from various non-idealities, including defects and variations occurring during manufacturing and runtime. Consequently, there is a lack of complete confidence in the predictions made by the model. To improve confidence in NN predictions made by hardware accelerators in the presence of device non-idealities, in this paper, we propose a Bayesian test vector generation framework that can estimate the model uncertainty of NNs implemented on memristor-based CIM hardware. Compared to the conventional point estimate test vector generation method, our method is more generalizable across different model dimensions and requires storing only one test Bayesian vector in the hardware. Our method is evaluated on different model dimensions, tasks, fault rates, and variation noise to show that it can consistently achieve $100%$ coverage with only $0.024$ MB of memory overhead. | [
"['Soyed Tuhin Ahmed' 'Mehdi Tahoori']"
] |
null | null | 2405.18896 | null | null | http://arxiv.org/pdf/2405.18896v1 | 2024-05-29T08:57:00Z | 2024-05-29T08:57:00Z | Unit-Aware Genetic Programming for the Development of Empirical
Equations | When developing empirical equations, domain experts require these to be accurate and adhere to physical laws. Often, constants with unknown units need to be discovered alongside the equations. Traditional unit-aware genetic programming (GP) approaches cannot be used when unknown constants with undetermined units are included. This paper presents a method for dimensional analysis that propagates unknown units as ''jokers'' and returns the magnitude of unit violations. We propose three methods, namely evolutive culling, a repair mechanism, and a multi-objective approach, to integrate the dimensional analysis in the GP algorithm. Experiments on datasets with ground truth demonstrate comparable performance of evolutive culling and the multi-objective approach to a baseline without dimensional analysis. Extensive analysis of the results on datasets without ground truth reveals that the unit-aware algorithms make only low sacrifices in accuracy, while producing unit-adherent solutions. Overall, we presented a promising novel approach for developing unit-adherent empirical equations. | [
"['Julia Reuter' 'Viktor Martinek' 'Roland Herzog' 'Sanaz Mostaghim']"
] |
null | null | 2405.18902 | null | null | http://arxiv.org/pdf/2405.18902v1 | 2024-05-29T09:03:44Z | 2024-05-29T09:03:44Z | A Causal Framework for Evaluating Deferring Systems | Deferring systems extend supervised Machine Learning (ML) models with the possibility to defer predictions to human experts. However, evaluating the impact of a deferring strategy on system accuracy is still an overlooked area. This paper fills this gap by evaluating deferring systems through a causal lens. We link the potential outcomes framework for causal inference with deferring systems. This allows us to identify the causal impact of the deferring strategy on predictive accuracy. We distinguish two scenarios. In the first one, we can access both the human and the ML model predictions for the deferred instances. In such a case, we can identify the individual causal effects for deferred instances and aggregates of them. In the second scenario, only human predictions are available for the deferred instances. In this case, we can resort to regression discontinuity design to estimate a local causal effect. We empirically evaluate our approach on synthetic and real datasets for seven deferring systems from the literature. | [
"['Filippo Palomba' 'Andrea Pugnana' 'José Manuel Alvarez'\n 'Salvatore Ruggieri']"
] |
null | null | 2405.18906 | null | null | http://arxiv.org/pdf/2405.18906v1 | 2024-05-29T09:09:00Z | 2024-05-29T09:09:00Z | Language Generation with Strictly Proper Scoring Rules | Language generation based on maximum likelihood estimation (MLE) has become the fundamental approach for text generation. Maximum likelihood estimation is typically performed by minimizing the log-likelihood loss, also known as the logarithmic score in statistical decision theory. The logarithmic score is strictly proper in the sense that it encourages honest forecasts, where the expected score is maximized only when the model reports true probabilities. Although many strictly proper scoring rules exist, the logarithmic score is the only local scoring rule among them that depends exclusively on the probability of the observed sample, making it capable of handling the exponentially large sample space of natural text. In this work, we propose a straightforward strategy for adapting scoring rules to language generation, allowing for language modeling with any non-local scoring rules. Leveraging this strategy, we train language generation models using two classic strictly proper scoring rules, the Brier score and the Spherical score, as alternatives to the logarithmic score. Experimental results indicate that simply substituting the loss function, without adjusting other hyperparameters, can yield substantial improvements in model's generation capabilities. Moreover, these improvements can scale up to large language models (LLMs) such as LLaMA-7B and LLaMA-13B. Source code: url{https://github.com/shaochenze/ScoringRulesLM}. | [
"['Chenze Shao' 'Fandong Meng' 'Yijin Liu' 'Jie Zhou']"
] |
null | null | 2405.18913 | null | null | http://arxiv.org/pdf/2405.18913v1 | 2024-05-29T09:16:03Z | 2024-05-29T09:16:03Z | Leveraging Time-Series Foundation Models in Smart Agriculture for Soil
Moisture Forecasting | The recent surge in foundation models for natural language processing and computer vision has fueled innovation across various domains. Inspired by this progress, we explore the potential of foundation models for time-series forecasting in smart agriculture, a field often plagued by limited data availability. Specifically, this work presents a novel application of $texttt{TimeGPT}$, a state-of-the-art (SOTA) time-series foundation model, to predict soil water potential ($psi_mathrm{soil}$), a key indicator of field water status that is typically used for irrigation advice. Traditionally, this task relies on a wide array of input variables. We explore $psi_mathrm{soil}$'s ability to forecast $psi_mathrm{soil}$ in: ($i$) a zero-shot setting, ($ii$) a fine-tuned setting relying solely on historic $psi_mathrm{soil}$ measurements, and ($iii$) a fine-tuned setting where we also add exogenous variables to the model. We compare $texttt{TimeGPT}$'s performance to established SOTA baseline models for forecasting $psi_mathrm{soil}$. Our results demonstrate that $texttt{TimeGPT}$ achieves competitive forecasting accuracy using only historical $psi_mathrm{soil}$ data, highlighting its remarkable potential for agricultural applications. This research paves the way for foundation time-series models for sustainable development in agriculture by enabling forecasting tasks that were traditionally reliant on extensive data collection and domain expertise. | [
"['Boje Deforce' 'Bart Baesens' 'Estefanía Serral Asensio']"
] |
null | null | 2405.18917 | null | null | http://arxiv.org/pdf/2405.18917v1 | 2024-05-29T09:19:50Z | 2024-05-29T09:19:50Z | Causal Action Influence Aware Counterfactual Data Augmentation | Offline data are both valuable and practical resources for teaching robots complex behaviors. Ideally, learning agents should not be constrained by the scarcity of available demonstrations, but rather generalize beyond the training distribution. However, the complexity of real-world scenarios typically requires huge amounts of data to prevent neural network policies from picking up on spurious correlations and learning non-causal relationships. We propose CAIAC, a data augmentation method that can create feasible synthetic transitions from a fixed dataset without having access to online environment interactions. By utilizing principled methods for quantifying causal influence, we are able to perform counterfactual reasoning by swapping $it{action}$-unaffected parts of the state-space between independent trajectories in the dataset. We empirically show that this leads to a substantial increase in robustness of offline learning algorithms against distributional shift. | [
"['Núria Armengol Urpí' 'Marco Bagatella' 'Marin Vlastelica'\n 'Georg Martius']"
] |
null | null | 2405.18918 | null | null | http://arxiv.org/pdf/2405.18918v1 | 2024-05-29T09:20:54Z | 2024-05-29T09:20:54Z | Computing low-thrust transfers in the asteroid belt, a comparison
between astrodynamical manipulations and a machine learning approach | Low-thrust trajectories play a crucial role in optimizing scientific output and cost efficiency in asteroid belt missions. Unlike high-thrust transfers, low-thrust trajectories require solving complex optimal control problems. This complexity grows exponentially with the number of asteroids visited due to orbital mechanics intricacies. In the literature, methods for approximating low-thrust transfers without full optimization have been proposed, including analytical and machine learning techniques. In this work, we propose new analytical approximations and compare their accuracy and performance to machine learning methods. While analytical approximations leverage orbit theory to estimate trajectory costs, machine learning employs a more black-box approach, utilizing neural networks to predict optimal transfers based on various attributes. We build a dataset of about 3 million transfers, found by solving the time and fuel optimal control problems, for different time of flights, which we also release open-source. Comparison between the two methods on this database reveals the superiority of machine learning, especially for longer transfers. Despite challenges such as multi revolution transfers, both approaches maintain accuracy within a few percent in the final mass errors, on a database of trajectories involving numerous asteroids. This work contributes to the efficient exploration of mission opportunities in the asteroid belt, providing insights into the strengths and limitations of different approximation strategies. | [
"['Giacomo Acciarini' 'Laurent Beauregard' 'Dario Izzo']"
] |
null | null | 2405.18921 | null | null | http://arxiv.org/pdf/2405.18921v1 | 2024-05-29T09:24:25Z | 2024-05-29T09:24:25Z | GLANCE: Global Actions in a Nutshell for Counterfactual Explainability | Counterfactual explanations have emerged as an important tool to understand, debug, and audit complex machine learning models. To offer global counterfactual explainability, state-of-the-art methods construct summaries of local explanations, offering a trade-off among conciseness, counterfactual effectiveness, and counterfactual cost or burden imposed on instances. In this work, we provide a concise formulation of the problem of identifying global counterfactuals and establish principled criteria for comparing solutions, drawing inspiration from Pareto dominance. We introduce innovative algorithms designed to address the challenge of finding global counterfactuals for either the entire input space or specific partitions, employing clustering and decision trees as key components. Additionally, we conduct a comprehensive experimental evaluation, considering various instances of the problem and comparing our proposed algorithms with state-of-the-art methods. The results highlight the consistent capability of our algorithms to generate meaningful and interpretable global counterfactual explanations. | [
"['Ioannis Emiris' 'Dimitris Fotakis' 'Giorgos Giannopoulos'\n 'Dimitrios Gunopulos' 'Loukas Kavouras' 'Kleopatra Markou'\n 'Eleni Psaroudaki' 'Dimitrios Rontogiannis' 'Dimitris Sacharidis'\n 'Nikolaos Theologitis' 'Dimitrios Tomaras' 'Konstantinos Tsopelas']"
] |
null | null | 2405.18925 | null | null | http://arxiv.org/pdf/2405.18925v2 | 2024-07-03T10:22:04Z | 2024-05-29T09:29:39Z | Federated Continual Learning Goes Online: Leveraging Uncertainty for
Modality-Agnostic Class-Incremental Learning | Given the ability to model more realistic and dynamic problems, Federated Continual Learning (FCL) has been increasingly investigated recently. A well-known problem encountered in this setting is the so-called catastrophic forgetting, for which the learning model is inclined to focus on more recent tasks while forgetting the previously learned knowledge. The majority of the current approaches in FCL propose generative-based solutions to solve said problem. However, this setting requires multiple training epochs over the data, implying an offline setting where datasets are stored locally and remain unchanged over time. Furthermore, the proposed solutions are tailored for vision tasks solely. To overcome these limitations, we propose a new modality-agnostic approach to deal with the online scenario where new data arrive in streams of mini-batches that can only be processed once. To solve catastrophic forgetting, we propose an uncertainty-aware memory-based approach. In particular, we suggest using an estimator based on the Bregman Information (BI) to compute the model's variance at the sample level. Through measures of predictive uncertainty, we retrieve samples with specific characteristics, and - by retraining the model on such samples - we demonstrate the potential of this approach to reduce the forgetting effect in realistic settings. | [
"['Giuseppe Serra' 'Florian Buettner']"
] |
null | null | 2405.18929 | null | null | http://arxiv.org/pdf/2405.18929v1 | 2024-05-29T09:34:47Z | 2024-05-29T09:34:47Z | Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled
Data | Semi-supervised anomaly detection, which aims to improve the performance of the anomaly detector by using a small amount of anomaly data in addition to unlabeled data, has attracted attention. Existing semi-supervised approaches assume that unlabeled data are mostly normal. They train the anomaly detector to minimize the anomaly scores for the unlabeled data, and to maximize those for the anomaly data. However, in practice, the unlabeled data are often contaminated with anomalies. This weakens the effect of maximizing the anomaly scores for anomalies, and prevents us from improving the detection performance. To solve this problem, we propose the positive-unlabeled autoencoder, which is based on positive-unlabeled learning and the anomaly detector such as the autoencoder. With our approach, we can approximate the anomaly scores for normal data using the unlabeled and anomaly data. Therefore, without the labeled normal data, we can train the anomaly detector to minimize the anomaly scores for normal data, and to maximize those for the anomaly data. In addition, our approach is applicable to various anomaly detectors such as the DeepSVDD. Experiments on various datasets show that our approach achieves better detection performance than existing approaches. | [
"['Hiroshi Takahashi' 'Tomoharu Iwata' 'Atsutoshi Kumagai' 'Yuuki Yamanaka']"
] |
null | null | 2405.18931 | null | null | http://arxiv.org/pdf/2405.18931v1 | 2024-05-29T09:36:20Z | 2024-05-29T09:36:20Z | EntProp: High Entropy Propagation for Improving Accuracy and Robustness | Deep neural networks (DNNs) struggle to generalize to out-of-distribution domains that are different from those in training despite their impressive performance. In practical applications, it is important for DNNs to have both high standard accuracy and robustness against out-of-distribution domains. One technique that achieves both of these improvements is disentangled learning with mixture distribution via auxiliary batch normalization layers (ABNs). This technique treats clean and transformed samples as different domains, allowing a DNN to learn better features from mixed domains. However, if we distinguish the domains of the samples based on entropy, we find that some transformed samples are drawn from the same domain as clean samples, and these samples are not completely different domains. To generate samples drawn from a completely different domain than clean samples, we hypothesize that transforming clean high-entropy samples to further increase the entropy generates out-of-distribution samples that are much further away from the in-distribution domain. On the basis of the hypothesis, we propose high entropy propagation~(EntProp), which feeds high-entropy samples to the network that uses ABNs. We introduce two techniques, data augmentation and free adversarial training, that increase entropy and bring the sample further away from the in-distribution domain. These techniques do not require additional training costs. Our experimental results show that EntProp achieves higher standard accuracy and robustness with a lower training cost than the baseline methods. In particular, EntProp is highly effective at training on small datasets. | [
