categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.03768
| null | null |
http://arxiv.org/pdf/2403.03768v3
|
2024-03-18T15:05:55Z
|
2024-03-06T15:03:09Z
|
DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response
Evaluation
|
The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D.
|
[
"['Yushuai Wu' 'Ting Zhang' 'Hao Zhou' 'Hainan Wu' 'Hanwen Sunchu' 'Lei Hu'\n 'Xiaofang Chen' 'Suyuan Zhao' 'Gaochao Liu' 'Chao Sun' 'Jiahuan Zhang'\n 'Yizhen Luo' 'Peng Liu' 'Zaiqing Nie' 'Yushuai Wu']"
] |
null | null |
2403.03771
| null | null |
http://arxiv.org/abs/2403.03771v1
|
2024-03-06T15:05:39Z
|
2024-03-06T15:05:39Z
|
Joint Sparsity Pattern Learning Based Channel Estimation for Massive
MIMO-OTFS Systems
|
We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.
|
[
"['Kuo Meng' 'Shaoshi Yang' 'Xiao-Yang Wang' 'Yan Bu' 'Yurong Tang'\n 'Jianhua Zhang' 'Lajos Hanzo']"
] |
null | null |
2403.03772
| null | null |
http://arxiv.org/pdf/2403.03772v1
|
2024-03-06T15:06:11Z
|
2024-03-06T15:06:11Z
|
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
|
Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets. In response, more recent methods attempt to address this limitation by formulating causal discovery as structure learning with continuous optimization but such approaches thus far provide no statistical guarantees. In this paper, we show that by efficiently parallelizing existing causal discovery methods, we can in fact scale them to thousands of dimensions, making them practical for substantially larger-scale problems. In particular, we parallelize the LiNGAM method, which is quadratic in the number of variables, obtaining up to a 32-fold speed-up on benchmark datasets when compared with existing sequential implementations. Specifically, we focus on the causal ordering subprocedure in DirectLiNGAM and implement GPU kernels to accelerate it. This allows us to apply DirectLiNGAM to causal inference on large-scale gene expression data with genetic interventions yielding competitive results compared with specialized continuous optimization methods, and Var-LiNGAM for causal discovery on U.S. stock data.
|
[
"['Victor Akinwande' 'J. Zico Kolter']"
] |
null | null |
2403.03773
| null | null |
http://arxiv.org/pdf/2403.03773v1
|
2024-03-06T15:06:16Z
|
2024-03-06T15:06:16Z
|
Verified Training for Counterfactual Explanation Robustness under Data
Shift
|
Counterfactual explanations (CEs) enhance the interpretability of machine learning models by describing what changes to an input are necessary to change its prediction to a desired class. These explanations are commonly used to guide users' actions, e.g., by describing how a user whose loan application was denied can be approved for a loan in the future. Existing approaches generate CEs by focusing on a single, fixed model, and do not provide any formal guarantees on the CEs' future validity. When models are updated periodically to account for data shift, if the generated CEs are not robust to the shifts, users' actions may no longer have the desired impacts on their predictions. This paper introduces VeriTraCER, an approach that jointly trains a classifier and an explainer to explicitly consider the robustness of the generated CEs to small model shifts. VeriTraCER optimizes over a carefully designed loss function that ensures the verifiable robustness of CEs to local model updates, thus providing deterministic guarantees to CE validity. Our empirical evaluation demonstrates that VeriTraCER generates CEs that (1) are verifiably robust to small model updates and (2) display competitive robustness to state-of-the-art approaches in handling empirical model updates including random initialization, leave-one-out, and distribution shifts.
|
[
"['Anna P. Meyer' 'Yuhao Zhang' 'Aws Albarghouthi' \"Loris D'Antoni\"]"
] |
null | null |
2403.03777
| null | null |
http://arxiv.org/pdf/2403.03777v3
|
2024-07-03T10:02:39Z
|
2024-03-06T15:15:42Z
|
ENOT: Expectile Regularization for Fast and Accurate Training of Neural
Optimal Transport
|
We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularization on dual Kantorovich potentials. The main bottleneck of existing NOT solvers is associated with the procedure of finding a near-exact approximation of the conjugate operator (i.e., the c-transform), which is done either by optimizing over non-convex max-min objectives or by the computationally intensive fine-tuning of the initial approximated prediction. We resolve both issues by proposing a new, theoretically justified loss in the form of expectile regularisation which enforces binding conditions on the learning process of dual potentials. Such a regularization provides the upper bound estimation over the distribution of possible conjugate potentials and makes the learning stable, completely eliminating the need for additional extensive fine-tuning. Proposed method, called Expectile-Regularised Neural Optimal Transport (ENOT), outperforms previous state-of-the-art approaches on the established Wasserstein-2 benchmark tasks by a large margin (up to a 3-fold improvement in quality and up to a 10-fold improvement in runtime). Moreover, we showcase performance of ENOT for varying cost functions on different tasks such as image generation, showing robustness of proposed algorithm. OTT-JAX library includes our implementation of ENOT algorithm https://ott-jax.readthedocs.io/en/latest/tutorials/ENOT.html
|
[
"['Nazar Buzun' 'Maksim Bobrin' 'Dmitry V. Dylov']"
] |
null | null |
2403.03781
| null | null |
http://arxiv.org/pdf/2403.03781v1
|
2024-03-06T15:23:26Z
|
2024-03-06T15:23:26Z
|
Neural Architecture Search using Particle Swarm and Ant Colony
Optimization
|
Neural network models have a number of hyperparameters that must be chosen along with their architecture. This can be a heavy burden on a novice user, choosing which architecture and what values to assign to parameters. In most cases, default hyperparameters and architectures are used. Significant improvements to model accuracy can be achieved through the evaluation of multiple architectures. A process known as Neural Architecture Search (NAS) may be applied to automatically evaluate a large number of such architectures. A system integrating open source tools for Neural Architecture Search (OpenNAS), in the classification of images, has been developed as part of this research. OpenNAS takes any dataset of grayscale, or RBG images, and generates Convolutional Neural Network (CNN) architectures based on a range of metaheuristics using either an AutoKeras, a transfer learning or a Swarm Intelligence (SI) approach. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are used as the SI algorithms. Furthermore, models developed through such metaheuristics may be combined using stacking ensembles. In the context of this paper, we focus on training and optimizing CNNs using the Swarm Intelligence (SI) components of OpenNAS. Two major types of SI algorithms, namely PSO and ACO, are compared to see which is more effective in generating higher model accuracies. It is shown, with our experimental design, that the PSO algorithm performs better than ACO. The performance improvement of PSO is most notable with a more complex dataset. As a baseline, the performance of fine-tuned pre-trained models is also evaluated.
|
[
"['Séamus Lankford' 'Diarmuid Grimes']"
] |
null | null |
2403.03785
| null | null |
http://arxiv.org/pdf/2403.03785v1
|
2024-03-06T15:30:41Z
|
2024-03-06T15:30:41Z
|
A machine learning workflow to address credit default prediction
|
Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention. In this regard, CDP plays a crucial role in assessing the creditworthiness of individuals and businesses, enabling lenders to make informed decisions regarding loan approvals and risk management. In this paper, we propose a workflow-based approach to improve CDP, which refers to the task of assessing the probability that a borrower will default on his or her credit obligations. The workflow consists of multiple steps, each designed to leverage the strengths of different techniques featured in machine learning pipelines and, thus best solve the CDP task. We employ a comprehensive and systematic approach starting with data preprocessing using Weight of Evidence encoding, a technique that ensures in a single-shot data scaling by removing outliers, handling missing values, and making data uniform for models working with different data types. Next, we train several families of learning models, introducing ensemble techniques to build more robust models and hyperparameter optimization via multi-objective genetic algorithms to consider both predictive accuracy and financial aspects. Our research aims at contributing to the FinTech industry in providing a tool to move toward more accurate and reliable credit risk assessment, benefiting both lenders and borrowers.
|
[
"['Rambod Rahmani' 'Marco Parola' 'Mario G. C. A. Cimino']"
] |
null | null |
2403.03791
| null | null |
http://arxiv.org/pdf/2403.03791v1
|
2024-03-06T15:37:22Z
|
2024-03-06T15:37:22Z
|
KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing
Patient Data with Knowledge Graphs
|
Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates a deep bi-level attention synergy method for in-depth information fusion, enabling distinct encoding of treatment-covariate and outcome-covariate relationships. KG-TREAT also incorporates two pre-training tasks to ensure a thorough grounding and contextualization of patient data and KGs. Evaluation on four downstream TEE tasks shows KG-TREAT's superiority over existing methods, with an average improvement of 7% in Area under the ROC Curve (AUC) and 9% in Influence Function-based Precision of Estimating Heterogeneous Effects (IF-PEHE). The effectiveness of our estimated treatment effects is further affirmed by alignment with established randomized clinical trial findings.
|
[
"['Ruoqi Liu' 'Lingfei Wu' 'Ping Zhang']"
] |
null | null |
2403.03792
| null | null |
http://arxiv.org/pdf/2403.03792v2
|
2024-05-02T09:25:38Z
|
2024-03-06T15:40:30Z
|
Neural Exec: Learning (and Learning from) Execution Triggers for Prompt
Injection Attacks
|
We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., "Ignore previous instructions and..."), we show that it is possible to conceptualize the creation of execution triggers as a differentiable search problem and use learning-based methods to autonomously generate them. Our results demonstrate that a motivated adversary can forge triggers that are not only drastically more effective than current handcrafted ones but also exhibit inherent flexibility in shape, properties, and functionality. In this direction, we show that an attacker can design and generate Neural Execs capable of persisting through multi-stage preprocessing pipelines, such as in the case of Retrieval-Augmented Generation (RAG)-based applications. More critically, our findings show that attackers can produce triggers that deviate markedly in form and shape from any known attack, sidestepping existing blacklist-based detection and sanitation approaches.
|
[
"['Dario Pasquini' 'Martin Strohmeier' 'Carmela Troncoso']"
] |
null | null |
2403.03811
| null | null |
http://arxiv.org/pdf/2403.03811v1
|
2024-03-06T16:00:46Z
|
2024-03-06T16:00:46Z
|
Incentivized Learning in Principal-Agent Bandit Games
|
This work considers a repeated principal-agent bandit game, where the principal can only interact with her environment through the agent. The principal and the agent have misaligned objectives and the choice of action is only left to the agent. However, the principal can influence the agent's decisions by offering incentives which add up to his rewards. The principal aims to iteratively learn an incentive policy to maximize her own total utility. This framework extends usual bandit problems and is motivated by several practical applications, such as healthcare or ecological taxation, where traditionally used mechanism design theories often overlook the learning aspect of the problem. We present nearly optimal (with respect to a horizon $T$) learning algorithms for the principal's regret in both multi-armed and linear contextual settings. Finally, we support our theoretical guarantees through numerical experiments.
|
[
"['Antoine Scheid' 'Daniil Tiapkin' 'Etienne Boursier' 'Aymeric Capitaine'\n 'El Mahdi El Mhamdi' 'Eric Moulines' 'Michael I. Jordan' 'Alain Durmus']"
] |
null | null |
2403.03812
| null | null |
http://arxiv.org/pdf/2403.03812v1
|
2024-03-06T16:00:50Z
|
2024-03-06T16:00:50Z
|
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
|
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer duration. Our experiments further indicate that ProbSAINT is especially accurate on instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial.
|
[
"['Kiran Madhusudhanan' 'Gunnar Behrens' 'Maximilian Stubbemann'\n 'Lars Schmidt-Thieme']"
] |
null | null |
2403.03816
| null | null |
http://arxiv.org/pdf/2403.03816v1
|
2024-03-06T16:03:37Z
|
2024-03-06T16:03:37Z
|
Targeted Variance Reduction: Robust Bayesian Optimization of Black-Box
Simulators with Noise Parameters
|
The optimization of a black-box simulator over control parameters $mathbf{x}$ arises in a myriad of scientific applications. In such applications, the simulator often takes the form $f(mathbf{x},boldsymbol{theta})$, where $boldsymbol{theta}$ are parameters that are uncertain in practice. Robust optimization aims to optimize the objective $mathbb{E}[f(mathbf{x},boldsymbol{Theta})]$, where $boldsymbol{Theta} sim mathcal{P}$ is a random variable that models uncertainty on $boldsymbol{theta}$. For this, existing black-box methods typically employ a two-stage approach for selecting the next point $(mathbf{x},boldsymbol{theta})$, where $mathbf{x}$ and $boldsymbol{theta}$ are optimized separately via different acquisition functions. As such, these approaches do not employ a joint acquisition over $(mathbf{x},boldsymbol{theta})$, and thus may fail to fully exploit control-to-noise interactions for effective robust optimization. To address this, we propose a new Bayesian optimization method called Targeted Variance Reduction (TVR). The TVR leverages a novel joint acquisition function over $(mathbf{x},boldsymbol{theta})$, which targets variance reduction on the objective within the desired region of improvement. Under a Gaussian process surrogate on $f$, the TVR acquisition can be evaluated in closed form, and reveals an insightful exploration-exploitation-precision trade-off for robust black-box optimization. The TVR can further accommodate a broad class of non-Gaussian distributions on $mathcal{P}$ via a careful integration of normalizing flows. We demonstrate the improved performance of TVR over the state-of-the-art in a suite of numerical experiments and an application to the robust design of automobile brake discs under operational uncertainty.
|
[
"['John Joshua Miller' 'Simon Mak']"
] |
null | null |
2403.03827
| null | null |
http://arxiv.org/pdf/2403.03827v1
|
2024-03-06T16:17:34Z
|
2024-03-06T16:17:34Z
|
Linear and nonlinear system identification under $\ell_1$- and
group-Lasso regularization via L-BFGS-B
|
In this paper, we propose an approach for identifying linear and nonlinear discrete-time state-space models, possibly under $ell_1$- and group-Lasso regularization, based on the L-BFGS-B algorithm. For the identification of linear models, we show that, compared to classical linear subspace methods, the approach often provides better results, is much more general in terms of the loss and regularization terms used, and is also more stable from a numerical point of view. The proposed method not only enriches the existing set of linear system identification tools but can be also applied to identifying a very broad class of parametric nonlinear state-space models, including recurrent neural networks. We illustrate the approach on synthetic and experimental datasets and apply it to solve the challenging industrial robot benchmark for nonlinear multi-input/multi-output system identification proposed by Weigand et al. (2022). A Python implementation of the proposed identification method is available in the package texttt{jax-sysid}, available at url{https://github.com/bemporad/jax-sysid}.
