categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
cs.AI cs.LG cs.SY | null | 1206.3285 | null | null | http://arxiv.org/pdf/1206.3285v1 | 2012-06-13T15:45:04Z | 2012-06-13T15:45:04Z | Dyna-Style Planning with Linear Function Approximation and Prioritized
Sweeping | We consider the problem of efficiently learning optimal control policies and
value functions over large state spaces in an online setting in which estimates
must be available after each interaction with the world. This paper develops an
explicitly model-based approach extending the Dyna architecture to linear
function approximation. Dynastyle planning proceeds by generating imaginary
experience from the world model and then applying model-free reinforcement
learning algorithms to the imagined state transitions. Our main results are to
prove that linear Dyna-style planning converges to a unique solution
independent of the generating distribution, under natural conditions. In the
policy evaluation setting, we prove that the limit point is the least-squares
(LSTD) solution. An implication of our results is that prioritized-sweeping can
be soundly extended to the linear approximation case, backing up to preceding
features rather than to preceding states. We introduce two versions of
prioritized sweeping with linear Dyna and briefly illustrate their performance
empirically on the Mountain Car and Boyan Chain problems.
| [
"Richard S. Sutton, Csaba Szepesvari, Alborz Geramifard, Michael P.\n Bowling",
"['Richard S. Sutton' 'Csaba Szepesvari' 'Alborz Geramifard'\n 'Michael P. Bowling']"
] |
cs.LG stat.ME stat.ML | null | 1206.3287 | null | null | http://arxiv.org/pdf/1206.3287v1 | 2012-06-13T15:45:39Z | 2012-06-13T15:45:39Z | Learning the Bayesian Network Structure: Dirichlet Prior versus Data | In the Bayesian approach to structure learning of graphical models, the
equivalent sample size (ESS) in the Dirichlet prior over the model parameters
was recently shown to have an important effect on the maximum-a-posteriori
estimate of the Bayesian network structure. In our first contribution, we
theoretically analyze the case of large ESS-values, which complements previous
work: among other results, we find that the presence of an edge in a Bayesian
network is favoured over its absence even if both the Dirichlet prior and the
data imply independence, as long as the conditional empirical distribution is
notably different from uniform. In our second contribution, we focus on
realistic ESS-values, and provide an analytical approximation to the "optimal"
ESS-value in a predictive sense (its accuracy is also validated
experimentally): this approximation provides an understanding as to which
properties of the data have the main effect determining the "optimal"
ESS-value.
| [
"['Harald Steck']",
"Harald Steck"
] |
cs.LG stat.ML | null | 1206.3290 | null | null | http://arxiv.org/pdf/1206.3290v1 | 2012-06-13T15:47:16Z | 2012-06-13T15:47:16Z | Modelling local and global phenomena with sparse Gaussian processes | Much recent work has concerned sparse approximations to speed up the Gaussian
process regression from the unfavorable O(n3) scaling in computational time to
O(nm2). Thus far, work has concentrated on models with one covariance function.
However, in many practical situations additive models with multiple covariance
functions may perform better, since the data may contain both long and short
length-scale phenomena. The long length-scales can be captured with global
sparse approximations, such as fully independent conditional (FIC), and the
short length-scales can be modeled naturally by covariance functions with
compact support (CS). CS covariance functions lead to naturally sparse
covariance matrices, which are computationally cheaper to handle than full
covariance matrices. In this paper, we propose a new sparse Gaussian process
model with two additive components: FIC for the long length-scales and CS
covariance function for the short length-scales. We give theoretical and
experimental results and show that under certain conditions the proposed model
has the same computational complexity as FIC. We also compare the model
performance of the proposed model to additive models approximated by fully and
partially independent conditional (PIC). We use real data sets and show that
our model outperforms FIC and PIC approximations for data sets with two
additive phenomena.
| [
"['Jarno Vanhatalo' 'Aki Vehtari']",
"Jarno Vanhatalo, Aki Vehtari"
] |
cs.LG stat.ML | null | 1206.3294 | null | null | http://arxiv.org/pdf/1206.3294v1 | 2012-06-13T15:52:35Z | 2012-06-13T15:52:35Z | Flexible Priors for Exemplar-based Clustering | Exemplar-based clustering methods have been shown to produce state-of-the-art
results on a number of synthetic and real-world clustering problems. They are
appealing because they offer computational benefits over latent-mean models and
can handle arbitrary pairwise similarity measures between data points. However,
when trying to recover underlying structure in clustering problems, tailored
similarity measures are often not enough; we also desire control over the
distribution of cluster sizes. Priors such as Dirichlet process priors allow
the number of clusters to be unspecified while expressing priors over data
partitions. To our knowledge, they have not been applied to exemplar-based
models. We show how to incorporate priors, including Dirichlet process priors,
into the recently introduced affinity propagation algorithm. We develop an
efficient maxproduct belief propagation algorithm for our new model and
demonstrate experimentally how the expanded range of clustering priors allows
us to better recover true clusterings in situations where we have some
information about the generating process.
| [
"Daniel Tarlow, Richard S. Zemel, Brendan J. Frey",
"['Daniel Tarlow' 'Richard S. Zemel' 'Brendan J. Frey']"
] |
cs.LG stat.ML | null | 1206.3297 | null | null | http://arxiv.org/pdf/1206.3297v1 | 2012-06-13T15:56:12Z | 2012-06-13T15:56:12Z | Hybrid Variational/Gibbs Collapsed Inference in Topic Models | Variational Bayesian inference and (collapsed) Gibbs sampling are the two
important classes of inference algorithms for Bayesian networks. Both have
their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is
also inefficient for large count values and requires averaging over many
samples to reduce variance. On the other hand, variational Bayesian inference
is efficient and accurate for large count values but suffers from bias for
small counts. We propose a hybrid algorithm that combines the best of both
worlds: it samples very small counts and applies variational updates to large
counts. This hybridization is shown to significantly improve testset perplexity
relative to variational inference at no computational cost.
| [
"Max Welling, Yee Whye Teh, Hilbert Kappen",
"['Max Welling' 'Yee Whye Teh' 'Hilbert Kappen']"
] |
cs.IR cs.LG stat.ML | null | 1206.3298 | null | null | http://arxiv.org/pdf/1206.3298v2 | 2015-05-16T22:57:04Z | 2012-06-13T15:56:33Z | Continuous Time Dynamic Topic Models | In this paper, we develop the continuous time dynamic topic model (cDTM). The
cDTM is a dynamic topic model that uses Brownian motion to model the latent
topics through a sequential collection of documents, where a "topic" is a
pattern of word use that we expect to evolve over the course of the collection.
We derive an efficient variational approximate inference algorithm that takes
advantage of the sparsity of observations in text, a property that lets us
easily handle many time points. In contrast to the cDTM, the original
discrete-time dynamic topic model (dDTM) requires that time be discretized.