"['Shohei Enomoto']"
] |
null | null | 2405.18932 | null | null | http://arxiv.org/pdf/2405.18932v1 | 2024-05-29T09:36:57Z | 2024-05-29T09:36:57Z | A Mallows-like Criterion for Anomaly Detection with Random Forest
Implementation | The effectiveness of anomaly signal detection can be significantly undermined by the inherent uncertainty of relying on one specified model. Under the framework of model average methods, this paper proposes a novel criterion to select the weights on aggregation of multiple models, wherein the focal loss function accounts for the classification of extremely imbalanced data. This strategy is further integrated into Random Forest algorithm by replacing the conventional voting method. We have evaluated the proposed method on benchmark datasets across various domains, including network intrusion. The findings indicate that our proposed method not only surpasses the model averaging with typical loss functions but also outstrips common anomaly detection algorithms in terms of accuracy and robustness. | [
"['Gaoxiang Zhao' 'Lu Wang' 'Xiaoqiang Wang']"
] |
null | null | 2405.18933 | null | null | http://arxiv.org/pdf/2405.18933v2 | 2024-05-31T05:03:48Z | 2024-05-29T09:37:23Z | LSPI: Heterogeneous Graph Neural Network Classification Aggregation
Algorithm Based on Size Neighbor Path Identification | Existing heterogeneous graph neural network algorithms (HGNNs) mostly rely on meta-paths to capture the rich semantic information contained in heterogeneous graphs (also known as heterogeneous information networks (HINs)), but most of these HGNNs focus on different ways of feature aggre gation and ignore the properties of the meta-paths themselves. This paper studies meta-paths in three commonly used data sets and finds that there are huge differences in the number of neighbors connected by different meta paths. At the same time, the noise information contained in large neigh bor paths will have an adverse impact on model performance. Therefore, this paper proposes a Heterogeneous Graph Neural Network Classification and Aggregation Algorithm Based on Large and Small Neighbor Path Iden tification(LSPI). LSPI firstly divides the meta-paths into large and small neighbor paths through the path discriminator , and in order to reduce the noise interference problem in large neighbor paths, LSPI selects neighbor nodes with higher similarity from both topology and feature perspectives, and passes small neighbor paths and filtered large neighbor paths through different graph convolution components. Aggregation is performed to obtain feature information under different subgraphs, and then LSPI uses subgraph level attention to fuse the feature information under different subgraphs to generate the final node embedding. Finally this paper verifies the superiority of the method through extensive experiments and also gives suggestions on the number of nodes to be retained in large neighbor paths through exper iments. The complete reproducible code adn data has been published at: https://github.com/liuhua811/LSPIA. | [
"['Yufei Zhao' 'Shiduo Wang' 'Hua Duan']"
] |
null | null | 2405.18938 | null | null | http://arxiv.org/pdf/2405.18938v3 | 2024-06-04T10:42:46Z | 2024-05-29T09:46:44Z | HLOB -- Information Persistence and Structure in Limit Order Books | We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it `HLOB'. This architecture (i) exploits the information encoded by an Information Filtering Network, namely the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial dependency structures among volume levels; and (ii) guarantees deterministic design choices to handle the complexity of the underlying system by drawing inspiration from the groundbreaking class of Homological Convolutional Neural Networks. We test our model against 9 state-of-the-art deep learning alternatives on 3 real-world Limit Order Book datasets, each including 15 stocks traded on the NASDAQ exchange, and we systematically characterize the scenarios where HLOB outperforms state-of-the-art architectures. Our approach sheds new light on the spatial distribution of information in Limit Order Books and on its degradation over increasing prediction horizons, narrowing the gap between microstructural modeling and deep learning-based forecasting in high-frequency financial markets. | [
"['Antonio Briola' 'Silvia Bartolucci' 'Tomaso Aste']"
] |
null | null | 2405.18941 | null | null | http://arxiv.org/pdf/2405.18941v1 | 2024-05-29T09:50:39Z | 2024-05-29T09:50:39Z | Content-Agnostic Moderation for Stance-Neutral Recommendation | Personalized recommendation systems often drive users towards more extreme content, exacerbating opinion polarization. While (content-aware) moderation has been proposed to mitigate these effects, such approaches risk curtailing the freedom of speech and of information. To address this concern, we propose and explore the feasibility of emph{content-agnostic} moderation as an alternative approach for reducing polarization. Content-agnostic moderation does not rely on the actual content being moderated, arguably making it less prone to forms of censorship. We establish theoretically that content-agnostic moderation cannot be guaranteed to work in a fully generic setting. However, we show that it can often be effectively achieved in practice with plausible assumptions. We introduce two novel content-agnostic moderation methods that modify the recommendations from the content recommender to disperse user-item co-clusters without relying on content features. To evaluate the potential of content-agnostic moderation in controlled experiments, we built a simulation environment to analyze the closed-loop behavior of a system with a given set of users, recommendation system, and moderation approach. Through comprehensive experiments in this environment, we show that our proposed moderation methods significantly enhance stance neutrality and maintain high recommendation quality across various data scenarios. Our results indicate that achieving stance neutrality without direct content information is not only feasible but can also help in developing more balanced and informative recommendation systems without substantially degrading user engagement. | [
"['Nan Li' 'Bo Kang' 'Tijl De Bie']"
] |
null | null | 2405.18942 | null | null | http://arxiv.org/pdf/2405.18942v2 | 2024-06-06T10:43:08Z | 2024-05-29T09:50:43Z | Verifiably Robust Conformal Prediction | Conformal Prediction (CP) is a popular uncertainty quantification method that provides distribution-free, statistically valid prediction sets, assuming that training and test data are exchangeable. In such a case, CP's prediction sets are guaranteed to cover the (unknown) true test output with a user-specified probability. Nevertheless, this guarantee is violated when the data is subjected to adversarial attacks, which often result in a significant loss of coverage. Recently, several approaches have been put forward to recover CP guarantees in this setting. These approaches leverage variations of randomised smoothing to produce conservative sets which account for the effect of the adversarial perturbations. They are, however, limited in that they only support $ell^2$-bounded perturbations and classification tasks. This paper introduces VRCP (Verifiably Robust Conformal Prediction), a new framework that leverages recent neural network verification methods to recover coverage guarantees under adversarial attacks. Our VRCP method is the first to support perturbations bounded by arbitrary norms including $ell^1$, $ell^2$, and $ell^infty$, as well as regression tasks. We evaluate and compare our approach on image classification tasks (CIFAR10, CIFAR100, and TinyImageNet) and regression tasks for deep reinforcement learning environments. In every case, VRCP achieves above nominal coverage and yields significantly more efficient and informative prediction regions than the SotA. | [
"['Linus Jeary' 'Tom Kuipers' 'Mehran Hosseini' 'Nicola Paoletti']"
] |
null | null | 2405.18944 | null | null | http://arxiv.org/pdf/2405.18944v1 | 2024-05-29T09:56:00Z | 2024-05-29T09:56:00Z | Predicting Many Properties of Crystals by a Single Deep Learning Model | The use of machine learning methods for predicting the properties of crystalline materials encounters significant challenges, primarily related to input encoding, output versatility, and interpretability. Here, we introduce CrystalBERT, an adaptable transformer-based framework with novel structure that integrates space group, elemental, and unit cell information. The method's adaptability lies not only in its ability to seamlessly combine diverse features but also in its capability to accurately predict a wide range of physically important properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT also provides insightful physical interpretations regarding the features that most significantly influence the target properties. Our findings indicate that space group and elemental information are more important for predicting topological and superconducting properties, in contrast to some properties that primarily depend on the unit cell information. This underscores the intricate nature of topological and superconducting properties. By incorporating all these features, we achieve a high accuracy of 91% in topological classification, surpassing prior studies and identifying previously misclassified topological materials, further demonstrating the effectiveness of our model. | [
"['Haosheng Xu' 'Dongheng Qian' 'Jing Wang']"
] |
null | null | 2405.18945 | null | null | http://arxiv.org/pdf/2405.18945v1 | 2024-05-29T09:56:54Z | 2024-05-29T09:56:54Z | WTTFNet: A Weather-Time-Trajectory Fusion Network for Pedestrian
Trajectory Prediction in Urban Complex | Pedestrian trajectory modelling in an urban complex is challenging because pedestrians can have many possible destinations, such as shops, escalators, and attractions. Moreover, weather and time-of-day may affect pedestrian behavior. In this paper, a new weather-time-trajectory fusion network (WTTFNet) is proposed to improve the performance of baseline deep neural network architecture. By incorporating weather and time-of-day information as an embedding structure, a novel WTTFNet based on gate multimodal unit is used to fuse the multimodal information and deep representation of trajectories. A joint loss function based on focal loss is used to co-optimize both the deep trajectory features and final classifier, which helps to improve the accuracy in predicting the intended destination of pedestrians and hence the trajectories under possible scenarios of class imbalances. Experimental results using the Osaka Asia and Pacific Trade Center (ATC) dataset shows improved performance of the proposed approach over state-of-the-art algorithms by 23.67% increase in classification accuracy, 9.16% and 7.07% reduction of average and final displacement error. The proposed approach may serve as an attractive approach for improving existing baseline trajectory prediction models when they are applied to scenarios with influences of weather-time conditions. It can be employed in numerous applications such as pedestrian facility engineering, public space development and technology-driven retail. | [
"['Ho Chun Wu' 'Esther Hoi Shan Lau' 'Paul Yuen' 'Kevin Hung'\n 'John Kwok Tai Chui' 'Andrew Kwok Fai Lui']"
] |
null | null | 2405.18948 | null | null | http://arxiv.org/pdf/2405.18948v1 | 2024-05-29T10:03:57Z | 2024-05-29T10:03:57Z | Learning to Recover from Plan Execution Errors during Robot
Manipulation: A Neuro-symbolic Approach | Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots. Most of the recent work on learning to plan from demonstrations lacks the ability to detect and recover from errors in the absence of an explicit state representation and/or a (sub-) goal check function. We propose an approach (blending learning with symbolic search) for automated error discovery and recovery, without needing annotated data of failures. Central to our approach is a neuro-symbolic state representation, in the form of dense scene graph, structured based on the objects present within the environment. This enables efficient learning of the transition function and a discriminator that not only identifies failures but also localizes them facilitating fast re-planning via computation of heuristic distance function. We also present an anytime version of our algorithm, where instead of recovering to the last correct state, we search for a sub-goal in the original plan minimizing the total distance to the goal given a re-planning budget. Experiments on a physics simulator with a variety of simulated failures show the effectiveness of our approach compared to existing baselines, both in terms of efficiency as well as accuracy of our recovery mechanism. | [
"['Namasivayam Kalithasan' 'Arnav Tuli' 'Vishal Bindal'\n 'Himanshu Gaurav Singh' 'Parag Singla' 'Rohan Paul']"
] |
null | null | 2405.18952 | null | null | http://arxiv.org/pdf/2405.18952v2 | 2024-06-01T02:18:06Z | 2024-05-29T10:08:31Z | Are You Sure? Rank Them Again: Repeated Ranking For Better Preference
Datasets | Training Large Language Models (LLMs) with Reinforcement Learning from AI Feedback (RLAIF) aligns model outputs more closely with human preferences. This involves an evaluator model ranking multiple candidate responses to user prompts. However, the rankings from popular evaluator models such as GPT-4 can be inconsistent. We propose the Repeat Ranking method - where we evaluate the same responses multiple times and train only on those responses which are consistently ranked. Using 2,714 prompts in 62 languages, we generated responses from 7 top multilingual LLMs and had GPT-4 rank them five times each. Evaluating on MT-Bench chat benchmarks in six languages, our method outperformed the standard practice of training on all available prompts. Our work highlights the quality versus quantity trade-off in RLAIF dataset generation and offers a stackable strategy for enhancing dataset and thus model quality. | [
"['Peter Devine']"
] |
null | null | 2405.18953 | null | null | http://arxiv.org/pdf/2405.18953v1 | 2024-05-29T10:11:10Z | 2024-05-29T10:11:10Z | MAGIC: Modular Auto-encoder for Generalisable Model Inversion with Bias
Corrections | Scientists often model physical processes to understand the natural world and uncover the causation behind observations. Due to unavoidable simplification, discrepancies often arise between model predictions and actual observations, in the form of systematic biases, whose impact varies with model completeness. Classical model inversion methods such as Bayesian inference or regressive neural networks tend either to overlook biases or make assumptions about their nature during data preprocessing, potentially leading to implausible results. Inspired by recent work in inverse graphics, we replace the decoder stage of a standard autoencoder with a physical model followed by a bias-correction layer. This generalisable approach simultaneously inverts the model and corrects its biases in an end-to-end manner without making strong assumptions about the nature of the biases. We demonstrate the effectiveness of our approach using two physical models from disparate domains: a complex radiative transfer model from remote sensing; and a volcanic deformation model from geodesy. Our method matches or surpasses results from classical approaches without requiring biases to be explicitly filtered out, suggesting an effective pathway for understanding the causation of various physical processes. | [