|
[
"['Alberto Bemporad']"
] |
null | null |
2403.03835
| null | null |
http://arxiv.org/pdf/2403.03835v3
|
2024-05-09T03:50:30Z
|
2024-03-06T16:26:40Z
|
Cobweb: An Incremental and Hierarchical Model of Human-Like Category
Learning
|
Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as basic-level, typicality, and fan effects. However, a broader evaluation of Cobweb as a model of human categorization remains lacking. The current study addresses this gap. It establishes Cobweb's alignment with classical human category learning effects. It also explores Cobweb's flexibility to exhibit both exemplar- and prototype-like learning within a single framework. These findings set the stage for further research on Cobweb as a robust model of human category learning.
|
[
"['Xin Lian' 'Sashank Varma' 'Christopher J. MacLellan']"
] |
null | null |
2403.03838
| null | null |
http://arxiv.org/pdf/2403.03838v1
|
2024-03-06T16:31:56Z
|
2024-03-06T16:31:56Z
|
Feature Selection as Deep Sequential Generative Learning
|
Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., Lasso) methods have hyperparameters (e.g., top-K, score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature subset in a huge discrete space and is computationally costly. To transform the way of feature selection, we regard a selected feature subset as a selection decision token sequence and reformulate feature selection as a deep sequential generative learning task that distills feature knowledge and generates decision sequences. Our method includes three steps: (1) We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores. (2) We leverage the trained feature subset utility evaluator as a gradient provider to guide the identification of the optimal feature subset embedding;(3) We decode the optimal feature subset embedding to autoregressively generate the best feature selection decision sequence with autostop. Extensive experimental results show this generative perspective is effective and generic, without large discrete search space and expert-specific hyperparameters.
|
[
"['Wangyang Ying' 'Dongjie Wang' 'Haifeng Chen' 'Yanjie Fu']"
] |
null | null |
2403.03846
| null | null |
http://arxiv.org/pdf/2403.03846v1
|
2024-03-06T16:42:10Z
|
2024-03-06T16:42:10Z
|
On the Effectiveness of Distillation in Mitigating Backdoors in
Pre-trained Encoder
|
In this paper, we study a defense against poisoned encoders in SSL called distillation, which is a defense used in supervised learning originally. Distillation aims to distill knowledge from a given model (a.k.a the teacher net) and transfer it to another (a.k.a the student net). Now, we use it to distill benign knowledge from poisoned pre-trained encoders and transfer it to a new encoder, resulting in a clean pre-trained encoder. In particular, we conduct an empirical study on the effectiveness and performance of distillation against poisoned encoders. Using two state-of-the-art backdoor attacks against pre-trained image encoders and four commonly used image classification datasets, our experimental results show that distillation can reduce attack success rate from 80.87% to 27.51% while suffering a 6.35% loss in accuracy. Moreover, we investigate the impact of three core components of distillation on performance: teacher net, student net, and distillation loss. By comparing 4 different teacher nets, 3 student nets, and 6 distillation losses, we find that fine-tuned teacher nets, warm-up-training-based student nets, and attention-based distillation loss perform best, respectively.
|
[
"['Tingxu Han' 'Shenghan Huang' 'Ziqi Ding' 'Weisong Sun' 'Yebo Feng'\n 'Chunrong Fang' 'Jun Li' 'Hanwei Qian' 'Cong Wu' 'Quanjun Zhang'\n 'Yang Liu' 'Zhenyu Chen']"
] |
null | null |
2403.03848
| null | null |
http://arxiv.org/pdf/2403.03848v1
|
2024-03-06T16:49:08Z
|
2024-03-06T16:49:08Z
|
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement
Learning
|
Recent advances of locomotion controllers utilizing deep reinforcement learning (RL) have yielded impressive results in terms of achieving rapid and robust locomotion across challenging terrain, such as rugged rocks, non-rigid ground, and slippery surfaces. However, while these controllers primarily address challenges underneath the robot, relatively little research has investigated legged mobility through confined 3D spaces, such as narrow tunnels or irregular voids, which impose all-around constraints. The cyclic gait patterns resulted from existing RL-based methods to learn parameterized locomotion skills characterized by motion parameters, such as velocity and body height, may not be adequate to navigate robots through challenging confined 3D spaces, requiring both agile 3D obstacle avoidance and robust legged locomotion. Instead, we propose to learn locomotion skills end-to-end from goal-oriented navigation in confined 3D spaces. To address the inefficiency of tracking distant navigation goals, we introduce a hierarchical locomotion controller that combines a classical planner tasked with planning waypoints to reach a faraway global goal location, and an RL-based policy trained to follow these waypoints by generating low-level motion commands. This approach allows the policy to explore its own locomotion skills within the entire solution space and facilitates smooth transitions between local goals, enabling long-term navigation towards distant goals. In simulation, our hierarchical approach succeeds at navigating through demanding confined 3D environments, outperforming both pure end-to-end learning approaches and parameterized locomotion skills. We further demonstrate the successful real-world deployment of our simulation-trained controller on a real robot.
|
[
"['Zifan Xu' 'Amir Hossain Raj' 'Xuesu Xiao' 'Peter Stone']"
] |
null | null |
2403.03849
| null | null |
http://arxiv.org/pdf/2403.03849v4
|
2024-06-10T14:06:05Z
|
2024-03-06T16:49:33Z
|
MedMamba: Vision Mamba for Medical Image Classification
|
Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model long-range dependencies while maintaining linear computational complexity. Inspired by it, we proposed MedMamba, the first vision Mamba for generalized medical image classification. Concretely, we introduced a novel hybrid basic block named SS-Conv-SSM, which integrates the convolutional layers for extracting local features with the abilities of SSM to capture long-range dependencies, aiming to model medical images from different image modalities efficiently. By employing the grouped convolution strategy and channel-shuffle operation, MedMamba successfully provides fewer model parameters and a lower computational burden for efficient applications. To demonstrate the potential of MedMamba, we conducted extensive experiments using 16 datasets containing ten imaging modalities and 411,007 images. Experimental results show that the proposed MedMamba demonstrates competitive performance in classifying various medical images compared with the state-of-the-art methods. Our work is aims to establish a new baseline for medical image classification and provide valuable insights for developing more powerful SSM-based artificial intelligence algorithms and application systems in the medical field. The source codes and all pre-trained weights of MedMamba are available at https://github.com/YubiaoYue/MedMamba.
|
[
"['Yubiao Yue' 'Zhenzhang Li']"
] |
null | null |
2403.03850
| null | null |
http://arxiv.org/pdf/2403.03850v2
|
2024-05-23T16:51:43Z
|
2024-03-06T16:55:40Z
|
Conformal prediction for multi-dimensional time series by ellipsoidal
sets
|
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problems in supervised learning, most CP methods focus on building prediction intervals for univariate responses. In this work, we develop a sequential CP method called $texttt{MultiDimSPCI}$ that builds prediction $textit{regions}$ for a multivariate response, especially in the context of multivariate time series, which are not exchangeable. Theoretically, we estimate $textit{finite-sample}$ high-probability bounds on the conditional coverage gap. Empirically, we demonstrate that $texttt{MultiDimSPCI}$ maintains valid coverage on a wide range of multivariate time series while producing smaller prediction regions than CP and non-CP baselines.
|
[
"['Chen Xu' 'Hanyang Jiang' 'Yao Xie']"
] |
null | null |
2403.03852
| null | null |
http://arxiv.org/pdf/2403.03852v1
|
2024-03-06T17:02:39Z
|
2024-03-06T17:02:39Z
|
Accelerating Convergence of Score-Based Diffusion Models, Provably
|
Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampling phase. Despite a flurry of recent activities towards speeding up diffusion generative modeling in practice, theoretical underpinnings for acceleration techniques remain severely limited. In this paper, we design novel training-free algorithms to accelerate popular deterministic (i.e., DDIM) and stochastic (i.e., DDPM) samplers. Our accelerated deterministic sampler converges at a rate $O(1/{T}^2)$ with $T$ the number of steps, improving upon the $O(1/T)$ rate for the DDIM sampler; and our accelerated stochastic sampler converges at a rate $O(1/T)$, outperforming the rate $O(1/sqrt{T})$ for the DDPM sampler. The design of our algorithms leverages insights from higher-order approximation, and shares similar intuitions as popular high-order ODE solvers like the DPM-Solver-2. Our theory accommodates $ell_2$-accurate score estimates, and does not require log-concavity or smoothness on the target distribution.
|
[
"['Gen Li' 'Yu Huang' 'Timofey Efimov' 'Yuting Wei' 'Yuejie Chi'\n 'Yuxin Chen']"
] |
null | null |
2403.03856
| null | null |
http://arxiv.org/pdf/2403.03856v1
|
2024-03-06T17:06:11Z
|
2024-03-06T17:06:11Z
|
Public-data Assisted Private Stochastic Optimization: Power and
Limitations
|
We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any $(epsilon,delta)$-PA-DP has excess risk $tilde{Omega}big(minbig{frac{1}{sqrt{n_{text{pub}}}},frac{1}{sqrt{n}}+frac{sqrt{d}}{nepsilon} big} big)$, where $d$ is the dimension, ${n_{text{pub}}}$ is the number of public samples, ${n_{text{priv}}}$ is the number of private samples, and $n={n_{text{pub}}}+{n_{text{priv}}}$. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with textit{unlabeled} public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given $tilde{O}({n_{text{priv}}}epsilon)$ unlabeled public samples, achieves the dimension independent rate $tilde{O}big(frac{1}{sqrt{{n_{text{priv}}}}} + frac{1}{sqrt{{n_{text{priv}}}epsilon}}big)$. We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries.
|
[
"['Enayat Ullah' 'Michael Menart' 'Raef Bassily' 'Cristóbal Guzmán'\n 'Raman Arora']"
] |
null | null |
2403.03861
| null | null |
http://arxiv.org/pdf/2403.03861v2
|
2024-03-19T19:51:09Z
|
2024-03-06T17:11:38Z
|
Designing Informative Metrics for Few-Shot Example Selection
|
Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. However, selecting the "best" examples remains an open challenge. We propose a complexity-based prompt selection approach for sequence tagging tasks. This approach avoids the training of a dedicated model for selection of examples, and instead uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. We use both sentence- and word-level metrics to match the complexity of examples to the (test) sentence being considered. Our results demonstrate that our approach extracts greater performance from PLMs: it achieves state-of-the-art performance on few-shot NER, achieving a 5% absolute improvement in F1 score on the CoNLL2003 dataset for GPT-4. We also see large gains of upto 28.85 points (F1/Acc.) in smaller models like GPT-j-6B.
|
[
"['Rishabh Adiga' 'Lakshminarayanan Subramanian' 'Varun Chandrasekaran']"
] |
null | null |
2403.03867
| null | null |
http://arxiv.org/pdf/2403.03867v1
|
2024-03-06T17:17:36Z
|
2024-03-06T17:17:36Z
|
On the Origins of Linear Representations in Large Language Models
|
Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism to show that the next token prediction objective (softmax with cross-entropy) and the implicit bias of gradient descent together promote the linear representation of concepts. Experiments show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already suffices to yield linear representations. We additionally confirm some predictions of the theory using the LLaMA-2 large language model, giving evidence that the simplified model yields generalizable insights.
|
[
"['Yibo Jiang' 'Goutham Rajendran' 'Pradeep Ravikumar' 'Bryon Aragam'\n 'Victor Veitch']"
] |
null | null |
2403.03870
| null | null |
http://arxiv.org/pdf/2403.03870v1
|
2024-03-06T17:23:28Z
|
2024-03-06T17:23:28Z
|
Learning to Decode Collaboratively with Multiple Language Models
|
We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates the next token as a latent variable. By optimizing the marginal likelihood of a training set under our latent variable model, the base LLM automatically learns when to generate itself and when to call on one of the ``assistant'' language models to generate, all without direct supervision. Token-level collaboration during decoding allows for a fusion of each model's expertise in a manner tailored to the specific task at hand. Our collaborative decoding is especially useful in cross-domain settings where a generalist base LLM learns to invoke domain expert models. On instruction-following, domain-specific QA, and reasoning tasks, we show that the performance of the joint system exceeds that of the individual models. Through qualitative analysis of the learned latent decisions, we show models trained with our method exhibit several interesting collaboration patterns, e.g., template-filling. Our code is available at https://github.com/clinicalml/co-llm.
|
[
"['Shannon Zejiang Shen' 'Hunter Lang' 'Bailin Wang' 'Yoon Kim'\n 'David Sontag']"
] |
null | null |
2403.03871
| null | null |
http://arxiv.org/pdf/2403.03871v1
|
2024-03-06T17:23:28Z
|
2024-03-06T17:23:28Z
|
Decoupled Vertical Federated Learning for Practical Training on
Vertically Partitioned Data
|
Vertical Federated Learning (VFL) is an emergent distributed machine learning paradigm wherein owners of disjoint features of a common set of entities collaborate to learn a global model without sharing data. In VFL, a host client owns data labels for each entity and learns a final representation based on intermediate local representations from all guest clients. Therefore, the host is a single point of failure and label feedback can be used by malicious guest clients to infer private features. Requiring all participants to remain active and trustworthy throughout the entire training process is generally impractical and altogether infeasible outside of controlled environments. We propose Decoupled VFL (DVFL), a blockwise learning approach to VFL. By training each model on its own objective, DVFL allows for decentralized aggregation and isolation between feature learning and label supervision. With these properties, DVFL is fault tolerant and secure. We implement DVFL to train split neural networks and show that model performance is comparable to VFL on a variety of classification datasets.
|
[
"['Avi Amalanshu' 'Yash Sirvi' 'David I. Inouye']"
] |
null | null |
2403.03880
| null | null |
http://arxiv.org/pdf/2403.03880v2
|
2024-05-23T15:03:45Z
|
2024-03-06T17:40:26Z
|
Almost Surely Asymptotically Constant Graph Neural Networks
|
We present a new angle on the expressive power of graph neural networks (GNNs) by studying how the predictions of a GNN probabilistic classifier evolve as we apply it on larger graphs drawn from some random graph model. We show that the output converges to a constant function, which upper-bounds what these classifiers can uniformly express. This strong convergence phenomenon applies to a very wide class of GNNs, including state of the art models, with aggregates including mean and the attention-based mechanism of graph transformers. Our results apply to a broad class of random graph models, including sparse and dense variants of the ErdH{o}s-R'enyi model, the stochastic block model, and the Barab'asi-Albert model. We empirically validate these findings, observing that the convergence phenomenon appears not only on random graphs but also on some real-world graphs.