Moreover, the complexity of variational inference for the dDTM grows quickly as
time granularity increases, a drawback which limits fine-grained
discretization. We demonstrate the cDTM on two news corpora, reporting both
predictive perplexity and the novel task of time stamp prediction.
| [
"['Chong Wang' 'David Blei' 'David Heckerman']",
"Chong Wang, David Blei, David Heckerman"
] |
cs.AI cs.LG | null | 1206.3382 | null | null | http://arxiv.org/pdf/1206.3382v2 | 2012-12-19T08:48:44Z | 2012-06-15T07:23:28Z | Simple Regret Optimization in Online Planning for Markov Decision
Processes | We consider online planning in Markov decision processes (MDPs). In online
planning, the agent focuses on its current state only, deliberates about the
set of possible policies from that state onwards and, when interrupted, uses
the outcome of that exploratory deliberation to choose what action to perform
next. The performance of algorithms for online planning is assessed in terms of
simple regret, which is the agent's expected performance loss when the chosen
action, rather than an optimal one, is followed.
To date, state-of-the-art algorithms for online planning in general MDPs are
either best effort, or guarantee only polynomial-rate reduction of simple
regret over time. Here we introduce a new Monte-Carlo tree search algorithm,
BRUE, that guarantees exponential-rate reduction of simple regret and error
probability. This algorithm is based on a simple yet non-standard state-space
sampling scheme, MCTS2e, in which different parts of each sample are dedicated
to different exploratory objectives. Our empirical evaluation shows that BRUE
not only provides superior performance guarantees, but is also very effective
in practice and favorably compares to state-of-the-art. We then extend BRUE
with a variant of "learning by forgetting." The resulting set of algorithms,
BRUE(alpha), generalizes BRUE, improves the exponential factor in the upper
bound on its reduction rate, and exhibits even more attractive empirical
performance.
| [
"Zohar Feldman, Carmel Domshlak",
"['Zohar Feldman' 'Carmel Domshlak']"
] |
cs.LG q-bio.BM | null | 1206.3509 | null | null | http://arxiv.org/pdf/1206.3509v1 | 2012-06-15T16:31:45Z | 2012-06-15T16:31:45Z | A Novel Approach for Protein Structure Prediction | The idea of this project is to study the protein structure and sequence
relationship using the hidden markov model and artificial neural network. In
this context we have assumed two hidden markov models. In first model we have
taken protein secondary structures as hidden and protein sequences as observed.
In second model we have taken protein sequences as hidden and protein
structures as observed. The efficiencies for both the hidden markov models have
been calculated. The results show that the efficiencies of first model is
greater that the second one .These efficiencies are cross validated using
artificial neural network. This signifies the importance of protein secondary
structures as the main hidden controlling factors due to which we observe a
particular amino acid sequence. This also signifies that protein secondary
structure is more conserved in comparison to amino acid sequence.
| [
"Saurabh Sarkar, Prateek Malhotra, Virender Guman",
"['Saurabh Sarkar' 'Prateek Malhotra' 'Virender Guman']"
] |
math.OC cs.LG cs.SY | 10.1109/CDC.2012.6426587 | 1206.3582 | null | null | http://arxiv.org/abs/1206.3582v1 | 2012-06-14T07:07:58Z | 2012-06-14T07:07:58Z | Decentralized Learning for Multi-player Multi-armed Bandits | We consider the problem of distributed online learning with multiple players
in multi-armed bandits (MAB) models. Each player can pick among multiple arms.
When a player picks an arm, it gets a reward. We consider both i.i.d. reward
model and Markovian reward model. In the i.i.d. model each arm is modelled as
an i.i.d. process with an unknown distribution with an unknown mean. In the
Markovian model, each arm is modelled as a finite, irreducible, aperiodic and
reversible Markov chain with an unknown probability transition matrix and
stationary distribution. The arms give different rewards to different players.
If two players pick the same arm, there is a "collision", and neither of them
get any reward. There is no dedicated control channel for coordination or
communication among the players. Any other communication between the users is
costly and will add to the regret. We propose an online index-based distributed
learning policy called ${\tt dUCB_4}$ algorithm that trades off
\textit{exploration v. exploitation} in the right way, and achieves expected
regret that grows at most as near-$O(\log^2 T)$. The motivation comes from
opportunistic spectrum access by multiple secondary users in cognitive radio
networks wherein they must pick among various wireless channels that look
different to different users. This is the first distributed learning algorithm
for multi-player MABs to the best of our knowledge.
| [
"['Dileep Kalathil' 'Naumaan Nayyar' 'Rahul Jain']",
"Dileep Kalathil, Naumaan Nayyar and Rahul Jain"
] |
cs.LG q-bio.NC | null | 1206.3666 | null | null | http://arxiv.org/pdf/1206.3666v1 | 2012-06-16T13:35:21Z | 2012-06-16T13:35:21Z | Unsupervised adaptation of brain machine interface decoders | The performance of neural decoders can degrade over time due to
nonstationarities in the relationship between neuronal activity and behavior.
In this case, brain-machine interfaces (BMI) require adaptation of their
decoders to maintain high performance across time. One way to achieve this is
by use of periodical calibration phases, during which the BMI system (or an
external human demonstrator) instructs the user to perform certain movements or
behaviors. This approach has two disadvantages: (i) calibration phases
interrupt the autonomous operation of the BMI and (ii) between two calibration
phases the BMI performance might not be stable but continuously decrease. A
better alternative would be that the BMI decoder is able to continuously adapt
in an unsupervised manner during autonomous BMI operation, i.e. without knowing
the movement intentions of the user.
In the present article, we present an efficient method for such unsupervised
training of BMI systems for continuous movement control. The proposed method
utilizes a cost function derived from neuronal recordings, which guides a
learning algorithm to evaluate the decoding parameters. We verify the
performance of our adaptive method by simulating a BMI user with an optimal
feedback control model and its interaction with our adaptive BMI decoder. The
simulation results show that the cost function and the algorithm yield fast and
precise trajectories towards targets at random orientations on a 2-dimensional
computer screen. For initially unknown and non-stationary tuning parameters,
our unsupervised method is still able to generate precise trajectories and to
keep its performance stable in the long term. The algorithm can optionally work
also with neuronal error signals instead or in conjunction with the proposed
unsupervised adaptation.
| [
"Tayfun G\\\"urel, Carsten Mehring",
"['Tayfun Gürel' 'Carsten Mehring']"
] |
cs.LG cs.GT stat.ML | null | 1206.3713 | null | null | http://arxiv.org/pdf/1206.3713v4 | 2015-05-04T02:04:26Z | 2012-06-16T23:20:09Z | Learning the Structure and Parameters of Large-Population Graphical
Games from Behavioral Data | We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.