"['Yihang She' 'Clement Atzberger' 'Andrew Blake' 'Adriano Gualandi'\n 'Srinivasan Keshav']"
] |
null | null | 2405.18968 | null | null | http://arxiv.org/pdf/2405.18968v1 | 2024-05-29T10:26:16Z | 2024-05-29T10:26:16Z | UniIF: Unified Molecule Inverse Folding | Molecule inverse folding has been a long-standing challenge in chemistry and biology, with the potential to revolutionize drug discovery and material science. Despite specified models have been proposed for different small- or macro-molecules, few have attempted to unify the learning process, resulting in redundant efforts. Complementary to recent advancements in molecular structure prediction, such as RoseTTAFold All-Atom and AlphaFold3, we propose the unified model UniIF for the inverse folding of all molecules. We do such unification in two levels: 1) Data-Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization. 2) Model-Level: We introduce a geometric block attention network, comprising a geometric interaction, interactive attention and virtual long-term dependency modules, to capture the 3D interactions of all molecules. Through comprehensive evaluations across various tasks such as protein design, RNA design, and material design, we demonstrate that our proposed method surpasses state-of-the-art methods on all tasks. UniIF offers a versatile and effective solution for general molecule inverse folding. | [
"['Zhangyang Gao' 'Jue Wang' 'Cheng Tan' 'Lirong Wu' 'Yufei Huang'\n 'Siyuan Li' 'Zhirui Ye' 'Stan Z. Li']"
] |
null | null | 2405.18972 | null | null | http://arxiv.org/pdf/2405.18972v1 | 2024-05-29T10:34:44Z | 2024-05-29T10:34:44Z | Federated Learning with Bilateral Curation for Partially Class-Disjoint
Data | Partially class-disjoint data (PCDD), a common yet under-explored data formation where each client contributes a part of classes (instead of all classes) of samples, severely challenges the performance of federated algorithms. Without full classes, the local objective will contradict the global objective, yielding the angle collapse problem for locally missing classes and the space waste problem for locally existing classes. As far as we know, none of the existing methods can intrinsically mitigate PCDD challenges to achieve holistic improvement in the bilateral views (both global view and local view) of federated learning. To address this dilemma, we are inspired by the strong generalization of simplex Equiangular Tight Frame~(ETF) on the imbalanced data, and propose a novel approach called FedGELA where the classifier is globally fixed as a simplex ETF while locally adapted to the personal distributions. Globally, FedGELA provides fair and equal discrimination for all classes and avoids inaccurate updates of the classifier, while locally it utilizes the space of locally missing classes for locally existing classes. We conduct extensive experiments on a range of datasets to demonstrate that our FedGELA achieves promising performance~(averaged improvement of 3.9% to FedAvg and 1.5% to best baselines) and provide both local and global convergence guarantees. Source code is available at:https://github.com/MediaBrain-SJTU/FedGELA.git. | [
"['Ziqing Fan' 'Ruipeng Zhang' 'Jiangchao Yao' 'Bo Han' 'Ya Zhang'\n 'Yanfeng Wang']"
] |
null | null | 2405.18975 | null | null | http://arxiv.org/pdf/2405.18975v1 | 2024-05-29T10:38:25Z | 2024-05-29T10:38:25Z | Hierarchical Classification Auxiliary Network for Time Series
Forecasting | Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Square Error (MSE) loss often results in over-smooth predictions, making it challenging to handle the complexity and learn high-entropy features from time series data with high variability and unpredictability. In this work, we introduce a novel approach by tokenizing time series values to train forecasting models via cross-entropy loss, while considering the continuous nature of time series data. Specifically, we propose Hierarchical Classification Auxiliary Network, HCAN, a general model-agnostic component that can be integrated with any forecasting model. HCAN is based on a Hierarchy-Aware Attention module that integrates multi-granularity high-entropy features at different hierarchy levels. At each level, we assign a class label for timesteps to train an Uncertainty-Aware Classifier. This classifier mitigates the over-confidence in softmax loss via evidence theory. We also implement a Hierarchical Consistency Loss to maintain prediction consistency across hierarchy levels. Extensive experiments integrating HCAN with state-of-the-art forecasting models demonstrate substantial improvements over baselines on several real-world datasets. Code is available at:https://github.com/syrGitHub/HCAN. | [
"['Yanru Sun' 'Zongxia Xie' 'Dongyue Chen' 'Emadeldeen Eldele' 'Qinghua Hu']"
] |
null | null | 2405.18979 | null | null | http://arxiv.org/pdf/2405.18979v2 | 2024-06-24T09:12:08Z | 2024-05-29T10:45:06Z | MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under
Distribution Shifts | Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples without requiring access to the corresponding ground truth labels. Despite their ease of implementation and computational efficiency, current logit-based methods are vulnerable to overconfidence issues, leading to prediction bias, especially under the natural shift. In this work, we first study the relationship between logits and generalization performance from the view of low-density separation assumption. Our findings motivate our proposed method MaNo which (1) applies a data-dependent normalization on the logits to reduce prediction bias, and (2) takes the $L_p$ norm of the matrix of normalized logits as the estimation score. Our theoretical analysis highlights the connection between the provided score and the model's uncertainty. We conduct an extensive empirical study on common unsupervised accuracy estimation benchmarks and demonstrate that MaNo achieves state-of-the-art performance across various architectures in the presence of synthetic, natural, or subpopulation shifts. | [
"['Renchunzi Xie' 'Ambroise Odonnat' 'Vasilii Feofanov' 'Weijian Deng'\n 'Jianfeng Zhang' 'Bo An']"
] |
null | null | 2405.18983 | null | null | http://arxiv.org/pdf/2405.18983v2 | 2024-06-03T11:16:55Z | 2024-05-29T10:56:13Z | Federated Learning under Partially Class-Disjoint Data via Manifold
Reshaping | Statistical heterogeneity severely limits the performance of federated learning (FL), motivating several explorations e.g., FedProx, MOON and FedDyn, to alleviate this problem. Despite effectiveness, their considered scenario generally requires samples from almost all classes during the local training of each client, although some covariate shifts may exist among clients. In fact, the natural case of partially class-disjoint data (PCDD), where each client contributes a few classes (instead of all classes) of samples, is practical yet underexplored. Specifically, the unique collapse and invasion characteristics of PCDD can induce the biased optimization direction in local training, which prevents the efficiency of federated learning. To address this dilemma, we propose a manifold reshaping approach called FedMR to calibrate the feature space of local training. Our FedMR adds two interplaying losses to the vanilla federated learning: one is intra-class loss to decorrelate feature dimensions for anti-collapse; and the other one is inter-class loss to guarantee the proper margin among categories in the feature expansion. We conduct extensive experiments on a range of datasets to demonstrate that our FedMR achieves much higher accuracy and better communication efficiency. Source code is available at: https://github.com/MediaBrain-SJTU/FedMR.git. | [
"['Ziqing Fan' 'Jiangchao Yao' 'Ruipeng Zhang' 'Lingjuan Lyu' 'Ya Zhang'\n 'Yanfeng Wang']"
] |
null | null | 2405.18984 | null | null | http://arxiv.org/pdf/2405.18984v1 | 2024-05-29T10:57:25Z | 2024-05-29T10:57:25Z | Optimizing Vehicular Networks with Variational Quantum Circuits-based
Reinforcement Learning | In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL) framework to characterize efficient network selection and autonomous driving policies in a vehicular network (VNet). Numerical results showcase notable enhancements in both convergence rates and rewards when compared to conventional deep-Q networks (DQNs), validating the efficacy of the VQC-MORL solution. | [
"['Zijiang Yan' 'Ramsundar Tanikella' 'Hina Tabassum']"
] |
null | null | 2405.18986 | null | null | http://arxiv.org/pdf/2405.18986v1 | 2024-05-29T11:03:42Z | 2024-05-29T11:03:42Z | Robust Optimization in Protein Fitness Landscapes Using Reinforcement
Learning in Latent Space | Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains challenging, especially when starting from low-fitness sequences. We propose LatProtRL, an optimization method to efficiently traverse a latent space learned by an encoder-decoder leveraging a large protein language model. To escape local optima, our optimization is modeled as a Markov decision process using reinforcement learning acting directly in latent space. We evaluate our approach on two important fitness optimization tasks, demonstrating its ability to achieve comparable or superior fitness over baseline methods. Our findings and in vitro evaluation show that the generated sequences can reach high-fitness regions, suggesting a substantial potential of LatProtRL in lab-in-the-loop scenarios. | [
"['Minji Lee' 'Luiz Felipe Vecchietti' 'Hyunkyu Jung' 'Hyun Joo Ro'\n 'Meeyoung Cha' 'Ho Min Kim']"
] |
null | null | 2405.18997 | null | null | http://arxiv.org/pdf/2405.18997v1 | 2024-05-29T11:21:25Z | 2024-05-29T11:21:25Z | Kernel Semi-Implicit Variational Inference | Semi-implicit variational inference (SIVI) extends traditional variational families with semi-implicit distributions defined in a hierarchical manner. Due to the intractable densities of semi-implicit distributions, classical SIVI often resorts to surrogates of evidence lower bound (ELBO) that would introduce biases for training. A recent advancement in SIVI, named SIVI-SM, utilizes an alternative score matching objective made tractable via a minimax formulation, albeit requiring an additional lower-level optimization. In this paper, we propose kernel SIVI (KSIVI), a variant of SIVI-SM that eliminates the need for lower-level optimization through kernel tricks. Specifically, we show that when optimizing over a reproducing kernel Hilbert space (RKHS), the lower-level problem has an explicit solution. This way, the upper-level objective becomes the kernel Stein discrepancy (KSD), which is readily computable for stochastic gradient descent due to the hierarchical structure of semi-implicit variational distributions. An upper bound for the variance of the Monte Carlo gradient estimators of the KSD objective is derived, which allows us to establish novel convergence guarantees of KSIVI. We demonstrate the effectiveness and efficiency of KSIVI on both synthetic distributions and a variety of real data Bayesian inference tasks. | [
"['Ziheng Cheng' 'Longlin Yu' 'Tianyu Xie' 'Shiyue Zhang' 'Cheng Zhang']"
] |
null | null | 2405.19000 | null | null | http://arxiv.org/pdf/2405.19000v1 | 2024-05-29T11:28:06Z | 2024-05-29T11:28:06Z | FedMAP: Unlocking Potential in Personalized Federated Learning through
Bi-Level MAP Optimization | Federated Learning (FL) enables collaborative training of machine learning models on decentralized data while preserving data privacy. However, data across clients often differs significantly due to class imbalance, feature distribution skew, sample size imbalance, and other phenomena. Leveraging information from these not identically distributed (non-IID) datasets poses substantial challenges. FL methods based on a single global model cannot effectively capture the variations in client data and underperform in non-IID settings. Consequently, Personalized FL (PFL) approaches that adapt to each client's data distribution but leverage other clients' data are essential but currently underexplored. We propose a novel Bayesian PFL framework using bi-level optimization to tackle the data heterogeneity challenges. Our proposed framework utilizes the global model as a prior distribution within a Maximum A Posteriori (MAP) estimation of personalized client models. This approach facilitates PFL by integrating shared knowledge from the prior, thereby enhancing local model performance, generalization ability, and communication efficiency. We extensively evaluated our bi-level optimization approach on real-world and synthetic datasets, demonstrating significant improvements in model accuracy compared to existing methods while reducing communication overhead. This study contributes to PFL by establishing a solid theoretical foundation for the proposed method and offering a robust, ready-to-use framework that effectively addresses the challenges posed by non-IID data in FL. | [
"['Fan Zhang' 'Carlos Esteve-Yagüe' 'Sören Dittmer'\n 'Carola-Bibiane Schönlieb' 'Michael Roberts']"
] |
null | null | 2405.19013 | null | null | http://arxiv.org/pdf/2405.19013v1 | 2024-05-29T11:52:53Z | 2024-05-29T11:52:53Z | On Dissipativity of Cross-Entropy Loss in Training ResNets | The training of ResNets and neural ODEs can be formulated and analyzed from the perspective of optimal control. This paper proposes a dissipative formulation of the training of ResNets and neural ODEs for classification problems by including a variant of the cross-entropy as a regularization in the stage cost. Based on the dissipative formulation of the training, we prove that the trained ResNet exhibit the turnpike phenomenon. We then illustrate that the training exhibits the turnpike phenomenon by training on the two spirals and MNIST datasets. This can be used to find very shallow networks suitable for a given classification task. | [
"['Jens Püttschneider' 'Timm Faulwasser']"
] |
null | null | 2405.19014 | null | null | http://arxiv.org/pdf/2405.19014v3 | 2024-06-21T11:12:23Z | 2024-05-29T11:53:07Z | Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with
Uncertainty-Aware Rollout Adaption | Dyna-style model-based reinforcement learning (MBRL) combines model-free agents with predictive transition models through model-based rollouts. This combination raises a critical question: 'When to trust your model?'; i.e., which rollout length results in the model providing useful data? Janner et al. (2019) address this question by gradually increasing rollout lengths throughout the training. While theoretically tempting, uniform model accuracy is a fallacy that collapses at the latest when extrapolating. Instead, we propose asking the question 'Where to trust your model?'. Using inherent model uncertainty to consider local accuracy, we obtain the Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption (MACURA) algorithm. We propose an easy-to-tune rollout mechanism and demonstrate substantial improvements in data efficiency and performance compared to state-of-the-art deep MBRL methods on the MuJoCo benchmark. | [
"['Bernd Frauenknecht' 'Artur Eisele' 'Devdutt Subhasish'\n 'Friedrich Solowjow' 'Sebastian Trimpe']"
] |
null | null | 2405.19015 | null | null | http://arxiv.org/pdf/2405.19015v1 | 2024-05-29T11:54:11Z | 2024-05-29T11:54:11Z | Distributed Management of Fluctuating Energy Resources in Dynamic
Networked Systems | Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods. | [
"['Xiaotong Cheng' 'Ioannis Tsetis' 'Setareh Maghsudi']"
] |
null | null | 2405.19017 | null | null | http://arxiv.org/pdf/2405.19017v1 | 2024-05-29T11:59:56Z | 2024-05-29T11:59:56Z | Efficient Exploration in Average-Reward Constrained Reinforcement
Learning: Achieving Near-Optimal Regret With Posterior Sampling | We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost component of $tilde{O} (DSsqrt{AT})$ for any communicating CMDP with $S$ states, $A$ actions, and diameter $D$. This regret bound matches the lower bound in order of time horizon $T$ and is the best-known regret bound for communicating CMDPs achieved by a computationally tractable algorithm. Empirical results show that our posterior sampling algorithm outperforms the existing algorithms for constrained reinforcement learning. | [