|
[
"['Sam Adam-Day' 'Michael Benedikt' 'İsmail İlkan Ceylan'\n 'Ben Finkelshtein']"
] |
null | null |
2403.03881
| null | null |
http://arxiv.org/pdf/2403.03881v3
|
2024-07-11T09:10:10Z
|
2024-03-06T17:41:41Z
|
Latent Dataset Distillation with Diffusion Models
|
Machine learning traditionally relies on increasingly larger datasets. Yet, such datasets pose major storage challenges and usually contain non-influential samples, which could be ignored during training without negatively impacting the training quality. In response, the idea of distilling a dataset into a condensed set of synthetic samples, i.e., a distilled dataset, emerged. One key aspect is the selected architecture, usually ConvNet, for linking the original and synthetic datasets. However, the final accuracy is lower if the employed model architecture differs from that used during distillation. Another challenge is the generation of high-resolution images (128x128 and higher). To address both challenges, this paper proposes Latent Dataset Distillation with Diffusion Models (LD3M) that combine diffusion in latent space with dataset distillation. Our novel diffusion process is tailored for this task and significantly improves the gradient flow for distillation. By adjusting the number of diffusion steps, LD3M also offers a convenient way of controlling the trade-off between distillation speed and dataset quality. Overall, LD3M consistently outperforms state-of-the-art methods by up to 4.8 p.p. and 4.2 p.p. for 1 and 10 images per class, respectively, and on several ImageNet subsets and high resolutions (128x128 and 256x256).
|
[
"['Brian B. Moser' 'Federico Raue' 'Sebastian Palacio' 'Stanislav Frolov'\n 'Andreas Dengel']"
] |
null | null |
2403.03890
| null | null |
http://arxiv.org/pdf/2403.03890v1
|
2024-03-06T17:50:26Z
|
2024-03-06T17:50:26Z
|
Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic
Manipulation
|
This paper introduces Hierarchical Diffusion Policy (HDP), a hierarchical agent for multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP), and a low-level goal-conditioned diffusion policy which generates optimal motion trajectories. The factorised policy representation allows HDP to tackle both long-horizon task planning while generating fine-grained low-level actions. To generate context-aware motion trajectories while satisfying robot kinematics constraints, we present a novel kinematics-aware goal-conditioned control agent, Robot Kinematics Diffuser (RK-Diffuser). Specifically, RK-Diffuser learns to generate both the end-effector pose and joint position trajectories, and distill the accurate but kinematics-unaware end-effector pose diffuser to the kinematics-aware but less accurate joint position diffuser via differentiable kinematics. Empirically, we show that HDP achieves a significantly higher success rate than the state-of-the-art methods in both simulation and real-world.
|
[
"['Xiao Ma' 'Sumit Patidar' 'Iain Haughton' 'Stephen James']"
] |
null | null |
2403.03891
| null | null |
http://arxiv.org/pdf/2403.03891v1
|
2024-03-06T17:51:04Z
|
2024-03-06T17:51:04Z
|
Joint multi-task learning improves weakly-supervised biomarker
prediction in computational pathology
|
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regression-based DL has seen an increasing interest. Nonetheless, clinical decision making often requires a categorical outcome. Consequently, we developed a weakly-supervised joint multi-task Transformer architecture which has been trained and evaluated on four public patient cohorts for the prediction of two key predictive biomarkers, microsatellite instability (MSI) and homologous recombination deficiency (HRD), trained with auxiliary regression tasks related to the tumor microenvironment. Moreover, we perform a comprehensive benchmark of 16 approaches of task balancing for weakly-supervised joint multi-task learning in computational pathology. Using our novel approach, we improve over the state-of-the-art area under the receiver operating characteristic by +7.7% and +4.1%, as well as yielding better clustering of latent embeddings by +8% and +5% for the prediction of MSI and HRD in external cohorts, respectively.
|
[
"['Omar S. M. El Nahhas' 'Georg Wölflein' 'Marta Ligero' 'Tim Lenz'\n 'Marko van Treeck' 'Firas Khader' 'Daniel Truhn' 'Jakob Nikolas Kather']"
] |
null | null |
2403.03896
| null | null |
http://arxiv.org/pdf/2403.03896v1
|
2024-03-06T17:54:50Z
|
2024-03-06T17:54:50Z
|
DART: Implicit Doppler Tomography for Radar Novel View Synthesis
|
Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracking. However, simulating realistic radar scans is a challenging task that requires an accurate model of the scene, radio frequency material properties, and a corresponding radar synthesis function. Rather than specifying these models explicitly, we propose DART - Doppler Aided Radar Tomography, a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images.
|
[
"['Tianshu Huang' 'John Miller' 'Akarsh Prabhakara' 'Tao Jin'\n 'Tarana Laroia' 'Zico Kolter' 'Anthony Rowe']"
] |
null | null |
2403.03905
| null | null |
http://arxiv.org/pdf/2403.03905v3
|
2024-06-11T05:43:50Z
|
2024-03-06T18:07:20Z
|
Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
|
The $k$-principal component analysis ($k$-PCA) problem is a fundamental algorithmic primitive that is widely-used in data analysis and dimensionality reduction applications. In statistical settings, the goal of $k$-PCA is to identify a top eigenspace of the covariance matrix of a distribution, which we only have black-box access to via samples. Motivated by these settings, we analyze black-box deflation methods as a framework for designing $k$-PCA algorithms, where we model access to the unknown target matrix via a black-box $1$-PCA oracle which returns an approximate top eigenvector, under two popular notions of approximation. Despite being arguably the most natural reduction-based approach to $k$-PCA algorithm design, such black-box methods, which recursively call a $1$-PCA oracle $k$ times, were previously poorly-understood. Our main contribution is significantly sharper bounds on the approximation parameter degradation of deflation methods for $k$-PCA. For a quadratic form notion of approximation we term ePCA (energy PCA), we show deflation methods suffer no parameter loss. For an alternative well-studied approximation notion we term cPCA (correlation PCA), we tightly characterize the parameter regimes where deflation methods are feasible. Moreover, we show that in all feasible regimes, $k$-cPCA deflation algorithms suffer no asymptotic parameter loss for any constant $k$. We apply our framework to obtain state-of-the-art $k$-PCA algorithms robust to dataset contamination, improving prior work in sample complexity by a $mathsf{poly}(k)$ factor.
|
[
"['Arun Jambulapati' 'Syamantak Kumar' 'Jerry Li' 'Shourya Pandey'\n 'Ankit Pensia' 'Kevin Tian']"
] |
null | null |
2403.03929
| null | null |
http://arxiv.org/pdf/2403.03929v1
|
2024-03-06T18:39:41Z
|
2024-03-06T18:39:41Z
|
Extreme Precipitation Nowcasting using Transformer-based Generative
Models
|
This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed NowcastingGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events. The code is available at url{https://github.com/Cmeo97/NowcastingGPT}.
|
[
"['Cristian Meo' 'Ankush Roy' 'Mircea Lică' 'Junzhe Yin' 'Zeineb Bou Che'\n 'Yanbo Wang' 'Ruben Imhoff' 'Remko Uijlenhoet' 'Justin Dauwels']"
] |
null | null |
2403.03938
| null | null |
http://arxiv.org/pdf/2403.03938v2
|
2024-05-31T15:31:16Z
|
2024-03-06T18:47:32Z
|
GUIDE: Guidance-based Incremental Learning with Diffusion Models
|
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten. Existing generative strategies combat catastrophic forgetting by randomly sampling rehearsal examples from a generative model. Such an approach contradicts buffer-based approaches where sampling strategy plays an important role. We propose to bridge this gap by incorporating classifier guidance into the diffusion process to produce rehearsal examples specifically targeting information forgotten by a continuously trained model. This approach enables the generation of samples from preceding task distributions, which are more likely to be misclassified in the context of recently encountered classes. Our experimental results show that GUIDE significantly reduces catastrophic forgetting, outperforming conventional random sampling approaches and surpassing recent state-of-the-art methods in continual learning with generative replay.
|
[
"['Bartosz Cywiński' 'Kamil Deja' 'Tomasz Trzciński'\n 'Bartłomiej Twardowski' 'Łukasz Kuciński']"
] |
null | null |
2403.03942
| null | null |
http://arxiv.org/pdf/2403.03942v2
|
2024-06-05T17:44:03Z
|
2024-03-06T18:50:14Z
|
The Heuristic Core: Understanding Subnetwork Generalization in
Pretrained Language Models
|
Prior work has found that pretrained language models (LMs) fine-tuned with different random seeds can achieve similar in-domain performance but generalize differently on tests of syntactic generalization. In this work, we show that, even within a single model, we can find multiple subnetworks that perform similarly in-domain, but generalize vastly differently. To better understand these phenomena, we investigate if they can be understood in terms of "competing subnetworks": the model initially represents a variety of distinct algorithms, corresponding to different subnetworks, and generalization occurs when it ultimately converges to one. This explanation has been used to account for generalization in simple algorithmic tasks ("grokking"). Instead of finding competing subnetworks, we find that all subnetworks -- whether they generalize or not -- share a set of attention heads, which we refer to as the heuristic core. Further analysis suggests that these attention heads emerge early in training and compute shallow, non-generalizing features. The model learns to generalize by incorporating additional attention heads, which depend on the outputs of the "heuristic" heads to compute higher-level features. Overall, our results offer a more detailed picture of the mechanisms for syntactic generalization in pretrained LMs.
|
[
"['Adithya Bhaskar' 'Dan Friedman' 'Danqi Chen']"
] |
null | null |
2403.03945
| null | null |
http://arxiv.org/pdf/2403.03945v2
|
2024-06-03T09:55:44Z
|
2024-03-06T18:52:39Z
|
SPEAR:Exact Gradient Inversion of Batches in Federated Learning
|
Federated learning is a framework for collaborative machine learning where clients only share gradient updates and not their private data with a server. However, it was recently shown that gradient inversion attacks can reconstruct this data from the shared gradients. In the important honest-but-curious setting, existing attacks enable exact reconstruction only for a batch size of $b=1$, with larger batches permitting only approximate reconstruction. In this work, we propose SPEAR, the first algorithm reconstructing whole batches with $b >1$ exactly. SPEAR combines insights into the explicit low-rank structure of gradients with a sampling-based algorithm. Crucially, we leverage ReLU-induced gradient sparsity to precisely filter out large numbers of incorrect samples, making a final reconstruction step tractable. We provide an efficient GPU implementation for fully connected networks and show that it recovers high-dimensional ImageNet inputs in batches of up to $b lesssim 25$ exactly while scaling to large networks. Finally, we show theoretically that much larger batches can be reconstructed with high probability given exponential time.
|
[
"['Dimitar I. Dimitrov' 'Maximilian Baader' 'Mark Niklas Müller'\n 'Martin Vechev']"
] |
null | null |
2403.03949
| null | null |
http://arxiv.org/pdf/2403.03949v1
|
2024-03-06T18:55:36Z
|
2024-03-06T18:55:36Z
|
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach
for Robust Manipulation
|
Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement learning, on the other hand, can explore the environment autonomously to learn robust behaviors but may require impractical amounts of unsafe real-world data collection. To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly from small amounts of real-world data. To enable this real-to-sim-to-real pipeline, RialTo proposes an easy-to-use interface for quickly scanning and constructing digital twins of real-world environments. We also introduce a novel "inverse distillation" procedure for bringing real-world demonstrations into simulated environments for efficient fine-tuning, with minimal human intervention and engineering required. We evaluate RialTo across a variety of robotic manipulation problems in the real world, such as robustly stacking dishes on a rack, placing books on a shelf, and six other tasks. RialTo increases (over 67%) in policy robustness without requiring extensive human data collection. Project website and videos at https://real-to-sim-to-real.github.io/RialTo/
|
[
"['Marcel Torne' 'Anthony Simeonov' 'Zechu Li' 'April Chan' 'Tao Chen'\n 'Abhishek Gupta' 'Pulkit Agrawal']"
] |
null | null |
2403.03950
| null | null |
http://arxiv.org/pdf/2403.03950v1
|
2024-03-06T18:55:47Z
|
2024-03-06T18:55:47Z
|
Stop Regressing: Training Value Functions via Classification for
Scalable Deep RL
|
Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost.
|
[
"['Jesse Farebrother' 'Jordi Orbay' 'Quan Vuong' 'Adrien Ali Taïga'\n 'Yevgen Chebotar' 'Ted Xiao' 'Alex Irpan' 'Sergey Levine'\n 'Pablo Samuel Castro' 'Aleksandra Faust' 'Aviral Kumar' 'Rishabh Agarwal']"
] |
null | null |
2403.03954
| null | null |
http://arxiv.org/pdf/2403.03954v6
|
2024-06-08T06:17:48Z
|
2024-03-06T18:58:49Z
|
3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple
3D Representations
|
Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact 3D visual representation, extracted from sparse point clouds with an efficient point encoder. In our experiments involving 72 simulation tasks, DP3 successfully handles most tasks with just 10 demonstrations and surpasses baselines with a 24.2% relative improvement. In 4 real robot tasks, DP3 demonstrates precise control with a high success rate of 85%, given only 40 demonstrations of each task, and shows excellent generalization abilities in diverse aspects, including space, viewpoint, appearance, and instance. Interestingly, in real robot experiments, DP3 rarely violates safety requirements, in contrast to baseline methods which frequently do, necessitating human intervention. Our extensive evaluation highlights the critical importance of 3D representations in real-world robot learning. Videos, code, and data are available on https://3d-diffusion-policy.github.io .
|
[
"['Yanjie Ze' 'Gu Zhang' 'Kangning Zhang' 'Chenyuan Hu' 'Muhan Wang'\n 'Huazhe Xu']"
] |
null | null |
2403.03960
| null | null |
http://arxiv.org/pdf/2403.03960v1
|
2024-02-29T00:48:17Z
|
2024-02-29T00:48:17Z
|
Assessing the Extrapolation Capability of Template-Free Retrosynthesis
Models
|
Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted. In this study, we empirically assess the extrapolation capability of state-of-the-art template-free models by meticulously assembling an extensive set of out-of-distribution (OOD) reactions. Our findings demonstrate that while template-free models exhibit potential in predicting precursors with novel synthesis rules, their top-10 exact-match accuracy in OOD reactions is strikingly modest (< 1%). Furthermore, despite the capability of generating novel reactions, our investigation highlights a recurring issue where more than half of the novel reactions predicted by template-free models are chemically implausible. Consequently, we advocate for the future development of template-free models that integrate considerations of chemical feasibility when navigating unexplored regions of reaction space.