| [
"['Jean Honorio' 'Luis Ortiz']",
"Jean Honorio and Luis Ortiz"
] |
cs.CV cs.AI cs.LG | null | 1206.3714 | null | null | http://arxiv.org/pdf/1206.3714v1 | 2012-06-16T23:26:38Z | 2012-06-16T23:26:38Z | How important are Deformable Parts in the Deformable Parts Model? | The main stated contribution of the Deformable Parts Model (DPM) detector of
Felzenszwalb et al. (over the Histogram-of-Oriented-Gradients approach of Dalal
and Triggs) is the use of deformable parts. A secondary contribution is the
latent discriminative learning. Tertiary is the use of multiple components. A
common belief in the vision community (including ours, before this study) is
that their ordering of contributions reflects the performance of detector in
practice. However, what we have experimentally found is that the ordering of
importance might actually be the reverse. First, we show that by increasing the
number of components, and switching the initialization step from their
aspect-ratio, left-right flipping heuristics to appearance-based clustering,
considerable improvement in performance is obtained. But more intriguingly, we
show that with these new components, the part deformations can now be
completely switched off, yet obtaining results that are almost on par with the
original DPM detector. Finally, we also show initial results for using multiple
components on a different problem -- scene classification, suggesting that this
idea might have wider applications in addition to object detection.
| [
"['Santosh K. Divvala' 'Alexei A. Efros' 'Martial Hebert']",
"Santosh K. Divvala and Alexei A. Efros and Martial Hebert"
] |
cs.LG stat.ML | null | 1206.3721 | null | null | http://arxiv.org/pdf/1206.3721v1 | 2012-06-17T04:40:09Z | 2012-06-17T04:40:09Z | Constraint-free Graphical Model with Fast Learning Algorithm | In this paper, we propose a simple, versatile model for learning the
structure and parameters of multivariate distributions from a data set.
Learning a Markov network from a given data set is not a simple problem,
because Markov networks rigorously represent Markov properties, and this rigor
imposes complex constraints on the design of the networks. Our proposed model
removes these constraints, acquiring important aspects from the information
geometry. The proposed parameter- and structure-learning algorithms are simple
to execute as they are based solely on local computation at each node.
Experiments demonstrate that our algorithms work appropriately.
| [
"Kazuya Takabatake and Shotaro Akaho",
"['Kazuya Takabatake' 'Shotaro Akaho']"
] |
cs.LG stat.ML | null | 1206.3881 | null | null | http://arxiv.org/pdf/1206.3881v1 | 2012-06-18T10:33:29Z | 2012-06-18T10:33:29Z | DANCo: Dimensionality from Angle and Norm Concentration | In the last decades the estimation of the intrinsic dimensionality of a
dataset has gained considerable importance. Despite the great deal of research
work devoted to this task, most of the proposed solutions prove to be
unreliable when the intrinsic dimensionality of the input dataset is high and
the manifold where the points lie is nonlinearly embedded in a higher
dimensional space. In this paper we propose a novel robust intrinsic
dimensionality estimator that exploits the twofold complementary information
conveyed both by the normalized nearest neighbor distances and by the angles
computed on couples of neighboring points, providing also closed-forms for the
Kullback-Leibler divergences of the respective distributions. Experiments
performed on both synthetic and real datasets highlight the robustness and the
effectiveness of the proposed algorithm when compared to state of the art
methodologies.
| [
"Claudio Ceruti and Simone Bassis and Alessandro Rozza and Gabriele\n Lombardi and Elena Casiraghi and Paola Campadelli",
"['Claudio Ceruti' 'Simone Bassis' 'Alessandro Rozza' 'Gabriele Lombardi'\n 'Elena Casiraghi' 'Paola Campadelli']"
] |
cs.LG cs.CV stat.ML | null | 1206.4074 | null | null | http://arxiv.org/pdf/1206.4074v3 | 2013-06-12T19:29:18Z | 2012-06-18T21:05:16Z | A Linear Approximation to the chi^2 Kernel with Geometric Convergence | We propose a new analytical approximation to the $\chi^2$ kernel that
converges geometrically. The analytical approximation is derived with
elementary methods and adapts to the input distribution for optimal convergence
rate. Experiments show the new approximation leads to improved performance in
image classification and semantic segmentation tasks using a random Fourier
feature approximation of the $\exp-\chi^2$ kernel. Besides, out-of-core
principal component analysis (PCA) methods are introduced to reduce the
dimensionality of the approximation and achieve better performance at the
expense of only an additional constant factor to the time complexity. Moreover,
when PCA is performed jointly on the training and unlabeled testing data,
further performance improvements can be obtained. Experiments conducted on the
PASCAL VOC 2010 segmentation and the ImageNet ILSVRC 2010 datasets show
statistically significant improvements over alternative approximation methods.
| [
"Fuxin Li, Guy Lebanon, Cristian Sminchisescu",
"['Fuxin Li' 'Guy Lebanon' 'Cristian Sminchisescu']"
] |
cs.LG cs.IR | null | 1206.4110 | null | null | http://arxiv.org/pdf/1206.4110v1 | 2012-06-19T02:24:55Z | 2012-06-19T02:24:55Z | ConeRANK: Ranking as Learning Generalized Inequalities | We propose a new data mining approach in ranking documents based on the
concept of cone-based generalized inequalities between vectors. A partial
ordering between two vectors is made with respect to a proper cone and thus
learning the preferences is formulated as learning proper cones. A pairwise
learning-to-rank algorithm (ConeRank) is proposed to learn a non-negative
subspace, formulated as a polyhedral cone, over document-pair differences. The
algorithm is regularized by controlling the `volume' of the cone. The
experimental studies on the latest and largest ranking dataset LETOR 4.0 shows
that ConeRank is competitive against other recent ranking approaches.
| [
"Truyen T. Tran and Duc Son Pham",
"['Truyen T. Tran' 'Duc Son Pham']"
] |
cs.LG | null | 1206.4169 | null | null | http://arxiv.org/pdf/1206.4169v1 | 2012-06-19T10:26:45Z | 2012-06-19T10:26:45Z | Clustered Bandits | We consider a multi-armed bandit setting that is inspired by real-world
applications in e-commerce. In our setting, there are a few types of users,
each with a specific response to the different arms. When a user enters the
system, his type is unknown to the decision maker. The decision maker can
either treat each user separately ignoring the previously observed users, or
can attempt to take advantage of knowing that only few types exist and cluster
the users according to their response to the arms. We devise algorithms that
combine the usual exploration-exploitation tradeoff with clustering of users
and demonstrate the value of clustering. In the process of developing
algorithms for the clustered setting, we propose and analyze simple algorithms
for the setup where a decision maker knows that a user belongs to one of few
types, but does not know which one.