"['Danil Provodin' 'Maurits Kaptein' 'Mykola Pechenizkiy']"
] |
null | null | 2405.19019 | null | null | http://arxiv.org/pdf/2405.19019v1 | 2024-05-29T12:01:49Z | 2024-05-29T12:01:49Z | Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs
with applications in heterogeneous media | We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists of a probabilistic, learning objective in which weighted residuals are used to probe the PDE and provide a source of {em virtual} data i.e. the actual PDE never needs to be solved. This is combined with a physics-aware implicit solver that consists of a much coarser, discretized version of the original PDE, which provides the requisite information bottleneck for high-dimensional problems and enables generalization in out-of-distribution settings (e.g. different boundary conditions). We demonstrate its capability in the context of random heterogeneous materials where the input parameters represent the material microstructure. We extend the framework to multiscale problems and show that a surrogate can be learned for the effective (homogenized) solution without ever solving the reference problem. We further demonstrate how the proposed framework can accommodate and generalize several existing learning objectives and architectures while yielding probabilistic surrogates that can quantify predictive uncertainty. | [
"['Matthaios Chatzopoulos' 'Phaedon-Stelios Koutsourelakis']"
] |
null | null | 2405.19022 | null | null | http://arxiv.org/pdf/2405.19022v1 | 2024-05-29T12:03:45Z | 2024-05-29T12:03:45Z | Towards Standardizing AI Bias Exploration | Creating fair AI systems is a complex problem that involves the assessment of context-dependent bias concerns. Existing research and programming libraries express specific concerns as measures of bias that they aim to constrain or mitigate. In practice, one should explore a wide variety of (sometimes incompatible) measures before deciding which ones warrant corrective action, but their narrow scope means that most new situations can only be examined after devising new measures. In this work, we present a mathematical framework that distils literature measures of bias into building blocks, hereby facilitating new combinations to cover a wide range of fairness concerns, such as classification or recommendation differences across multiple multi-value sensitive attributes (e.g., many genders and races, and their intersections). We show how this framework generalizes existing concepts and present frequently used blocks. We provide an open-source implementation of our framework as a Python library, called FairBench, that facilitates systematic and extensible exploration of potential bias concerns. | [
"['Emmanouil Krasanakis' 'Symeon Papadopoulos']"
] |
null | null | 2405.19024 | null | null | http://arxiv.org/pdf/2405.19024v1 | 2024-05-29T12:07:17Z | 2024-05-29T12:07:17Z | Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory | We consider inverse reinforcement learning problems with concave utilities. Concave Utility Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which employs a concave function of the state occupancy measure, rather than a linear function. CURL has garnered recent attention for its ability to represent instances of many important applications including the standard RL such as imitation learning, pure exploration, constrained MDPs, offline RL, human-regularized RL, and others. Inverse reinforcement learning is a powerful paradigm that focuses on recovering an unknown reward function that can rationalize the observed behaviour of an agent. There has been recent theoretical advances in inverse RL where the problem is formulated as identifying the set of feasible reward functions. However, inverse RL for CURL problems has not been considered previously. In this paper we show that most of the standard IRL results do not apply to CURL in general, since CURL invalidates the classical Bellman equations. This calls for a new theoretical framework for the inverse CURL problem. Using a recent equivalence result between CURL and Mean-field Games, we propose a new definition for the feasible rewards for I-CURL by proving that this problem is equivalent to an inverse game theory problem in a subclass of mean-field games. We present initial query and sample complexity results for the I-CURL problem under assumptions such as Lipschitz-continuity. Finally, we outline future directions and applications in human--AI collaboration enabled by our results. | [
"['Mustafa Mert Çelikok' 'Frans A. Oliehoek' 'Jan-Willem van de Meent']"
] |
null | null | 2405.19026 | null | null | http://arxiv.org/pdf/2405.19026v1 | 2024-05-29T12:12:09Z | 2024-05-29T12:12:09Z | DiveR-CT: Diversity-enhanced Red Teaming with Relaxing Constraints | Recent advances in large language models (LLMs) have made them indispensable, raising significant concerns over managing their safety. Automated red teaming offers a promising alternative to the labor-intensive and error-prone manual probing for vulnerabilities, providing more consistent and scalable safety evaluations. However, existing approaches often compromise diversity by focusing on maximizing attack success rate. Additionally, methods that decrease the cosine similarity from historical embeddings with semantic diversity rewards lead to novelty stagnation as history grows. To address these issues, we introduce DiveR-CT, which relaxes conventional constraints on the objective and semantic reward, granting greater freedom for the policy to enhance diversity. Our experiments demonstrate DiveR-CT's marked superiority over baselines by 1) generating data that perform better in various diversity metrics across different attack success rate levels, 2) better-enhancing resiliency in blue team models through safety tuning based on collected data, 3) allowing dynamic control of objective weights for reliable and controllable attack success rates, and 4) reducing susceptibility to reward overoptimization. Project details and code can be found at https://andrewzh112.github.io/#diverct. | [
"['Andrew Zhao' 'Quentin Xu' 'Matthieu Lin' 'Shenzhi Wang' 'Yong-jin Liu'\n 'Zilong Zheng' 'Gao Huang']"
] |
null | null | 2405.19032 | null | null | http://arxiv.org/pdf/2405.19032v1 | 2024-05-29T12:18:51Z | 2024-05-29T12:18:51Z | Large Language Models for Code Summarization | Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to perform well on these problems. In this technical report, we aim to review how these models perform in code explanation/summarization, while also investigating their code generation capabilities (based on natural language descriptions). | [
"['Balázs Szalontai' 'Gergő Szalay' 'Tamás Márton' 'Anna Sike'\n 'Balázs Pintér' 'Tibor Gregorics']"
] |
null | null | 2405.19033 | null | null | http://arxiv.org/pdf/2405.19033v1 | 2024-05-29T12:22:59Z | 2024-05-29T12:22:59Z | CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation
in Ultra-Lightweight and One-Shot Graph Classification on Edge | Graph Neural Networks (GNNs) are computationally demanding and inefficient when applied to graph classification tasks in resource-constrained edge scenarios due to their inherent process, involving multiple rounds of forward and backward propagation. As a lightweight alternative, Hyper-Dimensional Computing (HDC), which leverages high-dimensional vectors for data encoding and processing, offers a more efficient solution by addressing computational bottleneck. However, current HDC methods primarily focus on static graphs and neglect to effectively capture node attributes and structural information, which leads to poor accuracy. In this work, we propose CiliaGraph, an enhanced expressive yet ultra-lightweight HDC model for graph classification. This model introduces a novel node encoding strategy that preserves relative distance isomorphism for accurate node connection representation. In addition, node distances are utilized as edge weights for information aggregation, and the encoded node attributes and structural information are concatenated to obtain a comprehensive graph representation. Furthermore, we explore the relationship between orthogonality and dimensionality to reduce the dimensions, thereby further enhancing computational efficiency. Compared to the SOTA GNNs, extensive experiments show that CiliaGraph reduces memory usage and accelerates training speed by an average of 292 times(up to 2341 times) and 103 times(up to 313 times) respectively while maintaining comparable accuracy. | [
"['Yuxi Han' 'Jihe Wang' 'Danghui Wang']"
] |
null | null | 2405.19036 | null | null | http://arxiv.org/pdf/2405.19036v1 | 2024-05-29T12:23:48Z | 2024-05-29T12:23:48Z | State Space Models are Comparable to Transformers in Estimating
Functions with Dynamic Smoothness | Deep neural networks based on state space models (SSMs) are attracting much attention in sequence modeling since their computational cost is significantly smaller than that of Transformers. While the capabilities of SSMs have been primarily investigated through experimental comparisons, theoretical understanding of SSMs is still limited. In particular, there is a lack of statistical and quantitative evaluation of whether SSM can replace Transformers. In this paper, we theoretically explore in which tasks SSMs can be alternatives of Transformers from the perspective of estimating sequence-to-sequence functions. We consider the setting where the target function has direction-dependent smoothness and prove that SSMs can estimate such functions with the same convergence rate as Transformers. Additionally, we prove that SSMs can estimate the target function, even if the smoothness changes depending on the input sequence, as well as Transformers. Our results show the possibility that SSMs can replace Transformers when estimating the functions in certain classes that appear in practice. | [
"['Naoki Nishikawa' 'Taiji Suzuki']"
] |
null | null | 2405.19047 | null | null | http://arxiv.org/pdf/2405.19047v1 | 2024-05-29T12:44:41Z | 2024-05-29T12:44:41Z | Statistical Context Detection for Deep Lifelong Reinforcement Learning | Context detection involves labeling segments of an online stream of data as belonging to different tasks. Task labels are used in lifelong learning algorithms to perform consolidation or other procedures that prevent catastrophic forgetting. Inferring task labels from online experiences remains a challenging problem. Most approaches assume finite and low-dimension observation spaces or a preliminary training phase during which task labels are learned. Moreover, changes in the transition or reward functions can be detected only in combination with a policy, and therefore are more difficult to detect than changes in the input distribution. This paper presents an approach to learning both policies and labels in an online deep reinforcement learning setting. The key idea is to use distance metrics, obtained via optimal transport methods, i.e., Wasserstein distance, on suitable latent action-reward spaces to measure distances between sets of data points from past and current streams. Such distances can then be used for statistical tests based on an adapted Kolmogorov-Smirnov calculation to assign labels to sequences of experiences. A rollback procedure is introduced to learn multiple policies by ensuring that only the appropriate data is used to train the corresponding policy. The combination of task detection and policy deployment allows for the optimization of lifelong reinforcement learning agents without an oracle that provides task labels. The approach is tested using two benchmarks and the results show promising performance when compared with related context detection algorithms. The results suggest that optimal transport statistical methods provide an explainable and justifiable procedure for online context detection and reward optimization in lifelong reinforcement learning. | [
"['Jeffery Dick' 'Saptarshi Nath' 'Christos Peridis' 'Eseoghene Benjamin'\n 'Soheil Kolouri' 'Andrea Soltoggio']"
] |
null | null | 2405.19053 | null | null | http://arxiv.org/abs/2405.19053v1 | 2024-05-29T12:54:22Z | 2024-05-29T12:54:22Z | Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of
Electric Vehicle Charging Stations | The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. To address these challenges, we propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM incorporates a multiscale graph neural network to discern hierarchical nonlinear temporal dependencies across various time scales. Besides, it also integrates a recurrent learning component and a residual fusion mechanism, enhancing its capability to accurately capture spatial and temporal variations in charging patterns. The effectiveness of the proposed MSTEM has been validated through comparative analysis with six baseline models using three evaluation metrics. The case studies utilize real-world datasets for both fast and slow charging loads at EVCS in Perth, UK. The experimental results demonstrate the superiority of MSTEM in short-term continuous load forecasting for EVCS. | [
"['Zongbao Zhang' 'Jiao Hao' 'Wenmeng Zhao' 'Yan Liu' 'Yaohui Huang'\n 'Xinhang Luo']"
] |
null | null | 2405.19059 | null | null | http://arxiv.org/pdf/2405.19059v2 | 2024-05-31T07:45:53Z | 2024-05-29T13:00:10Z | Robust Entropy Search for Safe Efficient Bayesian Optimization | The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of adversarial robustness, where all parameters are controllable during the optimization process, but a subset of them is uncontrollable or even adversely perturbed at the time of application. To this end, we develop an efficient information-based acquisition function that we call Robust Entropy Search (RES). We empirically demonstrate its benefits in experiments on synthetic and real-life data. The results showthat RES reliably finds robust optima, outperforming state-of-the-art algorithms. | [
"['Dorina Weichert' 'Alexander Kister' 'Sebastian Houben' 'Patrick Link'\n 'Gunar Ernis']"
] |
null | null | 2405.19062 | null | null | http://arxiv.org/pdf/2405.19062v1 | 2024-05-29T13:09:33Z | 2024-05-29T13:09:33Z | SIG: Efficient Self-Interpretable Graph Neural Network for
Continuous-time Dynamic Graphs | While dynamic graph neural networks have shown promise in various applications, explaining their predictions on continuous-time dynamic graphs (CTDGs) is difficult. This paper investigates a new research task: self-interpretable GNNs for CTDGs. We aim to predict future links within the dynamic graph while simultaneously providing causal explanations for these predictions. There are two key challenges: (1) capturing the underlying structural and temporal information that remains consistent across both independent and identically distributed (IID) and out-of-distribution (OOD) data, and (2) efficiently generating high-quality link prediction results and explanations. To tackle these challenges, we propose a novel causal inference model, namely the Independent and Confounded Causal Model (ICCM). ICCM is then integrated into a deep learning architecture that considers both effectiveness and efficiency. Extensive experiments demonstrate that our proposed model significantly outperforms existing methods across link prediction accuracy, explanation quality, and robustness to shortcut features. Our code and datasets are anonymously released at https://github.com/2024SIG/SIG. | [
"['Lanting Fang' 'Yulian Yang' 'Kai Wang' 'Shanshan Feng' 'Kaiyu Feng'\n 'Jie Gui' 'Shuliang Wang' 'Yew-Soon Ong']"
] |
null | null | 2405.19065 | null | null | http://arxiv.org/pdf/2405.19065v1 | 2024-05-29T13:16:46Z | 2024-05-29T13:16:46Z | xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based