|
[
"['Shuan Chen' 'Yousung Jung']"
] |
null | null |
2403.03967
| null | null |
http://arxiv.org/pdf/2403.03967v2
|
2024-03-23T11:22:00Z
|
2024-03-06T15:41:21Z
|
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
|
The existence of adversarial attacks on machine learning models imperceptible to a human is still quite a mystery from a theoretical perspective. In this work, we introduce two notions of adversarial attacks: natural or on-manifold attacks, which are perceptible by a human/oracle, and unnatural or off-manifold attacks, which are not. We argue that the existence of the off-manifold attacks is a natural consequence of the dimension gap between the intrinsic and ambient dimensions of the data. For 2-layer ReLU networks, we prove that even though the dimension gap does not affect generalization performance on samples drawn from the observed data space, it makes the clean-trained model more vulnerable to adversarial perturbations in the off-manifold direction of the data space. Our main results provide an explicit relationship between the $ell_2,ell_{infty}$ attack strength of the on/off-manifold attack and the dimension gap.
|
[
"['Rajdeep Haldar' 'Yue Xing' 'Qifan Song']"
] |
null | null |
2403.03994
| null | null |
http://arxiv.org/abs/2403.03994v1
|
2024-03-06T19:08:34Z
|
2024-03-06T19:08:34Z
|
Video Relationship Detection Using Mixture of Experts
|
Machine comprehension of visual information from images and videos by neural networks faces two primary challenges. Firstly, there exists a computational and inference gap in connecting vision and language, making it difficult to accurately determine which object a given agent acts on and represent it through language. Secondly, classifiers trained by a single, monolithic neural network often lack stability and generalization. To overcome these challenges, we introduce MoE-VRD, a novel approach to visual relationship detection utilizing a mixture of experts. MoE-VRD identifies language triplets in the form of < subject, predicate, object> tuples to extract relationships from visual processing. Leveraging recent advancements in visual relationship detection, MoE-VRD addresses the requirement for action recognition in establishing relationships between subjects (acting) and objects (being acted upon). In contrast to single monolithic networks, MoE-VRD employs multiple small models as experts, whose outputs are aggregated. Each expert in MoE-VRD specializes in visual relationship learning and object tagging. By utilizing a sparsely-gated mixture of experts, MoE-VRD enables conditional computation and significantly enhances neural network capacity without increasing computational complexity. Our experimental results demonstrate that the conditional computation capabilities and scalability of the mixture-of-experts approach lead to superior performance in visual relationship detection compared to state-of-the-art methods.
|
[
"['Ala Shaabana' 'Zahra Gharaee' 'Paul Fieguth']"
] |
null | null |
2403.04001
| null | null |
http://arxiv.org/pdf/2403.04001v1
|
2024-03-06T19:17:49Z
|
2024-03-06T19:17:49Z
|
Bidirectional Progressive Neural Networks with Episodic Return Progress
for Emergent Task Sequencing and Robotic Skill Transfer
|
Human brain and behavior provide a rich venue that can inspire novel control and learning methods for robotics. In an attempt to exemplify such a development by inspiring how humans acquire knowledge and transfer skills among tasks, we introduce a novel multi-task reinforcement learning framework named Episodic Return Progress with Bidirectional Progressive Neural Networks (ERP-BPNN). The proposed ERP-BPNN model (1) learns in a human-like interleaved manner by (2) autonomous task switching based on a novel intrinsic motivation signal and, in contrast to existing methods, (3) allows bidirectional skill transfer among tasks. ERP-BPNN is a general architecture applicable to several multi-task learning settings; in this paper, we present the details of its neural architecture and show its ability to enable effective learning and skill transfer among morphologically different robots in a reaching task. The developed Bidirectional Progressive Neural Network (BPNN) architecture enables bidirectional skill transfer without requiring incremental training and seamlessly integrates with online task arbitration. The task arbitration mechanism developed is based on soft Episodic Return progress (ERP), a novel intrinsic motivation (IM) signal. To evaluate our method, we use quantifiable robotics metrics such as 'expected distance to goal' and 'path straightness' in addition to the usual reward-based measure of episodic return common in reinforcement learning. With simulation experiments, we show that ERP-BPNN achieves faster cumulative convergence and improves performance in all metrics considered among morphologically different robots compared to the baselines.
|
[
"['Suzan Ece Ada' 'Hanne Say' 'Emre Ugur' 'Erhan Oztop']"
] |
null | null |
2403.04005
| null | null |
http://arxiv.org/pdf/2403.04005v1
|
2024-03-06T19:29:08Z
|
2024-03-06T19:29:08Z
|
On the Efficient Marginalization of Probabilistic Sequence Models
|
Real-world data often exhibits sequential dependence, across diverse domains such as human behavior, medicine, finance, and climate modeling. Probabilistic methods capture the inherent uncertainty associated with prediction in these contexts, with autoregressive models being especially prominent. This dissertation focuses on using autoregressive models to answer complex probabilistic queries that go beyond single-step prediction, such as the timing of future events or the likelihood of a specific event occurring before another. In particular, we develop a broad class of novel and efficient approximation techniques for marginalization in sequential models that are model-agnostic. These techniques rely solely on access to and sampling from next-step conditional distributions of a pre-trained autoregressive model, including both traditional parametric models as well as more recent neural autoregressive models. Specific approaches are presented for discrete sequential models, for marked temporal point processes, and for stochastic jump processes, each tailored to a well-defined class of informative, long-range probabilistic queries.
|
[
"['Alex Boyd']"
] |
null | null |
2403.04007
| null | null |
http://arxiv.org/pdf/2403.04007v1
|
2024-03-06T19:39:20Z
|
2024-03-06T19:39:20Z
|
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical
Systems
|
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advances at the intersection of control and RL follow a two-stage, safety filter approach to enforcing hard safety constraints: model-free RL is used to learn a potentially unsafe controller, whose actions are projected onto safe sets prescribed, for example, by a control barrier function. Though safe, such approaches lose any convergence guarantees enjoyed by the underlying RL methods. In this paper, we develop a single-stage, sampling-based approach to hard constraint satisfaction that learns RL controllers enjoying classical convergence guarantees while satisfying hard safety constraints throughout training and deployment. We validate the efficacy of our approach in simulation, including safe control of a quadcopter in a challenging obstacle avoidance problem, and demonstrate that it outperforms existing benchmarks.
|
[
"['Wesley A. Suttle' 'Vipul K. Sharma' 'Krishna C. Kosaraju'\n 'S. Sivaranjani' 'Ji Liu' 'Vijay Gupta' 'Brian M. Sadler']"
] |
null | null |
2403.04010
| null | null |
http://arxiv.org/pdf/2403.04010v1
|
2024-03-06T19:42:34Z
|
2024-03-06T19:42:34Z
|
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message
Passing and Hyperbolic Neural Networks
|
Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks. Despite advancements of methodology based on deep learning in recent years, existing benchmarking approaches exhibit limitations that hinder a comprehensive comparison. In this paper, we revisit datasets and approaches for unsupervised node-level graph anomaly detection tasks from three aspects. Firstly, we introduce outlier injection methods that create more diverse and graph-based anomalies in graph datasets. Secondly, we compare methods employing message passing against those without, uncovering the unexpected decline in performance associated with message passing. Thirdly, we explore the use of hyperbolic neural networks, specifying crucial architecture and loss design that contribute to enhanced performance. Through rigorous experiments and evaluations, our study sheds light on general strategies for improving node-level graph anomaly detection methods.
|
[
"['Jing Gu' 'Dongmian Zou']"
] |
null | null |
2403.04012
| null | null |
http://arxiv.org/pdf/2403.04012v2
|
2024-04-01T20:26:01Z
|
2024-03-06T19:46:44Z
|
Temporal Cross-Attention for Dynamic Embedding and Tokenization of
Multimodal Electronic Health Records
|
The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning. However, learning useful representations of EHR data is challenging due to its high dimensionality, sparsity, multimodality, irregular and variable-specific recording frequency, and timestamp duplication when multiple measurements are recorded simultaneously. Although recent efforts to fuse structured EHR and unstructured clinical notes suggest the potential for more accurate prediction of clinical outcomes, less focus has been placed on EHR embedding approaches that directly address temporal EHR challenges by learning time-aware representations from multimodal patient time series. In this paper, we introduce a dynamic embedding and tokenization framework for precise representation of multimodal clinical time series that combines novel methods for encoding time and sequential position with temporal cross-attention. Our embedding and tokenization framework, when integrated into a multitask transformer classifier with sliding window attention, outperformed baseline approaches on the exemplar task of predicting the occurrence of nine postoperative complications of more than 120,000 major inpatient surgeries using multimodal data from three hospitals and two academic health centers in the United States.
|
[
"['Yingbo Ma' 'Suraj Kolla' 'Dhruv Kaliraman' 'Victoria Nolan'\n 'Zhenhong Hu' 'Ziyuan Guan' 'Yuanfang Ren' 'Brooke Armfield'\n 'Tezcan Ozrazgat-Baslanti' 'Tyler J. Loftus' 'Parisa Rashidi'\n 'Azra Bihorac' 'Benjamin Shickel']"
] |
null | null |
2403.04015
| null | null |
http://arxiv.org/pdf/2403.04015v1
|
2024-03-06T19:58:19Z
|
2024-03-06T19:58:19Z
|
Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced
Agent
|
Feature selection prepares the AI-readiness of data by eliminating redundant features. Prior research falls into two primary categories: i) Supervised Feature Selection, which identifies the optimal feature subset based on their relevance to the target variable; ii) Unsupervised Feature Selection, which reduces the feature space dimensionality by capturing the essential information within the feature set instead of using target variable. However, SFS approaches suffer from time-consuming processes and limited generalizability due to the dependence on the target variable and downstream ML tasks. UFS methods are constrained by the deducted feature space is latent and untraceable. To address these challenges, we introduce an innovative framework for feature selection, which is guided by knockoff features and optimized through reinforcement learning, to identify the optimal and effective feature subset. In detail, our method involves generating "knockoff" features that replicate the distribution and characteristics of the original features but are independent of the target variable. Each feature is then assigned a pseudo label based on its correlation with all the knockoff features, serving as a novel metric for feature evaluation. Our approach utilizes these pseudo labels to guide the feature selection process in 3 novel ways, optimized by a single reinforced agent: 1). A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process in feature selection. 2). We introduce unsupervised rewards to evaluate the feature subset quality based on the pseudo labels and the feature space reconstruction loss to reduce dependencies on the target variable. 3). A new {epsilon}-greedy strategy is used, incorporating insights from the pseudo labels to make the feature selection process more effective.
|
[
"['Xinyuan Wang' 'Dongjie Wang' 'Wangyang Ying' 'Rui Xie' 'Haifeng Chen'\n 'Yanjie Fu']"
] |
null | null |
2403.04017
| null | null |
http://arxiv.org/pdf/2403.04017v1
|
2024-03-06T19:59:17Z
|
2024-03-06T19:59:17Z
|
Learning Guided Automated Reasoning: A Brief Survey
|
Automated theorem provers and formal proof assistants are general reasoning systems that are in theory capable of proving arbitrarily hard theorems, thus solving arbitrary problems reducible to mathematics and logical reasoning. In practice, such systems however face large combinatorial explosion, and therefore include many heuristics and choice points that considerably influence their performance. This is an opportunity for trained machine learning predictors, which can guide the work of such reasoning systems. Conversely, deductive search supported by the notion of logically valid proof allows one to train machine learning systems on large reasoning corpora. Such bodies of proof are usually correct by construction and when combined with more and more precise trained guidance they can be boostrapped into very large corpora, with increasingly long reasoning chains and possibly novel proof ideas. In this paper we provide an overview of several automated reasoning and theorem proving domains and the learning and AI methods that have been so far developed for them. These include premise selection, proof guidance in several settings, AI systems and feedback loops iterating between reasoning and learning, and symbolic classification problems.
|
[
"['Lasse Blaauwbroek' 'David Cerna' 'Thibault Gauthier' 'Jan Jakubův'\n 'Cezary Kaliszyk' 'Martin Suda' 'Josef Urban']"
] |
null | null |
2403.04033
| null | null |
http://arxiv.org/pdf/2403.04033v1
|
2024-03-06T20:23:59Z
|
2024-03-06T20:23:59Z
|
Online Learning with Unknown Constraints
|
We consider the problem of online learning where the sequence of actions played by the learner must adhere to an unknown safety constraint at every round. The goal is to minimize regret with respect to the best safe action in hindsight while simultaneously satisfying the safety constraint with high probability on each round. We provide a general meta-algorithm that leverages an online regression oracle to estimate the unknown safety constraint, and converts the predictions of an online learning oracle to predictions that adhere to the unknown safety constraint. On the theoretical side, our algorithm's regret can be bounded by the regret of the online regression and online learning oracles, the eluder dimension of the model class containing the unknown safety constraint, and a novel complexity measure that captures the difficulty of safe learning. We complement our result with an asymptotic lower bound that shows that the aforementioned complexity measure is necessary. When the constraints are linear, we instantiate our result to provide a concrete algorithm with $sqrt{T}$ regret using a scaling transformation that balances optimistic exploration with pessimistic constraint satisfaction.
|
[
"['Karthik Sridharan' 'Seung Won Wilson Yoo']"
] |
null | null |
2403.04036
| null | null |
http://arxiv.org/pdf/2403.04036v1
|
2024-03-06T20:33:55Z
|
2024-03-06T20:33:55Z
|
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting
Under Time-Domain Shift
|
Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem. Contrastive learning, a state-of-the-art self-supervised learning approach from deep learning, learns a distance metric such that positive pairs are closer (i.e. more similar) in the learned metric space than negative pairs. When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs. Through experiments on wireless and wired RF datasets collected over several days, we demonstrate that our contrastive learning approach captures domain-invariant features, diminishing the effects of domain-specific variations. Our results show large and consistent improvements in accuracy (10.8% to 27.8%) over baseline models, thus underscoring the effectiveness of contrastive learning in improving device classification under domain shift.