| [
"['Loc Bui' 'Ramesh Johari' 'Shie Mannor']",
"Loc Bui, Ramesh Johari, Shie Mannor"
] |
cs.NA cs.LG | null | 1206.4481 | null | null | http://arxiv.org/pdf/1206.4481v2 | 2012-09-10T13:01:47Z | 2012-06-20T12:49:48Z | Parsimonious Mahalanobis Kernel for the Classification of High
Dimensional Data | The classification of high dimensional data with kernel methods is considered
in this article. Exploit- ing the emptiness property of high dimensional
spaces, a kernel based on the Mahalanobis distance is proposed. The computation
of the Mahalanobis distance requires the inversion of a covariance matrix. In
high dimensional spaces, the estimated covariance matrix is ill-conditioned and
its inversion is unstable or impossible. Using a parsimonious statistical
model, namely the High Dimensional Discriminant Analysis model, the specific
signal and noise subspaces are estimated for each considered class making the
inverse of the class specific covariance matrix explicit and stable, leading to
the definition of a parsimonious Mahalanobis kernel. A SVM based framework is
used for selecting the hyperparameters of the parsimonious Mahalanobis kernel
by optimizing the so-called radius-margin bound. Experimental results on three
high dimensional data sets show that the proposed kernel is suitable for
classifying high dimensional data, providing better classification accuracies
than the conventional Gaussian kernel.
| [
"M. Fauvel, A. Villa, J. Chanussot and J. A. Benediktsson",
"['M. Fauvel' 'A. Villa' 'J. Chanussot' 'J. A. Benediktsson']"
] |
cs.LG stat.ML | null | 1206.4560 | null | null | http://arxiv.org/pdf/1206.4560v1 | 2012-06-18T15:11:22Z | 2012-06-18T15:11:22Z | Residual Component Analysis: Generalising PCA for more flexible
inference in linear-Gaussian models | Probabilistic principal component analysis (PPCA) seeks a low dimensional
representation of a data set in the presence of independent spherical Gaussian
noise. The maximum likelihood solution for the model is an eigenvalue problem
on the sample covariance matrix. In this paper we consider the situation where
the data variance is already partially explained by other actors, for example
sparse conditional dependencies between the covariates, or temporal
correlations leaving some residual variance. We decompose the residual variance
into its components through a generalised eigenvalue problem, which we call
residual component analysis (RCA). We explore a range of new algorithms that
arise from the framework, including one that factorises the covariance of a
Gaussian density into a low-rank and a sparse-inverse component. We illustrate
the ideas on the recovery of a protein-signaling network, a gene expression
time-series data set and the recovery of the human skeleton from motion capture
3-D cloud data.
| [
"Alfredo Kalaitzis (University of Sheffield), Neil Lawrence (University\n of Sheffield)",
"['Alfredo Kalaitzis' 'Neil Lawrence']"
] |
cs.LG stat.ML | null | 1206.4599 | null | null | http://arxiv.org/pdf/1206.4599v1 | 2012-06-18T14:39:39Z | 2012-06-18T14:39:39Z | A Unified Robust Classification Model | A wide variety of machine learning algorithms such as support vector machine
(SVM), minimax probability machine (MPM), and Fisher discriminant analysis
(FDA), exist for binary classification. The purpose of this paper is to provide
a unified classification model that includes the above models through a robust
optimization approach. This unified model has several benefits. One is that the
extensions and improvements intended for SVM become applicable to MPM and FDA,
and vice versa. Another benefit is to provide theoretical results to above
learning methods at once by dealing with the unified model. We give a
statistical interpretation of the unified classification model and propose a
non-convex optimization algorithm that can be applied to non-convex variants of
existing learning methods.
| [
"['Akiko Takeda' 'Hiroyuki Mitsugi' 'Takafumi Kanamori']",
"Akiko Takeda (Keio University), Hiroyuki Mitsugi (Keio University),\n Takafumi Kanamori (Nagoya University)"
] |
cs.LG stat.ML | null | 1206.4600 | null | null | http://arxiv.org/pdf/1206.4600v1 | 2012-06-18T14:40:38Z | 2012-06-18T14:40:38Z | Bayesian Nonexhaustive Learning for Online Discovery and Modeling of
Emerging Classes | We present a framework for online inference in the presence of a
nonexhaustively defined set of classes that incorporates supervised
classification with class discovery and modeling. A Dirichlet process prior
(DPP) model defined over class distributions ensures that both known and
unknown class distributions originate according to a common base distribution.
In an attempt to automatically discover potentially interesting class
formations, the prior model is coupled with a suitably chosen data model, and
sequential Monte Carlo sampling is used to perform online inference. Our
research is driven by a biodetection application, where a new class of pathogen
may suddenly appear, and the rapid increase in the number of samples
originating from this class indicates the onset of an outbreak.
| [
"['Murat Dundar' 'Ferit Akova' 'Alan Qi' 'Bartek Rajwa']",
"Murat Dundar (IUPUI), Ferit Akova (IUPUI), Alan Qi (Purdue), Bartek\n Rajwa (Purdue)"
] |
cs.LG stat.ML | null | 1206.4601 | null | null | http://arxiv.org/pdf/1206.4601v1 | 2012-06-18T14:40:55Z | 2012-06-18T14:40:55Z | Convex Multitask Learning with Flexible Task Clusters | Traditionally, multitask learning (MTL) assumes that all the tasks are
related. This can lead to negative transfer when tasks are indeed incoherent.
Recently, a number of approaches have been proposed that alleviate this problem
by discovering the underlying task clusters or relationships. However, they are
limited to modeling these relationships at the task level, which may be
restrictive in some applications. In this paper, we propose a novel MTL
formulation that captures task relationships at the feature-level. Depending on
the interactions among tasks and features, the proposed method construct
different task clusters for different features, without even the need of
pre-specifying the number of clusters. Computationally, the proposed
formulation is strongly convex, and can be efficiently solved by accelerated
proximal methods. Experiments are performed on a number of synthetic and
real-world data sets. Under various degrees of task relationships, the accuracy
of the proposed method is consistently among the best. Moreover, the
feature-specific task clusters obtained agree with the known/plausible task
structures of the data.
| [
"['Wenliang Zhong' 'James Kwok']",
"Wenliang Zhong (HKUST), James Kwok (HKUST)"
] |
cs.NA cs.LG stat.ML | null | 1206.4602 | null | null | http://arxiv.org/pdf/1206.4602v1 | 2012-06-18T14:41:11Z | 2012-06-18T14:41:11Z | Quasi-Newton Methods: A New Direction | Four decades after their invention, quasi-Newton methods are still state of
the art in unconstrained numerical optimization. Although not usually
interpreted thus, these are learning algorithms that fit a local quadratic
approximation to the objective function. We show that many, including the most
popular, quasi-Newton methods can be interpreted as approximations of Bayesian
linear regression under varying prior assumptions. This new notion elucidates
some shortcomings of classical algorithms, and lights the way to a novel
nonparametric quasi-Newton method, which is able to make more efficient use of
available information at computational cost similar to its predecessors.