Edge Systems | Ternary neural networks (TNNs) offer a superior accuracy-energy trade-off compared to binary neural networks. However, until now, they have required specialized accelerators to realize their efficiency potential, which has hindered widespread adoption. To address this, we present xTern, a lightweight extension of the RISC-V instruction set architecture (ISA) targeted at accelerating TNN inference on general-purpose cores. To complement the ISA extension, we developed a set of optimized kernels leveraging xTern, achieving 67% higher throughput than their 2-bit equivalents. Power consumption is only marginally increased by 5.2%, resulting in an energy efficiency improvement by 57.1%. We demonstrate that the proposed xTern extension, integrated into an octa-core compute cluster, incurs a minimal silicon area overhead of 0.9% with no impact on timing. In end-to-end benchmarks, we demonstrate that xTern enables the deployment of TNNs achieving up to 1.6 percentage points higher CIFAR-10 classification accuracy than 2-bit networks at equal inference latency. Our results show that xTern enables RISC-V-based ultra-low-power edge AI platforms to benefit from the efficiency potential of TNNs. | [
"['Georg Rutishauser' 'Joan Mihali' 'Moritz Scherer' 'Luca Benini']"
] |
null | null | 2405.19072 | null | null | http://arxiv.org/pdf/2405.19072v1 | 2024-05-29T13:25:49Z | 2024-05-29T13:25:49Z | Relevance-aware Algorithmic Recourse | As machine learning continues to gain prominence, transparency and explainability are increasingly critical. Without an understanding of these models, they can replicate and worsen human bias, adversely affecting marginalized communities. Algorithmic recourse emerges as a tool for clarifying decisions made by predictive models, providing actionable insights to alter outcomes. They answer, 'What do I have to change?' to achieve the desired result. Despite their importance, current algorithmic recourse methods treat all domain values equally, which is unrealistic in real-world settings. In this paper, we propose a novel framework, Relevance-Aware Algorithmic Recourse (RAAR), that leverages the concept of relevance in applying algorithmic recourse to regression tasks. We conducted multiple experiments on 15 datasets to outline how relevance influences recourses. Results show that relevance contributes algorithmic recourses comparable to well-known baselines, with greater efficiency and lower relative costs. | [
"['Dongwhi Kim' 'Nuno Moniz']"
] |
null | null | 2405.19076 | null | null | http://arxiv.org/pdf/2405.19076v3 | 2024-07-15T12:36:42Z | 2024-05-29T13:34:32Z | Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials
Analysis and Design | We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data from thousands of scientific papers and science-focused Wikipedia data demonstrates can interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The combination of a vision encoder with an autoregressive transformer supports multimodal natural language understanding, which can be coupled with other generative methods to create an image-to-text-to-3D pipeline. To develop more capable models from smaller ones, we report both mixture-of-expert methods and model merging. We examine the models in diverse use cases that incorporate biological materials, fracture and engineering analysis, protein biophysics, and bio-inspired design based on insect behavior. Generative applications include bio-inspired designs, including pollen-inspired architected materials, as well as the synthesis of bio-inspired material microstructures from a photograph of a solar eclipse. Additional model fine-tuning with a series of molecular dynamics results demonstrate Cephalo's enhanced capabilities to accurately predict statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials. | [
"['Markus J. Buehler']"
] |
null | null | 2405.19080 | null | null | http://arxiv.org/pdf/2405.19080v1 | 2024-05-29T13:36:36Z | 2024-05-29T13:36:36Z | OMPO: A Unified Framework for RL under Policy and Dynamics Shifts | Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge. Existing works often overlook the distribution discrepancies induced by policy or dynamics shifts, or rely on specialized algorithms with task priors, thus often resulting in suboptimal policy performances and high learning variances. In this paper, we identify a unified strategy for online RL policy learning under diverse settings of policy and dynamics shifts: transition occupancy matching. In light of this, we introduce a surrogate policy learning objective by considering the transition occupancy discrepancies and then cast it into a tractable min-max optimization problem through dual reformulation. Our method, dubbed Occupancy-Matching Policy Optimization (OMPO), features a specialized actor-critic structure equipped with a distribution discriminator and a small-size local buffer. We conduct extensive experiments based on the OpenAI Gym, Meta-World, and Panda Robots environments, encompassing policy shifts under stationary and nonstationary dynamics, as well as domain adaption. The results demonstrate that OMPO outperforms the specialized baselines from different categories in all settings. We also find that OMPO exhibits particularly strong performance when combined with domain randomization, highlighting its potential in RL-based robotics applications | [
"['Yu Luo' 'Tianying Ji' 'Fuchun Sun' 'Jianwei Zhang' 'Huazhe Xu'\n 'Xianyuan Zhan']"
] |
null | null | 2405.19098 | null | null | http://arxiv.org/pdf/2405.19098v1 | 2024-05-29T14:05:16Z | 2024-05-29T14:05:16Z | Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided
by a Function Prior | This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods improve the query efficiency by incorporating the gradient of a surrogate white-box model into query-based attacks due to the adversarial transferability. However, the localized gradient is not informative enough, making these methods still query-intensive. In this paper, we propose a Prior-guided Bayesian Optimization (P-BO) algorithm that leverages the surrogate model as a global function prior in black-box adversarial attacks. As the surrogate model contains rich prior information of the black-box one, P-BO models the attack objective with a Gaussian process whose mean function is initialized as the surrogate model's loss. Our theoretical analysis on the regret bound indicates that the performance of P-BO may be affected by a bad prior. Therefore, we further propose an adaptive integration strategy to automatically adjust a coefficient on the function prior by minimizing the regret bound. Extensive experiments on image classifiers and large vision-language models demonstrate the superiority of the proposed algorithm in reducing queries and improving attack success rates compared with the state-of-the-art black-box attacks. Code is available at https://github.com/yibo-miao/PBO-Attack. | [
"['Shuyu Cheng' 'Yibo Miao' 'Yinpeng Dong' 'Xiao Yang' 'Xiao-Shan Gao'\n 'Jun Zhu']"
] |
null | null | 2405.19101 | null | null | http://arxiv.org/pdf/2405.19101v1 | 2024-05-29T14:06:51Z | 2024-05-29T14:06:51Z | Poseidon: Efficient Foundation Models for PDEs | We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enable continuous-in-time evaluations. A novel training strategy leveraging the semi-group property of time-dependent PDEs to allow for significant scaling-up of the training data is also proposed. Poseidon is pretrained on a diverse, large scale dataset for the governing equations of fluid dynamics. It is then evaluated on a suite of 15 challenging downstream tasks that include a wide variety of PDE types and operators. We show that Poseidon exhibits excellent performance across the board by outperforming baselines significantly, both in terms of sample efficiency and accuracy. Poseidon also generalizes very well to new physics that is not seen during pretraining. Moreover, Poseidon scales with respect to model and data size, both for pretraining and for downstream tasks. Taken together, our results showcase the surprising ability of Poseidon to learn effective representations from a very small set of PDEs during pretraining in order to generalize well to unseen and unrelated PDEs downstream, demonstrating its potential as an effective, general purpose PDE foundation model. Finally, the Poseidon model as well as underlying pretraining and downstream datasets are open sourced, with code being available at https://github.com/camlab-ethz/poseidon and pretrained models and datasets at https://huggingface.co/camlab-ethz. | [
"['Maximilian Herde' 'Bogdan Raonić' 'Tobias Rohner' 'Roger Käppeli'\n 'Roberto Molinaro' 'Emmanuel de Bézenac' 'Siddhartha Mishra']"
] |
null | null | 2405.19103 | null | null | http://arxiv.org/pdf/2405.19103v1 | 2024-05-29T14:07:44Z | 2024-05-29T14:07:44Z | Voice Jailbreak Attacks Against GPT-4o | Recently, the concept of artificial assistants has evolved from science fiction into real-world applications. GPT-4o, the newest multimodal large language model (MLLM) across audio, vision, and text, has further blurred the line between fiction and reality by enabling more natural human-computer interactions. However, the advent of GPT-4o's voice mode may also introduce a new attack surface. In this paper, we present the first systematic measurement of jailbreak attacks against the voice mode of GPT-4o. We show that GPT-4o demonstrates good resistance to forbidden questions and text jailbreak prompts when directly transferring them to voice mode. This resistance is primarily due to GPT-4o's internal safeguards and the difficulty of adapting text jailbreak prompts to voice mode. Inspired by GPT-4o's human-like behaviors, we propose VoiceJailbreak, a novel voice jailbreak attack that humanizes GPT-4o and attempts to persuade it through fictional storytelling (setting, character, and plot). VoiceJailbreak is capable of generating simple, audible, yet effective jailbreak prompts, which significantly increases the average attack success rate (ASR) from 0.033 to 0.778 in six forbidden scenarios. We also conduct extensive experiments to explore the impacts of interaction steps, key elements of fictional writing, and different languages on VoiceJailbreak's effectiveness and further enhance the attack performance with advanced fictional writing techniques. We hope our study can assist the research community in building more secure and well-regulated MLLMs. | [
"['Xinyue Shen' 'Yixin Wu' 'Michael Backes' 'Yang Zhang']"
] |
null | null | 2405.19107 | null | null | http://arxiv.org/pdf/2405.19107v1 | 2024-05-29T14:11:29Z | 2024-05-29T14:11:29Z | Offline Regularised Reinforcement Learning for Large Language Models
Alignment | The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is a quadruplet composed of a prompt, two independent responses (completions of the prompt) and a human preference between the two independent responses, yielding a preferred and a dis-preferred response. Such data is typically scarce and expensive to collect. On the other hand, emph{single-trajectory} datasets where each element is a triplet composed of a prompt, a response and a human feedback is naturally more abundant. The canonical element of such datasets is for instance an LLM's response to a user's prompt followed by a user's feedback such as a thumbs-up/down. Consequently, in this work, we propose DRO, or emph{Direct Reward Optimisation}, as a framework and associated algorithms that do not require pairwise preferences. DRO uses a simple mean-squared objective that can be implemented in various ways. We validate our findings empirically, using T5 encoder-decoder language models, and show DRO's performance over selected baselines such as Kahneman-Tversky Optimization (KTO). Thus, we confirm that DRO is a simple and empirically compelling method for single-trajectory policy optimisation. | [
"['Pierre Harvey Richemond' 'Yunhao Tang' 'Daniel Guo'\n 'Daniele Calandriello' 'Mohammad Gheshlaghi Azar' 'Rafael Rafailov'\n 'Bernardo Avila Pires' 'Eugene Tarassov' 'Lucas Spangher'\n 'Will Ellsworth' 'Aliaksei Severyn' 'Jonathan Mallinson' 'Lior Shani'\n 'Gil Shamir' 'Rishabh Joshi' 'Tianqi Liu' 'Remi Munos' 'Bilal Piot']"
] |
null | null | 2405.19119 | null | null | http://arxiv.org/pdf/2405.19119v1 | 2024-05-29T14:26:24Z | 2024-05-29T14:26:24Z | Can Graph Learning Improve Task Planning? | Task planning is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests into solvable sub-tasks, thereby fulfilling the original requests. In this context, the sub-tasks can be naturally viewed as a graph, where the nodes represent the sub-tasks, and the edges denote the dependencies among them. Consequently, task planning is a decision-making problem that involves selecting a connected path or subgraph within the corresponding graph and invoking it. In this paper, we explore graph learning-based methods for task planning, a direction that is orthogonal to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs, which is adeptly addressed by graph neural networks (GNNs). This theoretical insight led us to integrate GNNs with LLMs to enhance overall performance. Extensive experiments demonstrate that GNN-based methods surpass existing solutions even without training, and minimal training can further enhance their performance. Additionally, our approach complements prompt engineering and fine-tuning techniques, with performance further enhanced by improved prompts or a fine-tuned model. | [
"['Xixi Wu' 'Yifei Shen' 'Caihua Shan' 'Kaitao Song' 'Siwei Wang'\n 'Bohang Zhang' 'Jiarui Feng' 'Hong Cheng' 'Wei Chen' 'Yun Xiong'\n 'Dongsheng Li']"
] |
null | null | 2405.19121 | null | null | http://arxiv.org/pdf/2405.19121v2 | 2024-06-02T18:03:44Z | 2024-05-29T14:28:08Z | Spatio-Spectral Graph Neural Networks | Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop neighborhood of a node and that information exchange between distant nodes is limited by over-squashing. Motivated by these limitations, we propose Spatio-Spectral Graph Neural Networks (S$^2$GNNs) -- a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters. Parameterizing filters partially in the frequency domain enables global yet efficient information propagation. We show that S$^2$GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs. Further, rethinking graph convolutions at a fundamental level unlocks new design spaces. For example, S$^2$GNNs allow for free positional encodings that make them strictly more expressive than the 1-Weisfeiler-Lehman (WL) test. Moreover, to obtain general-purpose S$^2$GNNs, we propose spectrally parametrized filters for directed graphs. S$^2$GNNs outperform spatial MPGNNs, graph transformers, and graph rewirings, e.g., on the peptide long-range benchmark tasks, and are competitive with state-of-the-art sequence modeling. On a 40 GB GPU, S$^2$GNNs scale to millions of nodes. | [
"['Simon Geisler' 'Arthur Kosmala' 'Daniel Herbst' 'Stephan Günnemann']"
] |
null | null | 2405.19146 | null | null | http://arxiv.org/pdf/2405.19146v1 | 2024-05-29T14:51:41Z | 2024-05-29T14:51:41Z | I Bet You Did Not Mean That: Testing Semantic Importance via Betting | Recent works have extended notions of feature importance to emph{semantic concepts} that are inherently interpretable to the users interacting with a black-box predictive model. Yet, precise statistical guarantees, such as false positive rate control, are needed to communicate findings transparently and to avoid unintended consequences in real-world scenarios. In this paper, we formalize the global (i.e., over a population) and local (i.e., for a sample) statistical importance of semantic concepts for the predictions of opaque models, by means of conditional independence, which allows for rigorous testing. We use recent ideas of sequential kernelized testing (SKIT) to induce a rank of importance across concepts, and showcase the effectiveness and flexibility of our framework on synthetic datasets as well as on image classification tasks using vision-language models such as CLIP. | [
"['Jacopo Teneggi' 'Jeremias Sulam']"
] |