|
[
"['Jun Chen' 'Weng-Keen Wong' 'Bechir Hamdaoui']"
] |
null | null |
2403.04037
| null | null |
http://arxiv.org/pdf/2403.04037v1
|
2024-03-06T20:34:08Z
|
2024-03-06T20:34:08Z
|
OCD-FL: A Novel Communication-Efficient Peer Selection-based
Decentralized Federated Learning
|
The conjunction of edge intelligence and the ever-growing Internet-of-Things (IoT) network heralds a new era of collaborative machine learning, with federated learning (FL) emerging as the most prominent paradigm. With the growing interest in these learning schemes, researchers started addressing some of their most fundamental limitations. Indeed, conventional FL with a central aggregator presents a single point of failure and a network bottleneck. To bypass this issue, decentralized FL where nodes collaborate in a peer-to-peer network has been proposed. Despite the latter's efficiency, communication costs and data heterogeneity remain key challenges in decentralized FL. In this context, we propose a novel scheme, called opportunistic communication-efficient decentralized federated learning, a.k.a., OCD-FL, consisting of a systematic FL peer selection for collaboration, aiming to achieve maximum FL knowledge gain while reducing energy consumption. Experimental results demonstrate the capability of OCD-FL to achieve similar or better performances than the fully collaborative FL, while significantly reducing consumed energy by at least 30% and up to 80%.
|
[
"['Nizar Masmoudi' 'Wael Jaafar']"
] |
null | null |
2403.04039
| null | null |
http://arxiv.org/pdf/2403.04039v1
|
2024-03-06T20:37:29Z
|
2024-03-06T20:37:29Z
|
Sample size planning for conditional counterfactual mean estimation with
a K-armed randomized experiment
|
We cover how to determine a sufficiently large sample size for a $K$-armed randomized experiment in order to estimate conditional counterfactual expectations in data-driven subgroups. The sub-groups can be output by any feature space partitioning algorithm, including as defined by binning users having similar predictive scores or as defined by a learned policy tree. After carefully specifying the inference target, a minimum confidence level, and a maximum margin of error, the key is to turn the original goal into a simultaneous inference problem where the recommended sample size to offset an increased possibility of estimation error is directly related to the number of inferences to be conducted. Given a fixed sample size budget, our result allows us to invert the question to one about the feasible number of treatment arms or partition complexity (e.g. number of decision tree leaves). Using policy trees to learn sub-groups, we evaluate our nominal guarantees on a large publicly-available randomized experiment test data set.
|
[
"['Gabriel Ruiz']"
] |
null | null |
2403.04050
| null | null |
http://arxiv.org/pdf/2403.04050v1
|
2024-03-06T20:52:49Z
|
2024-03-06T20:52:49Z
|
Belief-Enriched Pessimistic Q-Learning against Adversarial State
Perturbations
|
Reinforcement learning (RL) has achieved phenomenal success in various domains. However, its data-driven nature also introduces new vulnerabilities that can be exploited by malicious opponents. Recent work shows that a well-trained RL agent can be easily manipulated by strategically perturbing its state observations at the test stage. Existing solutions either introduce a regularization term to improve the smoothness of the trained policy against perturbations or alternatively train the agent's policy and the attacker's policy. However, the former does not provide sufficient protection against strong attacks, while the latter is computationally prohibitive for large environments. In this work, we propose a new robust RL algorithm for deriving a pessimistic policy to safeguard against an agent's uncertainty about true states. This approach is further enhanced with belief state inference and diffusion-based state purification to reduce uncertainty. Empirical results show that our approach obtains superb performance under strong attacks and has a comparable training overhead with regularization-based methods. Our code is available at https://github.com/SliencerX/Belief-enriched-robust-Q-learning.
|
[
"['Xiaolin Sun' 'Zizhan Zheng']"
] |
null | null |
2403.04070
| null | null |
http://arxiv.org/pdf/2403.04070v1
|
2024-03-06T21:50:52Z
|
2024-03-06T21:50:52Z
|
Improving Adversarial Training using Vulnerability-Aware Perturbation
Budget
|
Adversarial Training (AT) effectively improves the robustness of Deep Neural Networks (DNNs) to adversarial attacks. Generally, AT involves training DNN models with adversarial examples obtained within a pre-defined, fixed perturbation bound. Notably, individual natural examples from which these adversarial examples are crafted exhibit varying degrees of intrinsic vulnerabilities, and as such, crafting adversarial examples with fixed perturbation radius for all instances may not sufficiently unleash the potency of AT. Motivated by this observation, we propose two simple, computationally cheap vulnerability-aware reweighting functions for assigning perturbation bounds to adversarial examples used for AT, named Margin-Weighted Perturbation Budget (MWPB) and Standard-Deviation-Weighted Perturbation Budget (SDWPB). The proposed methods assign perturbation radii to individual adversarial samples based on the vulnerability of their corresponding natural examples. Experimental results show that the proposed methods yield genuine improvements in the robustness of AT algorithms against various adversarial attacks.
|
[
"['Olukorede Fakorede' 'Modeste Atsague' 'Jin Tian']"
] |
null | null |
2403.04072
| null | null |
http://arxiv.org/pdf/2403.04072v1
|
2024-03-06T22:06:21Z
|
2024-03-06T22:06:21Z
|
Forecasting and Mitigating Disruptions in Public Bus Transit Services
|
Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of Nashville, TN, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency: by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.
|
[
"['Chaeeun Han' 'Jose Paolo Talusan' 'Dan Freudberg' 'Ayan Mukhopadhyay'\n 'Abhishek Dubey' 'Aron Laszka']"
] |
null | null |
2403.04081
| null | null |
http://arxiv.org/pdf/2403.04081v1
|
2024-03-06T22:24:05Z
|
2024-03-06T22:24:05Z
|
Directional Smoothness and Gradient Methods: Convergence and Adaptivity
|
We develop new sub-optimality bounds for gradient descent (GD) that depend on the conditioning of the objective along the path of optimization, rather than on global, worst-case constants. Key to our proofs is directional smoothness, a measure of gradient variation that we use to develop upper-bounds on the objective. Minimizing these upper-bounds requires solving implicit equations to obtain a sequence of strongly adapted step-sizes; we show that these equations are straightforward to solve for convex quadratics and lead to new guarantees for two classical step-sizes. For general functions, we prove that the Polyak step-size and normalized GD obtain fast, path-dependent rates despite using no knowledge of the directional smoothness. Experiments on logistic regression show our convergence guarantees are tighter than the classical theory based on L-smoothness.
|
[
"['Aaron Mishkin' 'Ahmed Khaled' 'Yuanhao Wang' 'Aaron Defazio'\n 'Robert M. Gower']"
] |
null | null |
2403.04082
| null | null |
http://arxiv.org/pdf/2403.04082v2
|
2024-06-18T16:40:32Z
|
2024-03-06T22:27:30Z
|
Inference via Interpolation: Contrastive Representations Provably Enable
Planning and Inference
|
Given time series data, how can we answer questions like "what will happen in the future?" and "how did we get here?" These sorts of probabilistic inference questions are challenging when observations are high-dimensional. In this paper, we show how these questions can have compact, closed form solutions in terms of learned representations. The key idea is to apply a variant of contrastive learning to time series data. Prior work already shows that the representations learned by contrastive learning encode a probability ratio. By extending prior work to show that the marginal distribution over representations is Gaussian, we can then prove that joint distribution of representations is also Gaussian. Taken together, these results show that representations learned via temporal contrastive learning follow a Gauss-Markov chain, a graphical model where inference (e.g., prediction, planning) over representations corresponds to inverting a low-dimensional matrix. In one special case, inferring intermediate representations will be equivalent to interpolating between the learned representations. We validate our theory using numerical simulations on tasks up to 46-dimensions.
|
[
"['Benjamin Eysenbach' 'Vivek Myers' 'Ruslan Salakhutdinov' 'Sergey Levine']"
] |
null | null |
2403.04086
| null | null |
http://arxiv.org/pdf/2403.04086v2
|
2024-05-30T05:44:00Z
|
2024-03-06T22:32:48Z
|
Automated Multi-Task Learning for Joint Disease Prediction on Electronic
Health Records
|
In the realm of big data and digital healthcare, Electronic Health Records (EHR) have become a rich source of information with the potential to improve patient care and medical research. In recent years, machine learning models have proliferated for analyzing EHR data to predict patients future health conditions. Among them, some studies advocate for multi-task learning (MTL) to jointly predict multiple target diseases for improving the prediction performance over single task learning. Nevertheless, current MTL frameworks for EHR data have significant limitations due to their heavy reliance on human experts to identify task groups for joint training and design model architectures. To reduce human intervention and improve the framework design, we propose an automated approach named AutoDP, which can search for the optimal configuration of task grouping and architectures simultaneously. To tackle the vast joint search space encompassing task combinations and architectures, we employ surrogate model-based optimization, enabling us to efficiently discover the optimal solution. Experimental results on real-world EHR data demonstrate the efficacy of the proposed AutoDP framework. It achieves significant performance improvements over both hand-crafted and automated state-of-the-art methods, also maintains a feasible search cost at the same time.
|
[
"['Suhan Cui' 'Prasenjit Mitra']"
] |
null | null |
2403.04099
| null | null |
http://arxiv.org/pdf/2403.04099v1
|
2024-03-06T23:03:12Z
|
2024-03-06T23:03:12Z
|
Many-Objective Multi-Solution Transport
|
Optimizing the performance of many objectives (instantiated by tasks or clients) jointly with a few Pareto stationary solutions (models) is critical in machine learning. However, previous multi-objective optimization methods often focus on a few number of objectives and cannot scale to many objectives that outnumber the solutions, leading to either subpar performance or ignored objectives. We introduce Many-objective multi-solution Transport (MosT), a framework that finds multiple diverse solutions in the Pareto front of many objectives. Our insight is to seek multiple solutions, each performing as a domain expert and focusing on a specific subset of objectives while collectively covering all of them. MosT formulates the problem as a bi-level optimization of weighted objectives for each solution, where the weights are defined by an optimal transport between the objectives and solutions. Our algorithm ensures convergence to Pareto stationary solutions for complementary subsets of objectives. On a range of applications in federated learning, multi-task learning, and mixture-of-prompt learning for LLMs, MosT distinctly outperforms strong baselines, delivering high-quality, diverse solutions that profile the entire Pareto frontier, thus ensuring balanced trade-offs across many objectives.
|
[
"['Ziyue Li' 'Tian Li' 'Virginia Smith' 'Jeff Bilmes' 'Tianyi Zhou']"
] |
null | null |
2403.04109
| null | null |
http://arxiv.org/pdf/2403.04109v1
|
2024-03-06T23:43:51Z
|
2024-03-06T23:43:51Z
|
Using Causal Trees to Estimate Personalized Task Difficulty in
Post-Stroke Individuals
|
Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individual's performance. We show that this technique explains the variance in user performance for a reaching task better than previous approaches to estimating task difficulty.
|
[
"['Nathaniel Dennler' 'Stefanos Nikolaidis' 'Maja Matarić']"
] |
null | null |
2403.04114
| null | null |
http://arxiv.org/pdf/2403.04114v1
|
2024-03-07T00:00:02Z
|
2024-03-07T00:00:02Z
|
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs
|
Deep learning methods for perception are the cornerstone of many robotic systems. Despite their potential for impressive performance, obtaining real-world training data is expensive, and can be impractically difficult for some tasks. Sim-to-real transfer with domain randomization offers a potential workaround, but often requires extensive manual tuning and results in models that are brittle to distribution shift between sim and real. In this work, we introduce Composable Object Volume NeRF (COV-NeRF), an object-composable NeRF model that is the centerpiece of a real-to-sim pipeline for synthesizing training data targeted to scenes and objects from the real world. COV-NeRF extracts objects from real images and composes them into new scenes, generating photorealistic renderings and many types of 2D and 3D supervision, including depth maps, segmentation masks, and meshes. We show that COV-NeRF matches the rendering quality of modern NeRF methods, and can be used to rapidly close the sim-to-real gap across a variety of perceptual modalities.
|
[
"['Nikhil Mishra' 'Maximilian Sieb' 'Pieter Abbeel' 'Xi Chen']"
] |
null | null |
2403.04118
| null | null |
http://arxiv.org/pdf/2403.04118v1
|
2024-03-07T00:20:11Z
|
2024-03-07T00:20:11Z
|
Globally Stable Neural Imitation Policies
|
Imitation learning presents an effective approach to alleviate the resource-intensive and time-consuming nature of policy learning from scratch in the solution space. Even though the resulting policy can mimic expert demonstrations reliably, it often lacks predictability in unexplored regions of the state-space, giving rise to significant safety concerns in the face of perturbations. To address these challenges, we introduce the Stable Neural Dynamical System (SNDS), an imitation learning regime which produces a policy with formal stability guarantees. We deploy a neural policy architecture that facilitates the representation of stability based on Lyapunov theorem, and jointly train the policy and its corresponding Lyapunov candidate to ensure global stability. We validate our approach by conducting extensive experiments in simulation and successfully deploying the trained policies on a real-world manipulator arm. The experimental results demonstrate that our method overcomes the instability, accuracy, and computational intensity problems associated with previous imitation learning methods, making our method a promising solution for stable policy learning in complex planning scenarios.
|
[
"['Amin Abyaneh' 'Mariana Sosa Guzmán' 'Hsiu-Chin Lin']"
] |
null | null |
2403.04121
| null | null |
http://arxiv.org/abs/2403.04121v2
|
2024-03-08T19:51:14Z
|
2024-03-07T00:36:32Z
|
Can Large Language Models Reason and Plan?
|
While humans sometimes do show the capability of correcting their own erroneous guesses with self-critiquing, there seems to be no basis for that assumption in the case of LLMs.
|
[
"['Subbarao Kambhampati']"
] |
null | null |
2403.04123
| null | null |
http://arxiv.org/pdf/2403.04123v1
|
2024-03-07T00:44:01Z
|
2024-03-07T00:44:01Z
|
Exploring LLM-based Agents for Root Cause Analysis
|
The growing complexity of cloud based software systems has resulted in incident management becoming an integral part of the software development lifecycle. Root cause analysis (RCA), a critical part of the incident management process, is a demanding task for on-call engineers, requiring deep domain knowledge and extensive experience with a team's specific services. Automation of RCA can result in significant savings of time, and ease the burden of incident management on on-call engineers. Recently, researchers have utilized Large Language Models (LLMs) to perform RCA, and have demonstrated promising results. However, these approaches are not able to dynamically collect additional diagnostic information such as incident related logs, metrics or databases, severely restricting their ability to diagnose root causes. In this work, we explore the use of LLM based agents for RCA to address this limitation. We present a thorough empirical evaluation of a ReAct agent equipped with retrieval tools, on an out-of-distribution dataset of production incidents collected at Microsoft. Results show that ReAct performs competitively with strong retrieval and reasoning baselines, but with highly increased factual accuracy. We then extend this evaluation by incorporating discussions associated with incident reports as additional inputs for the models, which surprisingly does not yield significant performance improvements. Lastly, we conduct a case study with a team at Microsoft to equip the ReAct agent with tools that give it access to external diagnostic services that are used by the team for manual RCA. Our results show how agents can overcome the limitations of prior work, and practical considerations for implementing such a system in practice.