| [
"['Philipp Hennig' 'Martin Kiefel']",
"Philipp Hennig (MPI Intelligent Systems), Martin Kiefel (MPI for\n Intelligent Systems)"
] |
cs.LG cs.AI | null | 1206.4604 | null | null | http://arxiv.org/pdf/1206.4604v1 | 2012-06-18T14:42:16Z | 2012-06-18T14:42:16Z | Learning the Experts for Online Sequence Prediction | Online sequence prediction is the problem of predicting the next element of a
sequence given previous elements. This problem has been extensively studied in
the context of individual sequence prediction, where no prior assumptions are
made on the origin of the sequence. Individual sequence prediction algorithms
work quite well for long sequences, where the algorithm has enough time to
learn the temporal structure of the sequence. However, they might give poor
predictions for short sequences. A possible remedy is to rely on the general
model of prediction with expert advice, where the learner has access to a set
of $r$ experts, each of which makes its own predictions on the sequence. It is
well known that it is possible to predict almost as well as the best expert if
the sequence length is order of $\log(r)$. But, without firm prior knowledge on
the problem, it is not clear how to choose a small set of {\em good} experts.
In this paper we describe and analyze a new algorithm that learns a good set of
experts using a training set of previously observed sequences. We demonstrate
the merits of our approach by applying it on the task of click prediction on
the web.
| [
"['Elad Eban' 'Aharon Birnbaum' 'Shai Shalev-Shwartz' 'Amir Globerson']",
"Elad Eban (Hebrew University), Aharon Birnbaum (Hebrew University),\n Shai Shalev-Shwartz (Hebrew University), Amir Globerson (Hebrew University)"
] |
cs.LG cs.AI stat.ML | null | 1206.4606 | null | null | http://arxiv.org/pdf/1206.4606v1 | 2012-06-18T14:43:42Z | 2012-06-18T14:43:42Z | TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing
Multiple Ratings | This paper revisits the problem of analyzing multiple ratings given by
different judges. Different from previous work that focuses on distilling the
true labels from noisy crowdsourcing ratings, we emphasize gaining diagnostic
insights into our in-house well-trained judges. We generalize the well-known
DawidSkene model (Dawid & Skene, 1979) to a spectrum of probabilistic models
under the same "TrueLabel + Confusion" paradigm, and show that our proposed
hierarchical Bayesian model, called HybridConfusion, consistently outperforms
DawidSkene on both synthetic and real-world data sets.
| [
"Chao Liu (Tencent Inc.), Yi-Min Wang (Microsoft Research)",
"['Chao Liu' 'Yi-Min Wang']"
] |
cs.LG stat.ML | null | 1206.4607 | null | null | http://arxiv.org/pdf/1206.4607v1 | 2012-06-18T14:44:09Z | 2012-06-18T14:44:09Z | Distributed Tree Kernels | In this paper, we propose the distributed tree kernels (DTK) as a novel
method to reduce time and space complexity of tree kernels. Using a linear
complexity algorithm to compute vectors for trees, we embed feature spaces of
tree fragments in low-dimensional spaces where the kernel computation is
directly done with dot product. We show that DTKs are faster, correlate with
tree kernels, and obtain a statistically similar performance in two natural
language processing tasks.
| [
"['Fabio Massimo Zanzotto' \"Lorenzo Dell'Arciprete\"]",
"Fabio Massimo Zanzotto (University of Rome-Tor Vergata), Lorenzo\n Dell'Arciprete (University of Rome-Tor Vergata)"
] |
cs.LG cs.DS cs.NA stat.ML | null | 1206.4608 | null | null | http://arxiv.org/pdf/1206.4608v1 | 2012-06-18T14:44:28Z | 2012-06-18T14:44:28Z | A Hybrid Algorithm for Convex Semidefinite Optimization | We present a hybrid algorithm for optimizing a convex, smooth function over
the cone of positive semidefinite matrices. Our algorithm converges to the
global optimal solution and can be used to solve general large-scale
semidefinite programs and hence can be readily applied to a variety of machine
learning problems. We show experimental results on three machine learning
problems (matrix completion, metric learning, and sparse PCA) . Our approach
outperforms state-of-the-art algorithms.
| [
"['Soeren Laue']",
"Soeren Laue (Friedrich-Schiller-University)"
] |
cs.CV cs.LG stat.ML | null | 1206.4609 | null | null | http://arxiv.org/pdf/1206.4609v1 | 2012-06-18T14:45:17Z | 2012-06-18T14:45:17Z | On multi-view feature learning | Sparse coding is a common approach to learning local features for object
recognition. Recently, there has been an increasing interest in learning
features from spatio-temporal, binocular, or other multi-observation data,
where the goal is to encode the relationship between images rather than the
content of a single image. We provide an analysis of multi-view feature
learning, which shows that hidden variables encode transformations by detecting
rotation angles in the eigenspaces shared among multiple image warps. Our
analysis helps explain recent experimental results showing that
transformation-specific features emerge when training complex cell models on
videos. Our analysis also shows that transformation-invariant features can
emerge as a by-product of learning representations of transformations.
| [
"Roland Memisevic (University of Frankfurt)",
"['Roland Memisevic']"
] |
cs.LG cs.CV stat.ML | null | 1206.4610 | null | null | http://arxiv.org/pdf/1206.4610v1 | 2012-06-18T14:45:37Z | 2012-06-18T14:45:37Z | Manifold Relevance Determination | In this paper we present a fully Bayesian latent variable model which
exploits conditional nonlinear(in)-dependence structures to learn an efficient
latent representation. The latent space is factorized to represent shared and
private information from multiple views of the data. In contrast to previous
approaches, we introduce a relaxation to the discrete segmentation and allow
for a "softly" shared latent space. Further, Bayesian techniques allow us to
automatically estimate the dimensionality of the latent spaces. The model is
capable of capturing structure underlying extremely high dimensional spaces.