null | null | 2405.19153 | null | null | http://arxiv.org/pdf/2405.19153v1 | 2024-05-29T14:59:49Z | 2024-05-29T14:59:49Z | A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning | Continual learning with deep neural networks presents challenges distinct from both the fixed-dataset and convex continual learning regimes. One such challenge is plasticity loss, wherein a neural network trained in an online fashion displays a degraded ability to fit new tasks. This problem has been extensively studied in both supervised learning and off-policy reinforcement learning (RL), where a number of remedies have been proposed. Still, plasticity loss has received less attention in the on-policy deep RL setting. Here we perform an extensive set of experiments examining plasticity loss and a variety of mitigation methods in on-policy deep RL. We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even resulting in performance that is worse than performing no intervention at all. In contrast, we find that a class of ``regenerative'' methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and more challenging environments like Montezuma's Revenge and ProcGen. | [
"['Arthur Juliani' 'Jordan T. Ash']"
] |
null | null | 2405.19156 | null | null | http://arxiv.org/pdf/2405.19156v1 | 2024-05-29T15:00:19Z | 2024-05-29T15:00:19Z | Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift | Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training distribution. Yet, learning theory of distribution shift bounds performance on the target distribution as a function of the discrepancy between the source and target, rarely guaranteeing high target accuracy. Motivated by this gap, this work takes a closer look at the theory of distribution shift for a classifier from a source to a target distribution. Instead of relying on the discrepancy, we adopt an Invariant-Risk-Minimization (IRM)-like assumption connecting the distributions, and characterize conditions under which data from a source distribution is sufficient for accurate classification of the target. When these conditions are not met, we show when only unlabeled data from the target is sufficient, and when labeled target data is needed. In all cases, we provide rigorous theoretical guarantees in the large sample regime. | [
"['Robi Bhattacharjee' 'Nick Rittler' 'Kamalika Chaudhuri']"
] |
null | null | 2405.19162 | null | null | http://arxiv.org/pdf/2405.19162v1 | 2024-05-29T15:06:10Z | 2024-05-29T15:06:10Z | Does learning the right latent variables necessarily improve in-context
learning? | Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting avenues for efficiently solving new tasks. For many tasks, e.g., linear regression, the data factorizes: examples are independent given a task latent that generates the data, e.g., linear coefficients. While an optimal predictor leverages this factorization by inferring task latents, it is unclear if Transformers implicitly do so or if they instead exploit heuristics and statistical shortcuts enabled by attention layers. Both scenarios have inspired active ongoing work. In this paper, we systematically investigate the effect of explicitly inferring task latents. We minimally modify the Transformer architecture with a bottleneck designed to prevent shortcuts in favor of more structured solutions, and then compare performance against standard Transformers across various ICL tasks. Contrary to intuition and some recent works, we find little discernible difference between the two; biasing towards task-relevant latent variables does not lead to better out-of-distribution performance, in general. Curiously, we find that while the bottleneck effectively learns to extract latent task variables from context, downstream processing struggles to utilize them for robust prediction. Our study highlights the intrinsic limitations of Transformers in achieving structured ICL solutions that generalize, and shows that while inferring the right latents aids interpretability, it is not sufficient to alleviate this problem. | [
"['Sarthak Mittal' 'Eric Elmoznino' 'Leo Gagnon' 'Sangnie Bhardwaj'\n 'Dhanya Sridhar' 'Guillaume Lajoie']"
] |
null | null | 2405.19166 | null | null | http://arxiv.org/pdf/2405.19166v1 | 2024-05-29T15:10:24Z | 2024-05-29T15:10:24Z | Transformers as Neural Operators for Solutions of Differential Equations
with Finite Regularity | Neural operator learning models have emerged as very effective surrogates in data-driven methods for partial differential equations (PDEs) across different applications from computational science and engineering. Such operator learning models not only predict particular instances of a physical or biological system in real-time but also forecast classes of solutions corresponding to a distribution of initial and boundary conditions or forcing terms. % DeepONet is the first neural operator model and has been tested extensively for a broad class of solutions, including Riemann problems. Transformers have not been used in that capacity, and specifically, they have not been tested for solutions of PDEs with low regularity. % In this work, we first establish the theoretical groundwork that transformers possess the universal approximation property as operator learning models. We then apply transformers to forecast solutions of diverse dynamical systems with solutions of finite regularity for a plurality of initial conditions and forcing terms. In particular, we consider three examples: the Izhikevich neuron model, the tempered fractional-order Leaky Integrate-and-Fire (LIF) model, and the one-dimensional Euler equation Riemann problem. For the latter problem, we also compare with variants of DeepONet, and we find that transformers outperform DeepONet in accuracy but they are computationally more expensive. | [
"['Benjamin Shih' 'Ahmad Peyvan' 'Zhongqiang Zhang' 'George Em Karniadakis']"
] |
null | null | 2405.19175 | null | null | http://arxiv.org/pdf/2405.19175v1 | 2024-05-29T15:17:53Z | 2024-05-29T15:17:53Z | Online Linear Regression in Dynamic Environments via Discounting | We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees emph{even in the complete absence of prior knowledge}. We present a novel analysis showing that a discounted variant of the Vovk-Azoury-Warmuth forecaster achieves dynamic regret of the form $R_{T}(vec{u})le Oleft(dlog(T)vee sqrt{dP_{T}^{gamma}(vec{u})T}right)$, where $P_{T}^{gamma}(vec{u})$ is a measure of variability of the comparator sequence, and show that the discount factor achieving this result can be learned on-the-fly. We show that this result is optimal by providing a matching lower bound. We also extend our results to emph{strongly-adaptive} guarantees which hold over every sub-interval $[a,b]subseteq[1,T]$ simultaneously. | [
"['Andrew Jacobsen' 'Ashok Cutkosky']"
] |
null | null | 2405.19178 | null | null | http://arxiv.org/pdf/2405.19178v1 | 2024-05-29T15:18:39Z | 2024-05-29T15:18:39Z | Model-independent cosmological inference post DESI DR1 BAO measurements | In this work, we implement Gaussian process regression to reconstruct the expansion history of the universe in a model-agnostic manner, using the Pantheon-Plus SN-Ia compilation in combination with two different BAO measurements (SDSS-IV and DESI DR1). In both the reconstructions, the $Lambda$CDM model is always included in the 95% confidence intervals. We find evidence that the DESI LRG data at $z_{text{eff}} = 0.51$ is not an outlier within our model-independent framework. We study the $mathcal{O}m$-diagnostics and the evolution of the total equation of state (EoS) of our universe, which hint towards the possibility of a quintessence-like dark energy scenario with a very slowly varying EoS, and a phantom-crossing in higher $z$. The entire exercise is later complemented by considering two more SN-Ia compilations - DES-5YR and Union3 - in combination with DESI BAO. Reconstruction with the DESI BAO + DES-5YR SN data sets predicts that the $Lambda$CDM model lies outside the 3$sigma$ confidence levels, whereas with DESI BAO + Union3 data, the $Lambda$CDM model is always included within 1$sigma$. We also report constraints on $H_0 r_d$ from our model-agnostic analysis, independent of the pre-recombination physics. Our results point towards an $approx$ 2$sigma$ discrepancy between the DESI + Pantheon-Plus and DESI + DES-5YR data sets, which calls for further investigation. | [
"['Purba Mukherjee' 'Anjan Ananda Sen']"
] |
null | null | 2405.19186 | null | null | http://arxiv.org/pdf/2405.19186v1 | 2024-05-29T15:28:42Z | 2024-05-29T15:28:42Z | MetaToken: Detecting Hallucination in Image Descriptions by Meta
Classification | Large Vision Language Models (LVLMs) have shown remarkable capabilities in multimodal tasks like visual question answering or image captioning. However, inconsistencies between the visual information and the generated text, a phenomenon referred to as hallucinations, remain an unsolved problem with regard to the trustworthiness of LVLMs. To address this problem, recent works proposed to incorporate computationally costly Large (Vision) Language Models in order to detect hallucinations on a sentence- or subsentence-level. In this work, we introduce MetaToken, a lightweight binary classifier to detect hallucinations on the token-level at negligible cost. Based on a statistical analysis, we reveal key factors of hallucinations in LVLMs which have been overseen in previous works. MetaToken can be applied to any open-source LVLM without any knowledge about ground truth data providing a reliable detection of hallucinations. We evaluate our method on four state-of-the-art LVLMs demonstrating the effectiveness of our approach. | [
"['Laura Fieback' 'Jakob Spiegelberg' 'Hanno Gottschalk']"
] |
null | null | 2405.19189 | null | null | http://arxiv.org/pdf/2405.19189v2 | 2024-06-09T15:56:59Z | 2024-05-29T15:29:46Z | Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement
Learning | With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. Most of these works utilized DMs to sample directly from the trajectory space, where DMs can be viewed as a combination of dynamics models and policies. In this work, we explore how to decouple DMs' ability as dynamics models in fully offline settings, allowing the learning policy to roll out trajectories. As DMs learn the data distribution from the dataset, their intrinsic policy is actually the behavior policy induced from the dataset, which results in a mismatch between the behavior policy and the learning policy. We propose Dynamics Diffusion, short as DyDiff, which can inject information from the learning policy to DMs iteratively. DyDiff ensures long-horizon rollout accuracy while maintaining policy consistency and can be easily deployed on model-free algorithms. We provide theoretical analysis to show the advantage of DMs on long-horizon rollout over models and demonstrate the effectiveness of DyDiff in the context of offline reinforcement learning, where the rollout dataset is provided but no online environment for interaction. Our code is at https://github.com/FineArtz/DyDiff. | [
"['Hanye Zhao' 'Xiaoshen Han' 'Zhengbang Zhu' 'Minghuan Liu' 'Yong Yu'\n 'Weinan Zhang']"
] |
null | null | 2405.19202 | null | null | http://arxiv.org/pdf/2405.19202v3 | 2024-06-14T13:28:43Z | 2024-05-29T15:42:10Z | Vulnerable Road User Detection and Safety Enhancement: A Comprehensive
Survey | Traffic incidents involving vulnerable road users (VRUs) constitute a significant proportion of global road accidents. Advances in traffic communication ecosystems, coupled with sophisticated signal processing and machine learning techniques, have facilitated the utilization of data from diverse sensors. Despite these advancements and the availability of extensive datasets, substantial progress is required to mitigate traffic casualties. This paper provides a comprehensive survey of state-of-the-art technologies and methodologies to enhance the safety of VRUs. The study delves into the communication networks between vehicles and VRUs, emphasizing the integration of advanced sensors and the availability of relevant datasets. It explores preprocessing techniques and data fusion methods to enhance sensor data quality. Furthermore, our study assesses critical simulation environments essential for developing and testing VRU safety systems. Our research also highlights recent advances in VRU detection and classification algorithms, addressing challenges such as variable environmental conditions. Additionally, we cover cutting-edge research in predicting VRU intentions and behaviors, which is crucial for proactive collision avoidance strategies. Through this survey, we aim to provide a comprehensive understanding of the current landscape of VRU safety technologies, identifying areas of progress and areas needing further research and development. | [
"['Renato M. Silva' 'Gregório F. Azevedo' 'Matheus V. V. Berto'\n 'Jean R. Rocha' 'Eduardo C. Fidelis' 'Matheus V. Nogueira'\n 'Pedro H. Lisboa' 'Tiago A. Almeida']"
] |
null | null | 2405.19206 | null | null | http://arxiv.org/pdf/2405.19206v1 | 2024-05-29T15:47:35Z | 2024-05-29T15:47:35Z | Matrix Manifold Neural Networks++ | Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that spherical and hyperbolic manifolds have the rich algebraic structures of gyrogroups and gyrovector spaces. This enables principled and effective generalizations of the most successful DNNs to these manifolds. Recently, some works have shown that many concepts in the theory of gyrogroups and gyrovector spaces can also be generalized to matrix manifolds such as Symmetric Positive Definite (SPD) and Grassmann manifolds. As a result, some building blocks for SPD and Grassmann neural networks, e.g., isometric models and multinomial logistic regression (MLR) can be derived in a way that is fully analogous to their spherical and hyperbolic counterparts. Building upon these works, we design fully-connected (FC) and convolutional layers for SPD neural networks. We also develop MLR on Symmetric Positive Semi-definite (SPSD) manifolds, and propose a method for performing backpropagation with the Grassmann logarithmic map in the projector perspective. We demonstrate the effectiveness of the proposed approach in the human action recognition and node classification tasks. | [
"['Xuan Son Nguyen' 'Shuo Yang' 'Aymeric Histace']"
] |
null | null | 2405.19210 | null | null | http://arxiv.org/pdf/2405.19210v1 | 2024-05-29T15:51:40Z | 2024-05-29T15:51:40Z | Gradient Guided Hypotheses: A unified solution to enable machine
learning models on scarce and noisy data regimes | Ensuring high-quality data is paramount for maximizing the performance of machine learning models and business intelligence systems. However, challenges in data quality, including noise in data capture, missing records, limited data production, and confounding variables, significantly constrain the potential performance of these systems. In this study, we propose an architecture-agnostic algorithm, Gradient Guided Hypotheses (GGH), designed to address these challenges. GGH analyses gradients from hypotheses as a proxy of distinct and possibly contradictory patterns in the data. This framework entails an additional step in machine learning training, where gradients can be included or excluded from backpropagation. In this manner, missing and noisy data are addressed through a unified solution that perceives both challenges as facets of the same overarching issue: the propagation of erroneous information. Experimental validation of GGH is conducted using real-world open-source datasets, where records with missing rates of up to 98.5% are simulated. Comparative analysis with state-of-the-art imputation methods demonstrates a substantial improvement in model performance achieved by GGH. Specifically in very high scarcity regimes, GGH was found to be the only viable solution. Additionally, GGH's noise detection capabilities are showcased by introducing simulated noise into the datasets and observing enhanced model performance after filtering out the noisy data. This study presents GGH as a promising solution for improving data quality and model performance in various applications. | [
"['Paulo Neves' 'Joerg K. Wegner' 'Philippe Schwaller']"
] |