|
[
"['Devjeet Roy' 'Xuchao Zhang' 'Rashi Bhave' 'Chetan Bansal'\n 'Pedro Las-Casas' 'Rodrigo Fonseca' 'Saravan Rajmohan']"
] |
null | null |
2403.04144
| null | null |
http://arxiv.org/pdf/2403.04144v1
|
2024-03-07T01:50:36Z
|
2024-03-07T01:50:36Z
|
FedClust: Optimizing Federated Learning on Non-IID Data through
Weight-Driven Client Clustering
|
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions. However, existing CFL approaches require a large number of communication rounds for stable cluster formation and rely on a predefined number of clusters, thus limiting their flexibility and adaptability. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions. FedClust groups clients into clusters in a one-shot manner using strategically selected partial model weights and dynamically accommodates newcomers in real-time. Experimental results demonstrate FedClust outperforms baseline approaches in terms of accuracy and communication costs.
|
[
"['Md Sirajul Islam' 'Simin Javaherian' 'Fei Xu' 'Xu Yuan' 'Li Chen'\n 'Nian-Feng Tzeng']"
] |
null | null |
2403.04146
| null | null |
http://arxiv.org/abs/2403.04146v1
|
2024-03-07T01:52:05Z
|
2024-03-07T01:52:05Z
|
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of
Negative Federated Learning
|
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning behavior. However, FL may fail to function appropriately when the federation is not ideal, amid an unhealthy state called Negative Federated Learning (NFL), in which most clients gain no benefit from participating in FL. Many studies have tried to address NFL. However, their solutions either (1) predetermine to prevent NFL in the entire learning life-cycle or (2) tackle NFL in the aftermath of numerous learning rounds. Thus, they either (1) indiscriminately incur extra costs even if FL can perform well without such costs or (2) waste numerous learning rounds. Additionally, none of the previous work takes into account the clients who may be unwilling/unable to follow the proposed NFL solutions when using those solutions to upgrade an FL system in use. This paper introduces FL-GUARD, a holistic framework that can be employed on any FL system for tackling NFL in a run-time paradigm. That is, to dynamically detect NFL at the early stage (tens of rounds) of learning and then to activate recovery measures when necessary. Specifically, we devise a cost-effective NFL detection mechanism, which relies on an estimation of performance gain on clients. Only when NFL is detected, we activate the NFL recovery process, in which each client learns in parallel an adapted model when training the global model. Extensive experiment results confirm the effectiveness of FL-GUARD in detecting NFL and recovering from NFL to a healthy learning state. We also show that FL-GUARD is compatible with previous NFL solutions and robust against clients unwilling/unable to take any recovery measures.
|
[
"['Hong Lin' 'Lidan Shou' 'Ke Chen' 'Gang Chen' 'Sai Wu']"
] |
null | null |
2403.04154
| null | null |
http://arxiv.org/pdf/2403.04154v2
|
2024-06-26T02:28:07Z
|
2024-03-07T02:24:45Z
|
Stabilizing Policy Gradients for Stochastic Differential Equations via
Consistency with Perturbation Process
|
Considering generating samples with high rewards, we focus on optimizing deep neural networks parameterized stochastic differential equations (SDEs), the advanced generative models with high expressiveness, with policy gradient, the leading algorithm in reinforcement learning. Nevertheless, when applying policy gradients to SDEs, since the policy gradient is estimated on a finite set of trajectories, it can be ill-defined, and the policy behavior in data-scarce regions may be uncontrolled. This challenge compromises the stability of policy gradients and negatively impacts sample complexity. To address these issues, we propose constraining the SDE to be consistent with its associated perturbation process. Since the perturbation process covers the entire space and is easy to sample, we can mitigate the aforementioned problems. Our framework offers a general approach allowing for a versatile selection of policy gradient methods to effectively and efficiently train SDEs. We evaluate our algorithm on the task of structure-based drug design and optimize the binding affinity of generated ligand molecules. Our method achieves the best Vina score -9.07 on the CrossDocked2020 dataset.
|
[
"['Xiangxin Zhou' 'Liang Wang' 'Yichi Zhou']"
] |
null | null |
2403.04161
| null | null |
http://arxiv.org/pdf/2403.04161v5
|
2024-06-24T08:18:29Z
|
2024-03-07T02:40:42Z
|
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS
|
Training-free metrics (a.k.a. zero-cost proxies) are widely used to avoid resource-intensive neural network training, especially in Neural Architecture Search (NAS). Recent studies show that existing training-free metrics have several limitations, such as limited correlation and poor generalisation across different search spaces and tasks. Hence, we propose Sample-Wise Activation Patterns and its derivative, SWAP-Score, a novel high-performance training-free metric. It measures the expressivity of networks over a batch of input samples. The SWAP-Score is strongly correlated with ground-truth performance across various search spaces and tasks, outperforming 15 existing training-free metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101. The SWAP-Score can be further enhanced by regularisation, which leads to even higher correlations in cell-based search space and enables model size control during the search. For example, Spearman's rank correlation coefficient between regularised SWAP-Score and CIFAR-100 validation accuracies on NAS-Bench-201 networks is 0.90, significantly higher than 0.80 from the second-best metric, NWOT. When integrated with an evolutionary algorithm for NAS, our SWAP-NAS achieves competitive performance on CIFAR-10 and ImageNet in approximately 6 minutes and 9 minutes of GPU time respectively.
|
[
"['Yameng Peng' 'Andy Song' 'Haytham M. Fayek' 'Vic Ciesielski'\n 'Xiaojun Chang']"
] |
null | null |
2403.04162
| null | null |
http://arxiv.org/pdf/2403.04162v1
|
2024-03-07T02:47:08Z
|
2024-03-07T02:47:08Z
|
Noisy Spiking Actor Network for Exploration
|
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies. Spiking neural networks (SNNs), due to their binary firing mechanism, have strong robustness to noise, making it difficult to realize efficient exploration with local disturbances. To solve this exploration problem, we propose a noisy spiking actor network (NoisySAN) that introduces time-correlated noise during charging and transmission. Moreover, a noise reduction method is proposed to find a stable policy for the agent. Extensive experimental results demonstrate that our method outperforms the state-of-the-art performance on a wide range of continuous control tasks from OpenAI gym.
|
[
"['Ding Chen' 'Peixi Peng' 'Tiejun Huang' 'Yonghong Tian']"
] |
null | null |
2403.04180
| null | null |
http://arxiv.org/pdf/2403.04180v2
|
2024-06-16T15:59:13Z
|
2024-03-07T03:23:13Z
|
RATSF: Empowering Customer Service Volume Management through
Retrieval-Augmented Time-Series Forecasting
|
An efficient customer service management system hinges on precise forecasting of service volume. In this scenario, where data non-stationarity is pronounced, successful forecasting heavily relies on identifying and leveraging similar historical data rather than merely summarizing periodic patterns. Existing models based on RNN or Transformer architectures may struggle with this flexible and effective utilization. To tackle this challenge, we initially developed the Time Series Knowledge Base (TSKB) with an advanced indexing system for efficient historical data retrieval. We also developed the Retrieval Augmented Cross-Attention (RACA) module, a variant of the cross-attention mechanism within Transformer's decoder layers, designed to be seamlessly integrated into the vanilla Transformer architecture to assimilate key historical data segments. The synergy between TSKB and RACA forms the backbone of our Retrieval-Augmented Time Series Forecasting (RATSF) framework. Based on the above two components, RATSF not only significantly enhances performance in the context of Fliggy hotel service volume forecasting but also adapts flexibly to various scenarios and integrates with a multitude of Transformer variants for time-series forecasting. Extensive experimentation has validated the effectiveness and generalizability of this system design across multiple diverse contexts.
|
[
"['Tianfeng Wang' 'Gaojie Cui']"
] |
null | null |
2403.04189
| null | null |
http://arxiv.org/pdf/2403.04189v1
|
2024-03-07T03:38:35Z
|
2024-03-07T03:38:35Z
|
Silicon Photonic 2.5D Interposer Networks for Overcoming Communication
Bottlenecks in Scale-out Machine Learning Hardware Accelerators
|
Modern machine learning (ML) applications are becoming increasingly complex and monolithic (single chip) accelerator architectures cannot keep up with their energy efficiency and throughput demands. Even though modern digital electronic accelerators are gradually adopting 2.5D architectures with multiple smaller chiplets to improve scalability, they face fundamental limitations due to a reliance on slow metallic interconnects. This paper outlines how optical communication and computation can be leveraged in 2.5D platforms to realize energy-efficient and high throughput 2.5D ML accelerator architectures.
|
[
"['Febin Sunny' 'Ebadollah Taheri' 'Mahdi Nikdast' 'Sudeep Pasricha']"
] |
null | null |
2403.04190
| null | null |
http://arxiv.org/pdf/2403.04190v1
|
2024-03-07T03:38:44Z
|
2024-03-07T03:38:44Z
|
Generative AI for Synthetic Data Generation: Methods, Challenges and the
Future
|
The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.
|
[
"['Xu Guo' 'Yiqiang Chen']"
] |
null | null |
2403.04195
| null | null |
http://arxiv.org/pdf/2403.04195v1
|
2024-03-07T03:55:56Z
|
2024-03-07T03:55:56Z
|
Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for
Reservoir Operation Decision and Control
|
Changes in demand, various hydrological inputs, and environmental stressors are among the issues that water managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increases, it becomes more difficult to effectively represent a real-world system using traditional methods such as Dynamic Programming (DP) and Stochastic Dynamic Programming (SDP) for determining the best reservoir operation policy. One of the challenges is the "curse of dimensionality," which means the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with respect to the number of input variables (i.e., dimensionality) of the function. Deep Reinforcement Learning (DRL) is an intelligent approach to overcome the curses of stochastic optimization problems for reservoir operation policy decisions. To our knowledge, this study is the first attempt that examine various novel DRL continuous-action policy gradient methods (PGMs), including Deep Deterministic Policy Gradients (DDPG), Twin Delayed DDPG (TD3), and two different versions of Soft Actor-Critic (SAC18 and SAC19) for optimizing reservoir operation policy. In this study, multiple DRL techniques were implemented in order to find the optimal operation policy of Folsom Reservoir in California, USA. The reservoir system supplies agricultural, municipal, hydropower, and environmental flow demands and flood control operations to the City of Sacramento. Analysis suggests that the TD3 and SAC are robust to meet the Folsom Reservoir's demands and optimize reservoir operation policies.
|
[
"['Sadegh Sadeghi Tabas' 'Vidya Samadi']"
] |
null | null |
2403.04202
| null | null |
http://arxiv.org/pdf/2403.04202v3
|
2024-03-26T17:18:33Z
|
2024-03-07T04:12:24Z
|
Dynamics of Moral Behavior in Heterogeneous Populations of Learning
Agents
|
Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents. A promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents. However, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., caring about maximizing some outcome over time) or norm-based (i.e., focusing on conforming to a specific norm here and now). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using a Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain classes of moral agents are able to steer selfish agents towards more cooperative behavior.
|
[
"['Elizaveta Tennant' 'Stephen Hailes' 'Mirco Musolesi']"
] |
null | null |
2403.04206
| null | null |
http://arxiv.org/pdf/2403.04206v1
|
2024-03-07T04:22:34Z
|
2024-03-07T04:22:34Z
|
GRAWA: Gradient-based Weighted Averaging for Distributed Training of
Deep Learning Models
|
We study distributed training of deep learning models in time-constrained environments. We propose a new algorithm that periodically pulls workers towards the center variable computed as a weighted average of workers, where the weights are inversely proportional to the gradient norms of the workers such that recovering the flat regions in the optimization landscape is prioritized. We develop two asynchronous variants of the proposed algorithm that we call Model-level and Layer-level Gradient-based Weighted Averaging (resp. MGRAWA and LGRAWA), which differ in terms of the weighting scheme that is either done with respect to the entire model or is applied layer-wise. On the theoretical front, we prove the convergence guarantee for the proposed approach in both convex and non-convex settings. We then experimentally demonstrate that our algorithms outperform the competitor methods by achieving faster convergence and recovering better quality and flatter local optima. We also carry out an ablation study to analyze the scalability of the proposed algorithms in more crowded distributed training environments. Finally, we report that our approach requires less frequent communication and fewer distributed updates compared to the state-of-the-art baselines.
|
[
"['Tolga Dimlioglu' 'Anna Choromanska']"
] |
null | null |
2403.04207
| null | null |
http://arxiv.org/pdf/2403.04207v2
|
2024-05-10T09:02:28Z
|
2024-03-07T04:23:07Z
|
HeteroSwitch: Characterizing and Taming System-Induced Data
Heterogeneity in Federated Learning
|
Federated Learning (FL) is a practical approach to train deep learning models collaboratively across user-end devices, protecting user privacy by retaining raw data on-device. In FL, participating user-end devices are highly fragmented in terms of hardware and software configurations. Such fragmentation introduces a new type of data heterogeneity in FL, namely textit{system-induced data heterogeneity}, as each device generates distinct data depending on its hardware and software configurations. In this paper, we first characterize the impact of system-induced data heterogeneity on FL model performance. We collect a dataset using heterogeneous devices with variations across vendors and performance tiers. By using this dataset, we demonstrate that textit{system-induced data heterogeneity} negatively impacts accuracy, and deteriorates fairness and domain generalization problems in FL. To address these challenges, we propose HeteroSwitch, which adaptively adopts generalization techniques (i.e., ISP transformation and SWAD) depending on the level of bias caused by varying HW and SW configurations. In our evaluation with a realistic FL dataset (FLAIR), HeteroSwitch reduces the variance of averaged precision by 6.3% across device types.