This is illustrated by modelling unprocessed images with tenths of thousands of
pixels. This also allows us to directly generate novel images from the trained
model by sampling from the discovered latent spaces. We also demonstrate the
model by prediction of human pose in an ambiguous setting. Our Bayesian
framework allows us to perform disambiguation in a principled manner by
including latent space priors which incorporate the dynamic nature of the data.
| [
"Andreas Damianou (University of Sheffield), Carl Ek (KTH), Michalis\n Titsias (University of Oxford), Neil Lawrence (University of Sheffield)",
"['Andreas Damianou' 'Carl Ek' 'Michalis Titsias' 'Neil Lawrence']"
] |
cs.LG stat.ML | null | 1206.4611 | null | null | http://arxiv.org/pdf/1206.4611v1 | 2012-06-18T15:00:07Z | 2012-06-18T15:00:07Z | A Convex Feature Learning Formulation for Latent Task Structure
Discovery | This paper considers the multi-task learning problem and in the setting where
some relevant features could be shared across few related tasks. Most of the
existing methods assume the extent to which the given tasks are related or
share a common feature space to be known apriori. In real-world applications
however, it is desirable to automatically discover the groups of related tasks
that share a feature space. In this paper we aim at searching the exponentially
large space of all possible groups of tasks that may share a feature space. The
main contribution is a convex formulation that employs a graph-based
regularizer and simultaneously discovers few groups of related tasks, having
close-by task parameters, as well as the feature space shared within each
group. The regularizer encodes an important structure among the groups of tasks
leading to an efficient algorithm for solving it: if there is no feature space
under which a group of tasks has close-by task parameters, then there does not
exist such a feature space for any of its supersets. An efficient active set
algorithm that exploits this simplification and performs a clever search in the
exponentially large space is presented. The algorithm is guaranteed to solve
the proposed formulation (within some precision) in a time polynomial in the
number of groups of related tasks discovered. Empirical results on benchmark
datasets show that the proposed formulation achieves good generalization and
outperforms state-of-the-art multi-task learning algorithms in some cases.
| [
"['Pratik Jawanpuria' 'J. Saketha Nath']",
"Pratik Jawanpuria (IIT Bombay), J. Saketha Nath (IIT Bombay)"
] |
cs.LG | null | 1206.4612 | null | null | http://arxiv.org/pdf/1206.4612v1 | 2012-06-18T15:00:20Z | 2012-06-18T15:00:20Z | Exact Soft Confidence-Weighted Learning | In this paper, we propose a new Soft Confidence-Weighted (SCW) online
learning scheme, which enables the conventional confidence-weighted learning
method to handle non-separable cases. Unlike the previous confidence-weighted
learning algorithms, the proposed soft confidence-weighted learning method
enjoys all the four salient properties: (i) large margin training, (ii)
confidence weighting, (iii) capability to handle non-separable data, and (iv)
adaptive margin. Our experimental results show that the proposed SCW algorithms
significantly outperform the original CW algorithm. When comparing with a
variety of state-of-the-art algorithms (including AROW, NAROW and NHERD), we
found that SCW generally achieves better or at least comparable predictive
accuracy, but enjoys significant advantage of computational efficiency (i.e.,
smaller number of updates and lower time cost).
| [
"['Jialei Wang' 'Peilin Zhao' 'Steven C. H. Hoi']",
"Jialei Wang (NTU), Peilin Zhao (NTU), Steven C.H. Hoi (NTU)"
] |
cs.AI cs.LG stat.ML | null | 1206.4613 | null | null | http://arxiv.org/pdf/1206.4613v1 | 2012-06-18T15:00:40Z | 2012-06-18T15:00:40Z | Near-Optimal BRL using Optimistic Local Transitions | Model-based Bayesian Reinforcement Learning (BRL) allows a found
formalization of the problem of acting optimally while facing an unknown
environment, i.e., avoiding the exploration-exploitation dilemma. However,
algorithms explicitly addressing BRL suffer from such a combinatorial explosion
that a large body of work relies on heuristic algorithms. This paper introduces
BOLT, a simple and (almost) deterministic heuristic algorithm for BRL which is
optimistic about the transition function. We analyze BOLT's sample complexity,
and show that under certain parameters, the algorithm is near-optimal in the
Bayesian sense with high probability. Then, experimental results highlight the
key differences of this method compared to previous work.
| [
"['Mauricio Araya' 'Olivier Buffet' 'Vincent Thomas']",
"Mauricio Araya (LORIA/INRIA), Olivier Buffet (LORIA/INRIA), Vincent\n Thomas (LORIA/INRIA)"
] |
cs.LG stat.ML | null | 1206.4614 | null | null | http://arxiv.org/pdf/1206.4614v1 | 2012-06-18T15:01:43Z | 2012-06-18T15:01:43Z | Information-theoretic Semi-supervised Metric Learning via Entropy
Regularization | We propose a general information-theoretic approach called Seraph
(SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric
learning that does not rely upon the manifold assumption. Given the probability
parameterized by a Mahalanobis distance, we maximize the entropy of that
probability on labeled data and minimize it on unlabeled data following entropy
regularization, which allows the supervised and unsupervised parts to be
integrated in a natural and meaningful way. Furthermore, Seraph is regularized
by encouraging a low-rank projection induced from the metric. The optimization
of Seraph is solved efficiently and stably by an EM-like scheme with the
analytical E-Step and convex M-Step. Experiments demonstrate that Seraph
compares favorably with many well-known global and local metric learning
methods.
| [
"Gang Niu (Tokyo Institute of Technology), Bo Dai (Purdue University),\n Makoto Yamada (Tokyo Institute of Technology), Masashi Sugiyama (Tokyo\n Institute of Technology)",
"['Gang Niu' 'Bo Dai' 'Makoto Yamada' 'Masashi Sugiyama']"
] |
stat.ME cs.LG math.ST stat.TH | null | 1206.4615 | null | null | http://arxiv.org/pdf/1206.4615v1 | 2012-06-18T15:01:58Z | 2012-06-18T15:01:58Z | Levy Measure Decompositions for the Beta and Gamma Processes | We develop new representations for the Levy measures of the beta and gamma
processes. These representations are manifested in terms of an infinite sum of
well-behaved (proper) beta and gamma distributions. Further, we demonstrate how
these infinite sums may be truncated in practice, and explicitly characterize
truncation errors. We also perform an analysis of the characteristics of
posterior distributions, based on the proposed decompositions. The
decompositions provide new insights into the beta and gamma processes (and
their generalizations), and we demonstrate how the proposed representation
unifies some properties of the two. This paper is meant to provide a rigorous
foundation for and new perspectives on Levy processes, as these are of
increasing importance in machine learning.
| [
"Yingjian Wang (Duke University), Lawrence Carin (Duke University)",
"['Yingjian Wang' 'Lawrence Carin']"
] |
stat.AP cs.LG stat.ML | null | 1206.4616 | null | null | http://arxiv.org/pdf/1206.4616v1 | 2012-06-18T15:02:12Z | 2012-06-18T15:02:12Z | A Hierarchical Dirichlet Process Model with Multiple Levels of
Clustering for Human EEG Seizure Modeling | Driven by the multi-level structure of human intracranial
electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a
new variant of a hierarchical Dirichlet Process---the multi-level clustering
hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters
datasets on multiple levels. Our seizure dataset contains brain activity
recorded in typically more than a hundred individual channels for each seizure
of each patient. The MLC-HDP model clusters over channels-types, seizure-types,
and patient-types simultaneously. We describe this model and its implementation
in detail. We also present the results of a simulation study comparing the
MLC-HDP to a similar model, the Nested Dirichlet Process and finally
demonstrate the MLC-HDP's use in modeling seizures across multiple patients. We
find the MLC-HDP's clustering to be comparable to independent human physician
clusterings. To our knowledge, the MLC-HDP model is the first in the epilepsy
literature capable of clustering seizures within and between patients.