null | null | 2405.19211 | null | null | http://arxiv.org/pdf/2405.19211v1 | 2024-05-29T15:53:23Z | 2024-05-29T15:53:23Z | Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning | Machine learning models are vulnerable to adversarial attacks, including attacks that leak information about the model's training data. There has recently been an increase in interest about how to best address privacy concerns, especially in the presence of data-removal requests. Machine unlearning algorithms aim to efficiently update trained models to comply with data deletion requests while maintaining performance and without having to resort to retraining the model from scratch, a costly endeavor. Several algorithms in the machine unlearning literature demonstrate some level of privacy gains, but they are often evaluated only on rudimentary membership inference attacks, which do not represent realistic threats. In this paper we describe and propose alternative evaluation methods for three key shortcomings in the current evaluation of unlearning algorithms. We show the utility of our alternative evaluations via a series of experiments of state-of-the-art unlearning algorithms on different computer vision datasets, presenting a more detailed picture of the state of the field. | [
"['Keltin Grimes' 'Collin Abidi' 'Cole Frank' 'Shannon Gallagher']"
] |
null | null | 2405.19212 | null | null | http://arxiv.org/pdf/2405.19212v2 | 2024-06-07T09:04:47Z | 2024-05-29T15:54:03Z | Partial Information Decomposition for Data Interpretability and Feature
Selection | In this paper, we introduce Partial Information Decomposition of Features (PIDF), a new paradigm for simultaneous data interpretability and feature selection. Contrary to traditional methods that assign a single importance value, our approach is based on three metrics per feature: the mutual information shared with the target variable, the feature's contribution to synergistic information, and the amount of this information that is redundant. In particular, we develop a novel procedure based on these three metrics, which reveals not only how features are correlated with the target but also the additional and overlapping information provided by considering them in combination with other features. We extensively evaluate PIDF using both synthetic and real-world data, demonstrating its potential applications and effectiveness, by considering case studies from genetics and neuroscience. | [
"['Charles Westphal' 'Stephen Hailes' 'Mirco Musolesi']"
] |
null | null | 2405.19213 | null | null | http://arxiv.org/pdf/2405.19213v1 | 2024-05-29T15:56:33Z | 2024-05-29T15:56:33Z | HawkVision: Low-Latency Modeless Edge AI Serving | The trend of modeless ML inference is increasingly growing in popularity as it hides the complexity of model inference from users and caters to diverse user and application accuracy requirements. Previous work mostly focuses on modeless inference in data centers. To provide low-latency inference, in this paper, we promote modeless inference at the edge. The edge environment introduces additional challenges related to low power consumption, limited device memory, and volatile network environments. To address these challenges, we propose HawkVision, which provides low-latency modeless serving of vision DNNs. HawkVision leverages a two-layer edge-DC architecture that employs confidence scaling to reduce the number of model options while meeting diverse accuracy requirements. It also supports lossy inference under volatile network environments. Our experimental results show that HawkVision outperforms current serving systems by up to 1.6X in P99 latency for providing modeless service. Our FPGA prototype demonstrates similar performance at certain accuracy levels with up to a 3.34X reduction in power consumption. | [
"['ChonLam Lao' 'Jiaqi Gao' 'Ganesh Ananthanarayanan' 'Aditya Akella'\n 'Minlan Yu']"
] |
null | null | 2405.19217 | null | null | http://arxiv.org/pdf/2405.19217v1 | 2024-05-29T16:00:19Z | 2024-05-29T16:00:19Z | LoByITFL: Low Communication Secure and Private Federated Learning | Federated Learning (FL) faces several challenges, such as the privacy of the clients data and security against Byzantine clients. Existing works treating privacy and security jointly make sacrifices on the privacy guarantee. In this work, we introduce LoByITFL, the first communication-efficient Information-Theoretic (IT) private and secure FL scheme that makes no sacrifices on the privacy guarantees while ensuring security against Byzantine adversaries. The key ingredients are a small and representative dataset available to the federator, a careful transformation of the FLTrust algorithm and the use of a trusted third party only in a one-time preprocessing phase before the start of the learning algorithm. We provide theoretical guarantees on privacy and Byzantine-resilience, and provide convergence guarantee and experimental results validating our theoretical findings. | [
"['Yue Xia' 'Christoph Hofmeister' 'Maximilian Egger' 'Rawad Bitar']"
] |
null | null | 2405.19221 | null | null | http://arxiv.org/pdf/2405.19221v1 | 2024-05-29T16:01:15Z | 2024-05-29T16:01:15Z | Domain adaptation in small-scale and heterogeneous biological datasets | Machine learning techniques are steadily becoming more important in modern biology, and are used to build predictive models, discover patterns, and investigate biological problems. However, models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories, due to differences in the statistical properties of these datasets. These could stem from technical differences, such as the measurement technique used, or from relevant biological differences between the populations studied. Domain adaptation, a type of transfer learning, can alleviate this problem by aligning the statistical distributions of features and samples among different datasets so that similar models can be applied across them. However, a majority of state-of-the-art domain adaptation methods are designed to work with large-scale data, mostly text and images, while biological datasets often suffer from small sample sizes, and possess complexities such as heterogeneity of the feature space. This Review aims to synthetically discuss domain adaptation methods in the context of small-scale and highly heterogeneous biological data. We describe the benefits and challenges of domain adaptation in biological research and critically discuss some of its objectives, strengths, and weaknesses through key representative methodologies. We argue for the incorporation of domain adaptation techniques to the computational biologist's toolkit, with further development of customized approaches. | [
"['Seyedmehdi Orouji' 'Martin C. Liu' 'Tal Korem' 'Megan A. K. Peters']"
] |
null | null | 2405.19225 | null | null | http://arxiv.org/pdf/2405.19225v1 | 2024-05-29T16:05:57Z | 2024-05-29T16:05:57Z | Synthetic Potential Outcomes for Mixtures of Treatment Effects | Modern data analysis frequently relies on the use of large datasets, often constructed as amalgamations of diverse populations or data-sources. Heterogeneity across these smaller datasets constitutes two major challenges for causal inference: (1) the source of each sample can introduce latent confounding between treatment and effect, and (2) diverse populations may respond differently to the same treatment, giving rise to heterogeneous treatment effects (HTEs). The issues of latent confounding and HTEs have been studied separately but not in conjunction. In particular, previous works only report the conditional average treatment effect (CATE) among similar individuals (with respect to the measured covariates). CATEs cannot resolve mixtures of potential treatment effects driven by latent heterogeneity, which we call mixtures of treatment effects (MTEs). Inspired by method of moment approaches to mixture models, we propose "synthetic potential outcomes" (SPOs). Our new approach deconfounds heterogeneity while also guaranteeing the identifiability of MTEs. This technique bypasses full recovery of a mixture, which significantly simplifies its requirements for identifiability. We demonstrate the efficacy of SPOs on synthetic data. | [
"['Bijan Mazaheri' 'Chandler Squires' 'Caroline Uhler']"
] |
null | null | 2405.19230 | null | null | http://arxiv.org/pdf/2405.19230v1 | 2024-05-29T16:07:39Z | 2024-05-29T16:07:39Z | Valid Conformal Prediction for Dynamic GNNs | Graph neural networks (GNNs) are powerful black-box models which have shown impressive empirical performance. However, without any form of uncertainty quantification, it can be difficult to trust such models in high-risk scenarios. Conformal prediction aims to address this problem, however, an assumption of exchangeability is required for its validity which has limited its applicability to static graphs and transductive regimes. We propose to use unfolding, which allows any existing static GNN to output a dynamic graph embedding with exchangeability properties. Using this, we extend the validity of conformal prediction to dynamic GNNs in both transductive and semi-inductive regimes. We provide a theoretical guarantee of valid conformal prediction in these cases and demonstrate the empirical validity, as well as the performance gains, of unfolded GNNs against standard GNN architectures on both simulated and real datasets. | [
"['Ed Davis' 'Ian Gallagher' 'Daniel John Lawson' 'Patrick Rubin-Delanchy']"
] |
null | null | 2405.19234 | null | null | http://arxiv.org/pdf/2405.19234v1 | 2024-05-29T16:13:54Z | 2024-05-29T16:13:54Z | Forward-Backward Knowledge Distillation for Continual Clustering | Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing on enabling neural networks to sequentially learn tasks without explicit label information. Catastrophic Forgetting (CF), where models forget previously learned tasks upon learning new ones, poses a significant challenge in continual learning, especially in UCL, where labeled information of data is not accessible. CF mitigation strategies, such as knowledge distillation and replay buffers, often face memory inefficiency and privacy issues. Although current research in UCL has endeavored to refine data representations and address CF in streaming data contexts, there is a noticeable lack of algorithms specifically designed for unsupervised clustering. To fill this gap, in this paper, we introduce the concept of Unsupervised Continual Clustering (UCC). We propose Forward-Backward Knowledge Distillation for unsupervised Continual Clustering (FBCC) to counteract CF within the context of UCC. FBCC employs a single continual learner (the ``teacher'') with a cluster projector, along with multiple student models, to address the CF issue. The proposed method consists of two phases: Forward Knowledge Distillation, where the teacher learns new clusters while retaining knowledge from previous tasks with guidance from specialized student models, and Backward Knowledge Distillation, where a student model mimics the teacher's behavior to retain task-specific knowledge, aiding the teacher in subsequent tasks. FBCC marks a pioneering approach to UCC, demonstrating enhanced performance and memory efficiency in clustering across various tasks, outperforming the application of clustering algorithms to the latent space of state-of-the-art UCL algorithms. | [
"['Mohammadreza Sadeghi' 'Zihan Wang' 'Narges Armanfard']"
] |
null | null | 2405.19236 | null | null | http://arxiv.org/pdf/2405.19236v1 | 2024-05-29T16:17:19Z | 2024-05-29T16:17:19Z | Exploring the impact of traffic signal control and connected and
automated vehicles on intersections safety: A deep reinforcement learning
approach | In transportation networks, intersections pose significant risks of collisions due to conflicting movements of vehicles approaching from different directions. To address this issue, various tools can exert influence on traffic safety both directly and indirectly. This study focuses on investigating the impact of adaptive signal control and connected and automated vehicles (CAVs) on intersection safety using a deep reinforcement learning approach. The objective is to assess the individual and combined effects of CAVs and adaptive traffic signal control on traffic safety, considering rear-end and crossing conflicts. The study employs a Deep Q Network (DQN) to regulate traffic signals and driving behaviors of both CAVs and Human Drive Vehicles (HDVs), and uses Time To Collision (TTC) metric to evaluate safety. The findings demonstrate a significant reduction in rear-end and crossing conflicts through the combined implementation of CAVs and DQNs-based traffic signal control. Additionally, the long-term positive effects of CAVs on safety are similar to the short-term effects of combined CAVs and DQNs-based traffic signal control. Overall, the study emphasizes the potential benefits of integrating CAVs and adaptive traffic signal control approaches in order to enhance traffic safety. The findings of this study could provide valuable insights for city officials and transportation authorities in developing effective strategies to improve safety at signalized intersections. | [
"['Amir Hossein Karbasi' 'Hao Yang' 'Saiedeh Razavi']"
] |
null | null | 2405.19237 | null | null | http://arxiv.org/pdf/2405.19237v1 | 2024-05-29T16:19:37Z | 2024-05-29T16:19:37Z | ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron
Pruning | While large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities, there are significant concerns about their potential misuse for generating unsafe content, violating copyright, and perpetuating societal biases. Recently, the text-to-image generation community has begun addressing these concerns by editing or unlearning undesired concepts from pre-trained models. However, these methods often involve data-intensive and inefficient fine-tuning or utilize various forms of token remapping, rendering them susceptible to adversarial jailbreaks. In this paper, we present a simple and effective training-free approach, ConceptPrune, wherein we first identify critical regions within pre-trained models responsible for generating undesirable concepts, thereby facilitating straightforward concept unlearning via weight pruning. Experiments across a range of concepts including artistic styles, nudity, object erasure, and gender debiasing demonstrate that target concepts can be efficiently erased by pruning a tiny fraction, approximately 0.12% of total weights, enabling multi-concept erasure and robustness against various white-box and black-box adversarial attacks. | [
"['Ruchika Chavhan' 'Da Li' 'Timothy Hospedales']"
] |
null | null | 2405.19247 | null | null | http://arxiv.org/pdf/2405.19247v1 | 2024-05-29T16:28:12Z | 2024-05-29T16:28:12Z | Comparative Study of Neighbor-based Methods for Local Outlier Detection | The neighbor-based method has become a powerful tool to handle the outlier detection problem, which aims to infer the abnormal degree of the sample based on the compactness of the sample and its neighbors. However, the existing methods commonly focus on designing different processes to locate outliers in the dataset, while the contributions of different types neighbors to outlier detection has not been well discussed. To this end, this paper studies the neighbor in the existing outlier detection algorithms and a taxonomy is introduced, which uses the three-level components of information, neighbor and methodology to define hybrid methods. This taxonomy can serve as a paradigm where a novel neighbor-based outlier detection method can be proposed by combining different components in this taxonomy. A large number of comparative experiments were conducted on synthetic and real-world datasets in terms of performance comparison and case study, and the results show that reverse K-nearest neighbor based methods achieve promising performance and dynamic selection method is suitable for working in high-dimensional space. Notably, it is verified that rationally selecting components from this taxonomy may create an algorithms superior to existing methods. | [