|
[
"['Gyudong Kim' 'Mehdi Ghasemi' 'Soroush Heidari' 'Seungryong Kim'\n 'Young Geun Kim' 'Sarma Vrudhula' 'Carole-Jean Wu']"
] |
null | null |
2403.04221
| null | null |
http://arxiv.org/pdf/2403.04221v2
|
2024-07-10T23:51:52Z
|
2024-03-07T04:49:48Z
|
Why Online Reinforcement Learning is Causal
|
Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while the goal of reinforcement learning is to select interventions that maximize the rewards the agent receives from the environment. Reinforcement learning includes the two most powerful sources of information for estimating causal relationships: temporal ordering and the ability to act on an environment. This paper examines which reinforcement learning settings we can expect to benefit from causal modelling, and how. In online learning, the agent has the ability to interact directly with their environment, and learn from exploring it. Our main argument is that in online learning, conditional probabilities are causal, and therefore offline RL is the setting where causal learning has the most potential to make a difference. Essentially, the reason is that when an agent learns from their {em own} experience, there are no unobserved confounders that influence both the agent's own exploratory actions and the rewards they receive. Our paper formalizes this argument. For offline RL, where an agent may and typically does learn from the experience of {em others}, we describe previous and new methods for leveraging a causal model, including support for counterfactual queries.
|
[
"['Oliver Schulte' 'Pascal Poupart']"
] |
null | null |
2403.04224
| null | null |
http://arxiv.org/pdf/2403.04224v3
|
2024-06-16T15:59:11Z
|
2024-03-07T04:54:56Z
|
Aligners: Decoupling LLMs and Alignment
|
Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. Alignment is challenging, costly, and needs to be repeated for every LLM and alignment criterion. We propose to decouple LLMs and alignment by training aligner models that can be used to align any LLM for a given criteria on an as-needed basis, thus also reducing the potential negative impacts of alignment on performance. Our recipe for training the aligner models solely relies on synthetic data generated with a (prompted) LLM and can be easily adjusted for a variety of alignment criteria. We use the same synthetic data to train inspectors, binary miss-alignment classification models to guide a "squad" of multiple aligners. Our empirical results demonstrate consistent improvements when applying aligner squad to various LLMs, including chat-aligned models, across several instruction-following and red-teaming datasets.
|
[
"['Lilian Ngweta' 'Mayank Agarwal' 'Subha Maity' 'Alex Gittens'\n 'Yuekai Sun' 'Mikhail Yurochkin']"
] |
null | null |
2403.04232
| null | null |
http://arxiv.org/pdf/2403.04232v1
|
2024-03-07T05:25:34Z
|
2024-03-07T05:25:34Z
|
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
|
Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this direction, but learning DRL control policies that generalize to multiple traffic scenarios is still a challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control. By analyzing the performance of MRTL across nearly 600 signalized intersections and 1200 traffic scenarios, we demonstrate that it emerges as a promising approach to synergize the strengths of DRL and conventional methods in generalizable control.
|
[
"['Vindula Jayawardana' 'Sirui Li' 'Cathy Wu' 'Yashar Farid'\n 'Kentaro Oguchi']"
] |
null | null |
2403.04234
| null | null |
http://arxiv.org/pdf/2403.04234v1
|
2024-03-07T05:26:52Z
|
2024-03-07T05:26:52Z
|
Fundamental limits of Non-Linear Low-Rank Matrix Estimation
|
We consider the task of estimating a low-rank matrix from non-linear and noisy observations. We prove a strong universality result showing that Bayes-optimal performances are characterized by an equivalent Gaussian model with an effective prior, whose parameters are entirely determined by an expansion of the non-linear function. In particular, we show that to reconstruct the signal accurately, one requires a signal-to-noise ratio growing as $N^{frac 12 (1-1/k_F)}$, where $k_F$ is the first non-zero Fisher information coefficient of the function. We provide asymptotic characterization for the minimal achievable mean squared error (MMSE) and an approximate message-passing algorithm that reaches the MMSE under conditions analogous to the linear version of the problem. We also provide asymptotic errors achieved by methods such as principal component analysis combined with Bayesian denoising, and compare them with Bayes-optimal MMSE.
|
[
"['Pierre Mergny' 'Justin Ko' 'Florent Krzakala' 'Lenka Zdeborová']"
] |
null | null |
2403.04236
| null | null |
http://arxiv.org/pdf/2403.04236v1
|
2024-03-07T05:38:56Z
|
2024-03-07T05:38:56Z
|
Regularized DeepIV with Model Selection
|
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. While recent advancements in machine learning have introduced flexible methods for IV estimation, they often encounter one or more of the following limitations: (1) restricting the IV regression to be uniquely identified; (2) requiring minimax computation oracle, which is highly unstable in practice; (3) absence of model selection procedure. In this paper, we present the first method and analysis that can avoid all three limitations, while still enabling general function approximation. Specifically, we propose a minimax-oracle-free method called Regularized DeepIV (RDIV) regression that can converge to the least-norm IV solution. Our method consists of two stages: first, we learn the conditional distribution of covariates, and by utilizing the learned distribution, we learn the estimator by minimizing a Tikhonov-regularized loss function. We further show that our method allows model selection procedures that can achieve the oracle rates in the misspecified regime. When extended to an iterative estimator, our method matches the current state-of-the-art convergence rate. Our method is a Tikhonov regularized variant of the popular DeepIV method with a non-parametric MLE first-stage estimator, and our results provide the first rigorous guarantees for this empirically used method, showcasing the importance of regularization which was absent from the original work.
|
[
"['Zihao Li' 'Hui Lan' 'Vasilis Syrgkanis' 'Mengdi Wang' 'Masatoshi Uehara']"
] |
null | null |
2403.04245
| null | null |
http://arxiv.org/pdf/2403.04245v1
|
2024-03-07T06:06:55Z
|
2024-03-07T06:06:55Z
|
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video
Frames for Audio-Visual Speech Recognition
|
Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames, performing even worse than single-modality models. While applying the dropout technique to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. In this paper, we investigate this contrasting phenomenon from the perspective of modality bias and reveal that an excessive modality bias on the audio caused by dropout is the underlying reason. Moreover, we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between modality bias and robustness against missing modality in multimodal systems. Building on these findings, we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality and to maintain performance and robustness simultaneously. Finally, to address an entirely missing modality, we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated and validated through a series of comprehensive experiments using the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR
|
[
"['Yusheng Dai' 'Hang Chen' 'Jun Du' 'Ruoyu Wang' 'Shihao Chen'\n 'Jiefeng Ma' 'Haotian Wang' 'Chin-Hui Lee']"
] |
null | null |
2403.04246
| null | null |
http://arxiv.org/pdf/2403.04246v1
|
2024-03-07T06:07:31Z
|
2024-03-07T06:07:31Z
|
Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic
Differential Equations
|
This study addresses the challenges in parameter estimation of stochastic differential equations driven by non-Gaussian noises, which are critical in understanding dynamic phenomena such as price fluctuations and the spread of infectious diseases. Previous research highlighted the potential of LSTM networks in estimating parameters of alpha stable Levy driven SDEs but faced limitations including high time complexity and constraints of the LSTM chaining property. To mitigate these issues, we introduce the PEnet, a novel CNN-LSTM-based three-stage model that offers an end to end approach with superior accuracy and adaptability to varying data structures, enhanced inference speed for long sequence observations through initial data feature condensation by CNN, and high generalization capability, allowing its application to various complex SDE scenarios. Experiments on synthetic datasets confirm PEnet significant advantage in estimating SDE parameters associated with noise characteristics, establishing it as a competitive method for SDE parameter estimation in the presence of Levy noise.
|
[
"['Shuaiyu Li' 'Yang Ruan' 'Changzhou Long' 'Yuzhong Cheng']"
] |
null | null |
2403.04253
| null | null |
http://arxiv.org/pdf/2403.04253v1
|
2024-03-07T06:35:59Z
|
2024-03-07T06:35:59Z
|
Mastering Memory Tasks with World Models
|
Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solve tasks involving extended time gaps between actions and outcomes, or tasks demanding the recalling of distant observations to inform current actions. To improve temporal coherence, we integrate a new family of state space models (SSMs) in world models of MBRL agents to present a new method, Recall to Imagine (R2I). This integration aims to enhance both long-term memory and long-horizon credit assignment. Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze. At the same time, it upholds comparable performance in classic RL tasks, such as Atari and DMC, suggesting the generality of our method. We also show that R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster wall-time convergence.
|
[
"['Mohammad Reza Samsami' 'Artem Zholus' 'Janarthanan Rajendran'\n 'Sarath Chandar']"
] |
null | null |
2403.04259
| null | null |
http://arxiv.org/pdf/2403.04259v2
|
2024-03-12T04:02:49Z
|
2024-03-07T06:47:45Z
|
Decentralized and Equitable Optimal Transport
|
This paper considers the decentralized (discrete) optimal transport (D-OT) problem. In this setting, a network of agents seeks to design a transportation plan jointly, where the cost function is the sum of privately held costs for each agent. We reformulate the D-OT problem as a constraint-coupled optimization problem and propose a single-loop decentralized algorithm with an iteration complexity of O(1/{epsilon}) that matches existing centralized first-order approaches. Moreover, we propose the decentralized equitable optimal transport (DE-OT) problem. In DE-OT, in addition to cooperatively designing a transportation plan that minimizes transportation costs, agents seek to ensure equity in their individual costs. The iteration complexity of the proposed method to solve DE-OT is also O(1/{epsilon}). This rate improves existing centralized algorithms, where the best iteration complexity obtained is O(1/{epsilon}^2).
|
[
"['Ivan Lau' 'Shiqian Ma' 'César A. Uribe']"
] |
null | null |
2403.04260
| null | null |
http://arxiv.org/pdf/2403.04260v2
|
2024-03-28T11:55:32Z
|
2024-03-07T06:49:37Z
|
Can Small Language Models be Good Reasoners for Sequential
Recommendation?
|
Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations empowered by LLMs. Firstly, user behavior patterns are often complex, and relying solely on one-step reasoning from LLMs may lead to incorrect or task-irrelevant responses. Secondly, the prohibitively resource requirements of LLM (e.g., ChatGPT-175B) are overwhelmingly high and impractical for real sequential recommender systems. In this paper, we propose a novel Step-by-step knowLedge dIstillation fraMework for recommendation (SLIM), paving a promising path for sequential recommenders to enjoy the exceptional reasoning capabilities of LLMs in a "slim" (i.e., resource-efficient) manner. We introduce CoT prompting based on user behavior sequences for the larger teacher model. The rationales generated by the teacher model are then utilized as labels to distill the downstream smaller student model (e.g., LLaMA2-7B). In this way, the student model acquires the step-by-step reasoning capabilities in recommendation tasks. We encode the generated rationales from the student model into a dense vector, which empowers recommendation in both ID-based and ID-agnostic scenarios. Extensive experiments demonstrate the effectiveness of SLIM over state-of-the-art baselines, and further analysis showcasing its ability to generate meaningful recommendation reasoning at affordable costs.
|
[
"['Yuling Wang' 'Changxin Tian' 'Binbin Hu' 'Yanhua Yu' 'Ziqi Liu'\n 'Zhiqiang Zhang' 'Jun Zhou' 'Liang Pang' 'Xiao Wang']"
] |
null | null |
2403.04261
| null | null |
http://arxiv.org/pdf/2403.04261v1
|
2024-03-07T06:52:51Z
|
2024-03-07T06:52:51Z
|
Advancing Biomedical Text Mining with Community Challenges
|
The field of biomedical research has witnessed a significant increase in the accumulation of vast amounts of textual data from various sources such as scientific literatures, electronic health records, clinical trial reports, and social media. However, manually processing and analyzing these extensive and complex resources is time-consuming and inefficient. To address this challenge, biomedical text mining, also known as biomedical natural language processing, has garnered great attention. Community challenge evaluation competitions have played an important role in promoting technology innovation and interdisciplinary collaboration in biomedical text mining research. These challenges provide platforms for researchers to develop state-of-the-art solutions for data mining and information processing in biomedical research. In this article, we review the recent advances in community challenges specific to Chinese biomedical text mining. Firstly, we collect the information of these evaluation tasks, such as data sources and task types. Secondly, we conduct systematic summary and comparative analysis, including named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. Then, we summarize the potential clinical applications of these community challenge tasks from translational informatics perspective. Finally, we discuss the contributions and limitations of these community challenges, while highlighting future directions in the era of large language models.
|
[
"['Hui Zong' 'Rongrong Wu' 'Jiaxue Cha' 'Erman Wu' 'Jiakun Li' 'Liang Tao'\n 'Zuofeng Li' 'Buzhou Tang' 'Bairong Shen']"
] |
null | null |
2403.04268
| null | null |
http://arxiv.org/pdf/2403.04268v1
|
2024-03-07T07:08:57Z
|
2024-03-07T07:08:57Z
|
Qubit-Wise Architecture Search Method for Variational Quantum Circuits
|
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates. As the classical neural architecture search (NAS), quantum architecture search methods (QAS) employ methods like reinforcement learning, evolutionary algorithms and supernet optimiza-tion to improve the search efficiency. In this paper, we propose a novel qubit-wise architec-ture search (QWAS) method, which progres-sively search one-qubit configuration per stage, and combine with Monte Carlo Tree Search al-gorithm to find good quantum architectures by partitioning the search space into several good and bad subregions. The numerical experimental results indicate that our proposed method can balance the exploration and exploitation of cir-cuit performance and size in some real-world tasks, such as MNIST, Fashion and MOSI. As far as we know, QWAS achieves the state-of-art re-sults of all tasks in the terms of accuracy and circuit size.
|
[
"['Jialin Chen' 'Zhiqiang Cai' 'Ke Xu' 'Di Wu' 'Wei Cao']"
] |
null | null |
2403.04283
| null | null |
http://arxiv.org/pdf/2403.04283v1
|
2024-03-07T07:31:00Z
|
2024-03-07T07:31:00Z
|
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model
with Proxy
|
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require a high computational cost, one main reason being that RLHF assigns both the generation and alignment tasks to the LLM simultaneously. In this paper, we introduce Proxy-RLHF, which decouples the generation and alignment processes of LLMs, achieving alignment with human values at a much lower computational cost. We start with a novel Markov Decision Process (MDP) designed for the alignment process and employ Reinforcement Learning (RL) to train a streamlined proxy model that oversees the token generation of the LLM, without altering the LLM itself. Experiments show that our method achieves a comparable level of alignment with only 1% of the training parameters of other methods.