| [
"Drausin Wulsin (University of Pennsylvania), Shane Jensen (University\n of Pennsylvania), Brian Litt (University of Pennsylvania)",
"['Drausin Wulsin' 'Shane Jensen' 'Brian Litt']"
] |
cs.LG cs.AI stat.ML | null | 1206.4617 | null | null | http://arxiv.org/pdf/1206.4617v1 | 2012-06-18T15:02:28Z | 2012-06-18T15:02:28Z | Continuous Inverse Optimal Control with Locally Optimal Examples | Inverse optimal control, also known as inverse reinforcement learning, is the
problem of recovering an unknown reward function in a Markov decision process
from expert demonstrations of the optimal policy. We introduce a probabilistic
inverse optimal control algorithm that scales gracefully with task
dimensionality, and is suitable for large, continuous domains where even
computing a full policy is impractical. By using a local approximation of the
reward function, our method can also drop the assumption that the
demonstrations are globally optimal, requiring only local optimality. This
allows it to learn from examples that are unsuitable for prior methods.
| [
"Sergey Levine (Stanford University), Vladlen Koltun (Stanford\n University)",
"['Sergey Levine' 'Vladlen Koltun']"
] |
cs.LG stat.ML | null | 1206.4618 | null | null | http://arxiv.org/pdf/1206.4618v1 | 2012-06-18T15:03:10Z | 2012-06-18T15:03:10Z | Compact Hyperplane Hashing with Bilinear Functions | Hyperplane hashing aims at rapidly searching nearest points to a hyperplane,
and has shown practical impact in scaling up active learning with SVMs.
Unfortunately, the existing randomized methods need long hash codes to achieve
reasonable search accuracy and thus suffer from reduced search speed and large
memory overhead. To this end, this paper proposes a novel hyperplane hashing
technique which yields compact hash codes. The key idea is the bilinear form of
the proposed hash functions, which leads to higher collision probability than
the existing hyperplane hash functions when using random projections. To
further increase the performance, we propose a learning based framework in
which the bilinear functions are directly learned from the data. This results
in short yet discriminative codes, and also boosts the search performance over
the random projection based solutions. Large-scale active learning experiments
carried out on two datasets with up to one million samples demonstrate the
overall superiority of the proposed approach.
| [
"Wei Liu (Columbia University), Jun Wang (IBM T. J. Watson Research\n Center), Yadong Mu (Columbia University), Sanjiv Kumar (Google), Shih-Fu\n Chang (Columbia University)",
"['Wei Liu' 'Jun Wang' 'Yadong Mu' 'Sanjiv Kumar' 'Shih-Fu Chang']"
] |
cs.LG | null | 1206.4619 | null | null | http://arxiv.org/pdf/1206.4619v1 | 2012-06-18T15:04:39Z | 2012-06-18T15:04:39Z | Inductive Kernel Low-rank Decomposition with Priors: A Generalized
Nystrom Method | Low-rank matrix decomposition has gained great popularity recently in scaling
up kernel methods to large amounts of data. However, some limitations could
prevent them from working effectively in certain domains. For example, many
existing approaches are intrinsically unsupervised, which does not incorporate
side information (e.g., class labels) to produce task specific decompositions;
also, they typically work "transductively", i.e., the factorization does not
generalize to new samples, so the complete factorization needs to be recomputed
when new samples become available. To solve these problems, in this paper we
propose an"inductive"-flavored method for low-rank kernel decomposition with
priors. We achieve this by generalizing the Nystr\"om method in a novel way. On
the one hand, our approach employs a highly flexible, nonparametric structure
that allows us to generalize the low-rank factors to arbitrarily new samples;
on the other hand, it has linear time and space complexities, which can be
orders of magnitudes faster than existing approaches and renders great
efficiency in learning a low-rank kernel decomposition. Empirical results
demonstrate the efficacy and efficiency of the proposed method.
| [
"Kai Zhang (Siemens), Liang Lan (temple university), Jun Liu (Siemens),\n andreas Rauber (TU Wien), Fabian Moerchen (Siemens Corporate Research and\n Technology)",
"['Kai Zhang' 'Liang Lan' 'Jun Liu' 'andreas Rauber' 'Fabian Moerchen']"
] |
cs.LG stat.ML | null | 1206.4620 | null | null | http://arxiv.org/pdf/1206.4620v1 | 2012-06-18T15:04:54Z | 2012-06-18T15:04:54Z | Improved Information Gain Estimates for Decision Tree Induction | Ensembles of classification and regression trees remain popular machine
learning methods because they define flexible non-parametric models that
predict well and are computationally efficient both during training and
testing. During induction of decision trees one aims to find predicates that
are maximally informative about the prediction target. To select good
predicates most approaches estimate an information-theoretic scoring function,
the information gain, both for classification and regression problems. We point
out that the common estimation procedures are biased and show that by replacing
them with improved estimators of the discrete and the differential entropy we
can obtain better decision trees. In effect our modifications yield improved
predictive performance and are simple to implement in any decision tree code.
| [
"['Sebastian Nowozin']",
"Sebastian Nowozin (Microsoft Research Cambridge)"
] |
cs.LG | null | 1206.4621 | null | null | http://arxiv.org/pdf/1206.4621v1 | 2012-06-18T15:05:32Z | 2012-06-18T15:05:32Z | Path Integral Policy Improvement with Covariance Matrix Adaptation | There has been a recent focus in reinforcement learning on addressing
continuous state and action problems by optimizing parameterized policies. PI2
is a recent example of this approach. It combines a derivation from first
principles of stochastic optimal control with tools from statistical estimation
theory. In this paper, we consider PI2 as a member of the wider family of
methods which share the concept of probability-weighted averaging to
iteratively update parameters to optimize a cost function. We compare PI2 to
other members of the same family - Cross-Entropy Methods and CMAES - at the
conceptual level and in terms of performance. The comparison suggests the
derivation of a novel algorithm which we call PI2-CMA for "Path Integral Policy
Improvement with Covariance Matrix Adaptation". PI2-CMA's main advantage is
that it determines the magnitude of the exploration noise automatically.