"['Zhuang Qi' 'Junlin Zhang' 'Xiaming Chen' 'Xin Qi']"
] |
null | null | 2405.19256 | null | null | http://arxiv.org/pdf/2405.19256v1 | 2024-05-29T16:41:42Z | 2024-05-29T16:41:42Z | Weak Generative Sampler to Efficiently Sample Invariant Distribution of
Stochastic Differential Equation | Sampling invariant distributions from an Ito diffusion process presents a significant challenge in stochastic simulation. Traditional numerical solvers for stochastic differential equations require both a fine step size and a lengthy simulation period, resulting in both biased and correlated samples. Current deep learning-based method solves the stationary Fokker--Planck equation to determine the invariant probability density function in form of deep neural networks, but they generally do not directly address the problem of sampling from the computed density function. In this work, we introduce a framework that employs a weak generative sampler (WGS) to directly generate independent and identically distributed (iid) samples induced by a transformation map derived from the stationary Fokker--Planck equation. Our proposed loss function is based on the weak form of the Fokker--Planck equation, integrating normalizing flows to characterize the invariant distribution and facilitate sample generation from the base distribution. Our randomized test function circumvents the need for mini-max optimization in the traditional weak formulation. Distinct from conventional generative models, our method neither necessitates the computationally intensive calculation of the Jacobian determinant nor the invertibility of the transformation map. A crucial component of our framework is the adaptively chosen family of test functions in the form of Gaussian kernel functions with centres selected from the generated data samples. Experimental results on several benchmark examples demonstrate the effectiveness of our method, which offers both low computational costs and excellent capability in exploring multiple metastable states. | [
"['Zhiqiang Cai' 'Yu Cao' 'Yuanfei Huang' 'Xiang Zhou']"
] |
null | null | 2405.19261 | null | null | http://arxiv.org/pdf/2405.19261v1 | 2024-05-29T16:55:08Z | 2024-05-29T16:55:08Z | Faster Cascades via Speculative Decoding | Cascades and speculative decoding are two common approaches to improving language models' inference efficiency. Both approaches involve interleaving models of different sizes, but via fundamentally distinct mechanisms: cascades employ a deferral rule that invokes the larger model only for "hard" inputs, while speculative decoding uses speculative execution to primarily invoke the larger model in parallel verification mode. These mechanisms offer different benefits: empirically, cascades are often capable of yielding better quality than even the larger model, while theoretically, speculative decoding offers a guarantee of quality-neutrality. In this paper, we leverage the best of both these approaches by designing new speculative cascading techniques that implement their deferral rule through speculative execution. We characterize the optimal deferral rule for our speculative cascades, and employ a plug-in approximation to the optimal rule. Through experiments with T5 models on benchmark language tasks, we show that the proposed approach yields better cost-quality trade-offs than cascading and speculative decoding baselines. | [
"['Harikrishna Narasimhan' 'Wittawat Jitkrittum' 'Ankit Singh Rawat'\n 'Seungyeon Kim' 'Neha Gupta' 'Aditya Krishna Menon' 'Sanjiv Kumar']"
] |
null | null | 2405.19262 | null | null | http://arxiv.org/pdf/2405.19262v1 | 2024-05-29T16:55:32Z | 2024-05-29T16:55:32Z | Weak-to-Strong Search: Align Large Language Models via Searching over
Small Language Models | Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduce $textit{weak-to-strong search}$, framing the alignment of a large language model as a test-time greedy search to maximize the log-likelihood difference between small tuned and untuned models while sampling from the frozen large model. This method serves both as (i) a compute-efficient model up-scaling strategy that avoids directly tuning the large model and as (ii) an instance of weak-to-strong generalization that enhances a strong model with weak test-time guidance. Empirically, we demonstrate the flexibility of weak-to-strong search across different tasks. In controlled-sentiment generation and summarization, we use tuned and untuned $texttt{gpt2}$s to effectively improve the alignment of large models without additional training. Crucially, in a more difficult instruction-following benchmark, AlpacaEval 2.0, we show that reusing off-the-shelf small model pairs (e.g., $texttt{zephyr-7b-beta}$ and its untuned version) can significantly improve the length-controlled win rates of both white-box and black-box large models against $texttt{gpt-4-turbo}$ (e.g., $34.4 rightarrow 37.9$ for $texttt{Llama-3-70B-Instruct}$ and $16.0 rightarrow 20.1$ for $texttt{gpt-3.5-turbo-instruct}$), despite the small models' low win rates $approx 10.0$. | [
"['Zhanhui Zhou' 'Zhixuan Liu' 'Jie Liu' 'Zhichen Dong' 'Chao Yang'\n 'Yu Qiao']"
] |
null | null | 2405.19269 | null | null | http://arxiv.org/pdf/2405.19269v1 | 2024-05-29T17:02:49Z | 2024-05-29T17:02:49Z | Rich-Observation Reinforcement Learning with Continuous Latent Dynamics | Sample-efficiency and reliability remain major bottlenecks toward wide adoption of reinforcement learning algorithms in continuous settings with high-dimensional perceptual inputs. Toward addressing these challenges, we introduce a new theoretical framework, RichCLD (Rich-Observation RL with Continuous Latent Dynamics), in which the agent performs control based on high-dimensional observations, but the environment is governed by low-dimensional latent states and Lipschitz continuous dynamics. Our main contribution is a new algorithm for this setting that is provably statistically and computationally efficient. The core of our algorithm is a new representation learning objective; we show that prior representation learning schemes tailored to discrete dynamics do not naturally extend to the continuous setting. Our new objective is amenable to practical implementation, and empirically, we find that it compares favorably to prior schemes in a standard evaluation protocol. We further provide several insights into the statistical complexity of the RichCLD framework, in particular proving that certain notions of Lipschitzness that admit sample-efficient learning in the absence of rich observations are insufficient in the rich-observation setting. | [
"['Yuda Song' 'Lili Wu' 'Dylan J. Foster' 'Akshay Krishnamurthy']"
] |
null | null | 2405.19272 | null | null | http://arxiv.org/pdf/2405.19272v1 | 2024-05-29T17:03:31Z | 2024-05-29T17:03:31Z | Mitigating Disparate Impact of Differential Privacy in Federated
Learning through Robust Clustering | Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data localized and often incorporates Differential Privacy (DP) to enhance privacy guarantees. Similar to previous work on DP in ML, we observed that differentially private federated learning (DPFL) introduces performance disparities, particularly affecting minority groups. Recent work has attempted to address performance fairness in vanilla FL through clustering, but this method remains sensitive and prone to errors, which are further exacerbated by the DP noise in DPFL. To fill this gap, in this paper, we propose a novel clustered DPFL algorithm designed to effectively identify clients' clusters in highly heterogeneous settings while maintaining high accuracy with DP guarantees. To this end, we propose to cluster clients based on both their model updates and training loss values. Our proposed approach also addresses the server's uncertainties in clustering clients' model updates by employing larger batch sizes along with Gaussian Mixture Model (GMM) to alleviate the impact of noise and potential clustering errors, especially in privacy-sensitive scenarios. We provide theoretical analysis of the effectiveness of our proposed approach. We also extensively evaluate our approach across diverse data distributions and privacy budgets and show its effectiveness in mitigating the disparate impact of DP in FL settings with a small computational cost. | [
"['Saber Malekmohammadi' 'Afaf Taik' 'Golnoosh Farnadi']"
] |
null | null | 2405.19276 | null | null | http://arxiv.org/pdf/2405.19276v1 | 2024-05-29T17:07:24Z | 2024-05-29T17:07:24Z | A Recipe for Charge Density Prediction | In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet existing approaches either lack accuracy or scalability. We propose a recipe that can achieve both. In particular, we identify three key ingredients: (1) representing the charge density with atomic and virtual orbitals (spherical fields centered at atom/virtual coordinates); (2) using expressive and learnable orbital basis sets (basis function for the spherical fields); and (3) using high-capacity equivariant neural network architecture. Our method achieves state-of-the-art accuracy while being more than an order of magnitude faster than existing methods. Furthermore, our method enables flexible efficiency-accuracy trade-offs by adjusting the model/basis sizes. | [
"['Xiang Fu' 'Andrew Rosen' 'Kyle Bystrom' 'Rui Wang' 'Albert Musaelian'\n 'Boris Kozinsky' 'Tess Smidt' 'Tommi Jaakkola']"
] |
null | null | 2405.19277 | null | null | http://arxiv.org/pdf/2405.19277v3 | 2024-06-12T17:24:00Z | 2024-05-29T17:07:33Z | Deep Latent Variable Modeling of Physiological Signals | A deep latent variable model is a powerful method for capturing complex distributions. These models assume that underlying structures, but unobserved, are present within the data. In this dissertation, we explore high-dimensional problems related to physiological monitoring using latent variable models. First, we present a novel deep state-space model to generate electrical waveforms of the heart using optically obtained signals as inputs. This can bring about clinical diagnoses of heart disease via simple assessment through wearable devices. Second, we present a brain signal modeling scheme that combines the strengths of probabilistic graphical models and deep adversarial learning. The structured representations can provide interpretability and encode inductive biases to reduce the data complexity of neural oscillations. The efficacy of the learned representations is further studied in epilepsy seizure detection formulated as an unsupervised learning problem. Third, we propose a framework for the joint modeling of physiological measures and behavior. Existing methods to combine multiple sources of brain data provided are limited. Direct analysis of the relationship between different types of physiological measures usually does not involve behavioral data. Our method can identify the unique and shared contributions of brain regions to behavior and can be used to discover new functions of brain regions. The success of these innovative computational methods would allow the translation of biomarker findings across species and provide insight into neurocognitive analysis in numerous biological studies and clinical diagnoses, as well as emerging consumer applications. | [
"['Khuong Vo']"
] |
null | null | 2405.19279 | null | null | http://arxiv.org/pdf/2405.19279v1 | 2024-05-29T17:11:28Z | 2024-05-29T17:11:28Z | Understanding and Minimising Outlier Features in Neural Network Training | Outlier Features (OF) are neurons whose activation magnitudes significantly exceed the average over a neural network's (NN) width. They are well known to emerge during standard transformer training and have the undesirable effect of hindering quantisation in afflicted models. Despite their practical importance, little is known behind why OFs emerge during training, nor how one can minimise them. Our work focuses on the above questions, first identifying several quantitative metrics, such as the kurtosis over neuron activation norms, to measure OFs. With these metrics, we study how architectural and optimisation choices influence OFs, and provide practical insights to minimise OFs during training. As highlights, we emphasise the importance of controlling signal propagation throughout training, and propose the Outlier Protected transformer block, which removes standard Pre-Norm layers to mitigate OFs, without loss of convergence speed or training stability. Overall, our findings shed new light on our understanding of, our ability to prevent, and the complexity of this important facet in NN training dynamics. | [
"['Bobby He' 'Lorenzo Noci' 'Daniele Paliotta' 'Imanol Schlag'\n 'Thomas Hofmann']"
] |
null | null | 2405.19296 | null | null | http://arxiv.org/pdf/2405.19296v1 | 2024-05-29T17:24:25Z | 2024-05-29T17:24:25Z | Neural Isometries: Taming Transformations for Equivariant ML | Real-world geometry and 3D vision tasks are replete with challenging symmetries that defy tractable analytical expression. In this paper, we introduce Neural Isometries, an autoencoder framework which learns to map the observation space to a general-purpose latent space wherein encodings are related by isometries whenever their corresponding observations are geometrically related in world space. Specifically, we regularize the latent space such that maps between encodings preserve a learned inner product and commute with a learned functional operator, in the same manner as rigid-body transformations commute with the Laplacian. This approach forms an effective backbone for self-supervised representation learning, and we demonstrate that a simple off-the-shelf equivariant network operating in the pre-trained latent space can achieve results on par with meticulously-engineered, handcrafted networks designed to handle complex, nonlinear symmetries. Furthermore, isometric maps capture information about the respective transformations in world space, and we show that this allows us to regress camera poses directly from the coefficients of the maps between encodings of adjacent views of a scene. | [
"['Thomas W. Mitchel' 'Michael Taylor' 'Vincent Sitzmann']"
] |
null | null | 2405.19300 | null | null | http://arxiv.org/pdf/2405.19300v1 | 2024-05-29T17:27:08Z | 2024-05-29T17:27:08Z | Measuring and Mitigating Bias for Tabular Datasets with Multiple
Protected Attributes | Motivated by the recital (67) of the current corrigendum of the AI Act in the European Union, we propose and present measures and mitigation strategies for discrimination in tabular datasets. We specifically focus on datasets that contain multiple protected attributes, such as nationality, age, and sex. This makes measuring and mitigating bias more challenging, as many existing methods are designed for a single protected attribute. This paper comes with a twofold contribution: Firstly, new discrimination measures are introduced. These measures are categorized in our framework along with existing ones, guiding researchers and practitioners in choosing the right measure to assess the fairness of the underlying dataset. Secondly, a novel application of an existing bias mitigation method, FairDo, is presented. We show that this strategy can mitigate any type of discrimination, including intersectional discrimination, by transforming the dataset. By conducting experiments on real-world datasets (Adult, Bank, Compas), we demonstrate that de-biasing datasets with multiple protected attributes is achievable. Further, the transformed fair datasets do not compromise any of the tested machine learning models' performances significantly when trained on these datasets compared to the original datasets. Discrimination was reduced by up to 83% in our experimentation. For most experiments, the disparity between protected groups was reduced by at least 7% and 27% on average. Generally, the findings show that the mitigation strategy used is effective, and this study contributes to the ongoing discussion on the implementation of the European Union's AI Act. | [
"['Manh Khoi Duong' 'Stefan Conrad']"
] |
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