|
[
"['Yu Zhu' 'Chuxiong Sun' 'Wenfei Yang' 'Wenqiang Wei' 'Bo Tang'\n 'Tianzhu Zhang' 'Zhiyu Li' 'Shifeng Zhang' 'Feiyu Xiong' 'Jie Hu'\n 'Mingchuan yang']"
] |
null | null |
2403.04290
| null | null |
http://arxiv.org/pdf/2403.04290v1
|
2024-03-07T07:39:00Z
|
2024-03-07T07:39:00Z
|
MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided
Diffusion with Visual Invariant
|
Medical generative models, acknowledged for their high-quality sample generation ability, have accelerated the fast growth of medical applications. However, recent works concentrate on separate medical generation models for distinct medical tasks and are restricted to inadequate medical multi-modal knowledge, constraining medical comprehensive diagnosis. In this paper, we propose MedM2G, a Medical Multi-Modal Generative framework, with the key innovation to align, extract, and generate medical multi-modal within a unified model. Extending beyond single or two medical modalities, we efficiently align medical multi-modal through the central alignment approach in the unified space. Significantly, our framework extracts valuable clinical knowledge by preserving the medical visual invariant of each imaging modal, thereby enhancing specific medical information for multi-modal generation. By conditioning the adaptive cross-guided parameters into the multi-flow diffusion framework, our model promotes flexible interactions among medical multi-modal for generation. MedM2G is the first medical generative model that unifies medical generation tasks of text-to-image, image-to-text, and unified generation of medical modalities (CT, MRI, X-ray). It performs 5 medical generation tasks across 10 datasets, consistently outperforming various state-of-the-art works.
|
[
"['Chenlu Zhan' 'Yu Lin' 'Gaoang Wang' 'Hongwei Wang' 'Jian Wu']"
] |
null | null |
2403.04306
| null | null |
http://arxiv.org/pdf/2403.04306v4
|
2024-06-11T07:42:51Z
|
2024-03-07T08:25:27Z
|
Effectiveness Assessment of Recent Large Vision-Language Models
|
The advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence. However, the model's effectiveness in both specialized and general tasks warrants further investigation. This paper endeavors to evaluate the competency of popular LVLMs in specialized and general tasks, respectively, aiming to offer a comprehensive understanding of these novel models. To gauge their effectiveness in specialized tasks, we employ six challenging tasks in three different application scenarios: natural, healthcare, and industrial. These six tasks include salient/camouflaged/transparent object detection, as well as polyp detection, skin lesion detection, and industrial anomaly detection. We examine the performance of three recent open-source LVLMs, including MiniGPT-v2, LLaVA-1.5, and Shikra, on both visual recognition and localization in these tasks. Moreover, we conduct empirical investigations utilizing the aforementioned LVLMs together with GPT-4V, assessing their multi-modal understanding capabilities in general tasks including object counting, absurd question answering, affordance reasoning, attribute recognition, and spatial relation reasoning. Our investigations reveal that these LVLMs demonstrate limited proficiency not only in specialized tasks but also in general tasks. We delve deep into this inadequacy and uncover several potential factors, including limited cognition in specialized tasks, object hallucination, text-to-image interference, and decreased robustness in complex problems. We hope that this study can provide useful insights for the future development of LVLMs, helping researchers improve LVLMs for both general and specialized applications.
|
[
"['Yao Jiang' 'Xinyu Yan' 'Ge-Peng Ji' 'Keren Fu' 'Meijun Sun' 'Huan Xiong'\n 'Deng-Ping Fan' 'Fahad Shahbaz Khan']"
] |
null | null |
2403.04317
| null | null |
http://arxiv.org/pdf/2403.04317v1
|
2024-03-07T08:34:57Z
|
2024-03-07T08:34:57Z
|
Online Adaptation of Language Models with a Memory of Amortized Contexts
|
Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. Due to this crucial need to keep models updated, online learning has emerged as a critical necessity when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose an amortized feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank. When answering questions, our model attends to and extracts relevant knowledge from this memory bank. To learn informative modulations in an efficient manner, we utilize amortization-based meta-learning, which substitutes the optimization process with a single forward pass of the encoder. Subsequently, we learn to choose from and aggregate selected documents into a single modulation by conditioning on the question, allowing us to adapt a frozen language model during test time without requiring further gradient updates. Our experiment demonstrates the superiority of MAC in multiple aspects, including online adaptation performance, time, and memory efficiency. Code is available at: https://github.com/jihoontack/MAC.
|
[
"['Jihoon Tack' 'Jaehyung Kim' 'Eric Mitchell' 'Jinwoo Shin' 'Yee Whye Teh'\n 'Jonathan Richard Schwarz']"
] |
null | null |
2403.04322
| null | null |
http://arxiv.org/pdf/2403.04322v1
|
2024-03-07T08:37:36Z
|
2024-03-07T08:37:36Z
|
Memetic Differential Evolution Methods for Semi-Supervised Clustering
|
In this paper, we deal with semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems where background knowledge is given in the form of instance-level constraints. In particular, we take into account "must-link" and "cannot-link" constraints, each of which indicates if two dataset points should be associated to the same or to a different cluster. The presence of such constraints makes the problem at least as hard as its unsupervised version: it is no more true that each point is associated to its nearest cluster center, thus requiring some modifications in crucial operations, such as the assignment step. In this scenario, we propose a novel memetic strategy based on the Differential Evolution paradigm, directly extending a state-of-the-art framework recently proposed in the unsupervised clustering literature. As far as we know, our contribution represents the first attempt to define a memetic methodology designed to generate a (hopefully) optimal feasible solution for the semi-supervised MSSC problem. The proposal is compared with some state-of-the-art algorithms from the literature on a set of well-known datasets, highlighting its effectiveness and efficiency in finding good quality clustering solutions.
|
[
"['Pierluigi Mansueto' 'Fabio Schoen']"
] |
null | null |
2403.04326
| null | null |
http://arxiv.org/pdf/2403.04326v1
|
2024-03-07T08:45:31Z
|
2024-03-07T08:45:31Z
|
Edge-based Parametric Digital Twins for Intelligent Building Indoor
Climate Modeling
|
Digital transformation in the built environment generates vast data for developing data-driven models to optimize building operations. This study presents an integrated solution utilizing edge computing, digital twins, and deep learning to enhance the understanding of climate in buildings. Parametric digital twins, created using an ontology, ensure consistent data representation across diverse service systems equipped by different buildings. Based on created digital twins and collected data, deep learning methods are employed to develop predictive models for identifying patterns in indoor climate and providing insights. Both the parametric digital twin and deep learning models are deployed on edge for low latency and privacy compliance. As a demonstration, a case study was conducted in a historic building in "Osterg"otland, Sweden, to compare the performance of five deep learning architectures. The results indicate that the time-series dense encoder model exhibited strong competitiveness in performing multi-horizon forecasts of indoor temperature and relative humidity with low computational costs.
|
[
"['Zhongjun Ni' 'Chi Zhang' 'Magnus Karlsson' 'Shaofang Gong']"
] |
null | null |
2403.04329
| null | null |
http://arxiv.org/pdf/2403.04329v2
|
2024-05-27T02:21:28Z
|
2024-03-07T08:48:42Z
|
A mechanism-driven reinforcement learning framework for shape
optimization of airfoils
|
In this paper, a novel mechanism-driven reinforcement learning framework is proposed for airfoil shape optimization. To validate the framework, a reward function is designed and analyzed, from which the equivalence between the maximizing the cumulative reward and achieving the optimization objectives is guaranteed theoretically. To establish a quality exploration, and to obtain an accurate reward from the environment, an efficient solver for steady Euler equations is employed in the reinforcement learning method. The solver utilizes the B'ezier curve to describe the shape of the airfoil, and a Newton-geometric multigrid method for the solution. In particular, a dual-weighted residual-based h-adaptive method is used for efficient calculation of target functional. To effectively streamline the airfoil shape during the deformation process, we introduce the Laplacian smoothing, and propose a B'ezier fitting strategy, which not only remits mesh tangling but also guarantees a precise manipulation of the geometry. In addition, a neural network architecture is designed based on an attention mechanism to make the learning process more sensitive to the minor change of the airfoil geometry. Numerical experiments demonstrate that our framework can handle the optimization problem with hundreds of design variables. It is worth mentioning that, prior to this work, there are limited works combining such high-fidelity partial differential equatons framework with advanced reinforcement learning algorithms for design problems with such high dimensionality.
|
[
"['Jingfeng Wang' 'Guanghui Hu']"
] |
null | null |
2403.04337
| null | null |
http://arxiv.org/pdf/2403.04337v1
|
2024-03-07T09:02:11Z
|
2024-03-07T09:02:11Z
|
Explainable AI for Embedded Systems Design: A Case Study of Static
Redundant NVM Memory Write Prediction
|
This paper investigates the application of eXplainable Artificial Intelligence (XAI) in the design of embedded systems using machine learning (ML). As a case study, it addresses the challenging problem of static silent store prediction. This involves identifying redundant memory writes based only on static program features. Eliminating such stores enhances performance and energy efficiency by reducing memory access and bus traffic, especially in the presence of emerging non-volatile memory technologies. To achieve this, we propose a methodology consisting of: 1) the development of relevant ML models for explaining silent store prediction, and 2) the application of XAI to explain these models. We employ two state-of-the-art model-agnostic XAI methods to analyze the causes of silent stores. Through the case study, we evaluate the effectiveness of the methods. We find that these methods provide explanations for silent store predictions, which are consistent with known causes of silent store occurrences from previous studies. Typically, this allows us to confirm the prevalence of silent stores in operations that write the zero constant into memory, or the absence of silent stores in operations involving loop induction variables. This suggests the potential relevance of XAI in analyzing ML models' decision in embedded system design. From the case study, we share some valuable insights and pitfalls we encountered. More generally, this study aims to lay the groundwork for future research in the emerging field of XAI for embedded system design.
|
[
"['Abdoulaye Gamatié' 'Yuyang Wang']"
] |
null | null |
2403.04344
| null | null |
http://arxiv.org/pdf/2403.04344v1
|
2024-03-07T09:12:23Z
|
2024-03-07T09:12:23Z
|
RL-CFR: Improving Action Abstraction for Imperfect Information
Extensive-Form Games with Reinforcement Learning
|
Effective action abstraction is crucial in tackling challenges associated with large action spaces in Imperfect Information Extensive-Form Games (IIEFGs). However, due to the vast state space and computational complexity in IIEFGs, existing methods often rely on fixed abstractions, resulting in sub-optimal performance. In response, we introduce RL-CFR, a novel reinforcement learning (RL) approach for dynamic action abstraction. RL-CFR builds upon our innovative Markov Decision Process (MDP) formulation, with states corresponding to public information and actions represented as feature vectors indicating specific action abstractions. The reward is defined as the expected payoff difference between the selected and default action abstractions. RL-CFR constructs a game tree with RL-guided action abstractions and utilizes counterfactual regret minimization (CFR) for strategy derivation. Impressively, it can be trained from scratch, achieving higher expected payoff without increased CFR solving time. In experiments on Heads-up No-limit Texas Hold'em, RL-CFR outperforms ReBeL's replication and Slumbot, demonstrating significant win-rate margins of $64pm 11$ and $84pm 17$ mbb/hand, respectively.
|
[
"['Boning Li' 'Zhixuan Fang' 'Longbo Huang']"
] |
null | null |
2403.04348
| null | null |
http://arxiv.org/pdf/2403.04348v1
|
2024-03-07T09:22:50Z
|
2024-03-07T09:22:50Z
|
LoCoDL: Communication-Efficient Distributed Learning with Local Training
and Compression
|
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a communication-efficient algorithm that leverages the two popular and effective techniques of Local training, which reduces the communication frequency, and Compression, in which short bitstreams are sent instead of full-dimensional vectors of floats. LoCoDL works with a large class of unbiased compressors that includes widely-used sparsification and quantization methods. LoCoDL provably benefits from local training and compression and enjoys a doubly-accelerated communication complexity, with respect to the condition number of the functions and the model dimension, in the general heterogenous regime with strongly convex functions. This is confirmed in practice, with LoCoDL outperforming existing algorithms.
|
[
"['Laurent Condat' 'Artavazd Maranjyan' 'Peter Richtárik']"
] |
null | null |
2403.04385
| null | null |
http://arxiv.org/pdf/2403.04385v2
|
2024-04-12T10:15:45Z
|
2024-03-07T10:25:23Z
|
Impacts of Color and Texture Distortions on Earth Observation Data in
Deep Learning
|
Land cover classification and change detection are two important applications of remote sensing and Earth observation (EO) that have benefited greatly from the advances of deep learning. Convolutional and transformer-based U-net models are the state-of-the-art architectures for these tasks, and their performances have been boosted by an increased availability of large-scale annotated EO datasets. However, the influence of different visual characteristics of the input EO data on a model's predictions is not well understood. In this work we systematically examine model sensitivities with respect to several color- and texture-based distortions on the input EO data during inference, given models that have been trained without such distortions. We conduct experiments with multiple state-of-the-art segmentation networks for land cover classification and show that they are in general more sensitive to texture than to color distortions. Beyond revealing intriguing characteristics of widely used land cover classification models, our results can also be used to guide the development of more robust models within the EO domain.
|
[
"['Martin Willbo' 'Aleksis Pirinen' 'John Martinsson' 'Edvin Listo Zec'\n 'Olof Mogren' 'Mikael Nilsson']"
] |
null | null |
2403.04405
| null | null |
http://arxiv.org/pdf/2403.04405v1
|
2024-03-07T11:00:35Z
|
2024-03-07T11:00:35Z
|
Signature Isolation Forest
|
Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data. It relies on a tree partition procedure where an abnormality score is computed by projecting each curve observation on a drawn dictionary through a linear inner product. Such linear inner product and the dictionary are a priori choices that highly influence the algorithm's performances and might lead to unreliable results, particularly with complex datasets. This work addresses these challenges by introducing textit{Signature Isolation Forest}, a novel AD algorithm class leveraging the rough path theory's signature transform. Our objective is to remove the constraints imposed by FIF through the proposition of two algorithms which specifically target the linearity of the FIF inner product and the choice of the dictionary. We provide several numerical experiments, including a real-world applications benchmark showing the relevance of our methods.
|
[
"['Guillaume Staerman' 'Marta Campi' 'Gareth W. Peters']"
] |
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