| [
"Freek Stulp (Ecole Nationale Superieure de Techniques Avancees),\n Olivier Sigaud (Universite Pierre et Marie Curie)",
"['Freek Stulp' 'Olivier Sigaud']"
] |
cs.LG cs.IR stat.ML | null | 1206.4622 | null | null | http://arxiv.org/pdf/1206.4622v1 | 2012-06-18T15:05:52Z | 2012-06-18T15:05:52Z | A Graphical Model Formulation of Collaborative Filtering Neighbourhood
Methods with Fast Maximum Entropy Training | Item neighbourhood methods for collaborative filtering learn a weighted graph
over the set of items, where each item is connected to those it is most similar
to. The prediction of a user's rating on an item is then given by that rating
of neighbouring items, weighted by their similarity. This paper presents a new
neighbourhood approach which we call item fields, whereby an undirected
graphical model is formed over the item graph. The resulting prediction rule is
a simple generalization of the classical approaches, which takes into account
non-local information in the graph, allowing its best results to be obtained
when using drastically fewer edges than other neighbourhood approaches. A fast
approximate maximum entropy training method based on the Bethe approximation is
presented, which uses a simple gradient ascent procedure. When using
precomputed sufficient statistics on the Movielens datasets, our method is
faster than maximum likelihood approaches by two orders of magnitude.
| [
"['Aaron Defazio' 'Tiberio Caetano']",
"Aaron Defazio (ANU), Tiberio Caetano (NICTA and Australian National\n University)"
] |
cs.LG stat.ML | null | 1206.4623 | null | null | http://arxiv.org/pdf/1206.4623v1 | 2012-06-18T15:06:34Z | 2012-06-18T15:06:34Z | On the Size of the Online Kernel Sparsification Dictionary | We analyze the size of the dictionary constructed from online kernel
sparsification, using a novel formula that expresses the expected determinant
of the kernel Gram matrix in terms of the eigenvalues of the covariance
operator. Using this formula, we are able to connect the cardinality of the
dictionary with the eigen-decay of the covariance operator. In particular, we
show that under certain technical conditions, the size of the dictionary will
always grow sub-linearly in the number of data points, and, as a consequence,
the kernel linear regressor constructed from the resulting dictionary is
consistent.
| [
"Yi Sun (IDSIA), Faustino Gomez (IDSIA), Juergen Schmidhuber (IDSIA)",
"['Yi Sun' 'Faustino Gomez' 'Juergen Schmidhuber']"
] |
cs.LG stat.ML | null | 1206.4624 | null | null | http://arxiv.org/pdf/1206.4624v1 | 2012-06-18T15:06:49Z | 2012-06-18T15:06:49Z | Robust Multiple Manifolds Structure Learning | We present a robust multiple manifolds structure learning (RMMSL) scheme to
robustly estimate data structures under the multiple low intrinsic dimensional
manifolds assumption. In the local learning stage, RMMSL efficiently estimates
local tangent space by weighted low-rank matrix factorization. In the global
learning stage, we propose a robust manifold clustering method based on local
structure learning results. The proposed clustering method is designed to get
the flattest manifolds clusters by introducing a novel curved-level similarity
function. Our approach is evaluated and compared to state-of-the-art methods on
synthetic data, handwritten digit images, human motion capture data and
motorbike videos. We demonstrate the effectiveness of the proposed approach,
which yields higher clustering accuracy, and produces promising results for
challenging tasks of human motion segmentation and motion flow learning from
videos.
| [
"['Dian Gong' 'Xuemei Zhao' 'Gerard Medioni']",
"Dian Gong (Univ. of Southern California), Xuemei Zhao (Univ of\n Southern California), Gerard Medioni (University of Southern California)"
] |
cs.LG | null | 1206.4625 | null | null | http://arxiv.org/pdf/1206.4625v1 | 2012-06-18T15:07:04Z | 2012-06-18T15:07:04Z | Optimizing F-measure: A Tale of Two Approaches | F-measures are popular performance metrics, particularly for tasks with
imbalanced data sets. Algorithms for learning to maximize F-measures follow two
approaches: the empirical utility maximization (EUM) approach learns a
classifier having optimal performance on training data, while the
decision-theoretic approach learns a probabilistic model and then predicts
labels with maximum expected F-measure. In this paper, we investigate the
theoretical justifications and connections for these two approaches, and we
study the conditions under which one approach is preferable to the other using
synthetic and real datasets. Given accurate models, our results suggest that
the two approaches are asymptotically equivalent given large training and test
sets. Nevertheless, empirically, the EUM approach appears to be more robust
against model misspecification, and given a good model, the decision-theoretic
approach appears to be better for handling rare classes and a common domain
adaptation scenario.
| [
"['Ye Nan' 'Kian Ming Chai' 'Wee Sun Lee' 'Hai Leong Chieu']",
"Ye Nan (NUS), Kian Ming Chai (DSO National Laboratories), Wee Sun Lee\n (NUS), Hai Leong Chieu (DSO National Laboratories)"
] |
cs.CE cs.LG q-fin.PM | null | 1206.4626 | null | null | http://arxiv.org/pdf/1206.4626v1 | 2012-06-18T15:07:23Z | 2012-06-18T15:07:23Z | On-Line Portfolio Selection with Moving Average Reversion | On-line portfolio selection has attracted increasing interests in machine
learning and AI communities recently. Empirical evidences show that stock's
high and low prices are temporary and stock price relatives are likely to
follow the mean reversion phenomenon. While the existing mean reversion
strategies are shown to achieve good empirical performance on many real
datasets, they often make the single-period mean reversion assumption, which is
not always satisfied in some real datasets, leading to poor performance when
the assumption does not hold. To overcome the limitation, this article proposes
a multiple-period mean reversion, or so-called Moving Average Reversion (MAR),
and a new on-line portfolio selection strategy named "On-Line Moving Average
Reversion" (OLMAR), which exploits MAR by applying powerful online learning
techniques. From our empirical results, we found that OLMAR can overcome the
drawback of existing mean reversion algorithms and achieve significantly better
results, especially on the datasets where the existing mean reversion
algorithms failed. In addition to superior trading performance, OLMAR also runs
extremely fast, further supporting its practical applicability to a wide range
of applications.
| [
"Bin Li (NTU), Steven C.H. Hoi (NTU)",
"['Bin Li' 'Steven C. H. Hoi']"
] |
cs.LG stat.ML | null | 1206.4627 | null | null | http://arxiv.org/pdf/1206.4627v1 | 2012-06-18T15:07:39Z | 2012-06-18T15:07:39Z | Convergence Rates of Biased Stochastic Optimization for Learning Sparse
Ising Models | We study the convergence rate of stochastic optimization of exact (NP-hard)
objectives, for which only biased estimates of the gradient are available. We
motivate this problem in the context of learning the structure and parameters
of Ising models. We first provide a convergence-rate analysis of deterministic
errors for forward-backward splitting (FBS). We then extend our analysis to
biased stochastic errors, by first characterizing a family of samplers and
providing a high probability bound that allows understanding not only FBS, but
also proximal gradient (PG) methods. We derive some interesting conclusions:
FBS requires only a logarithmically increasing number of random samples in
order to converge (although at a very low rate); the required number of random
samples is the same for the deterministic and the biased stochastic setting for
FBS and basic PG; accelerated PG is not guaranteed to converge in the biased
stochastic setting.
| [
"Jean Honorio (Stony Brook University)",
"['Jean Honorio']"
] |
cs.LG stat.ML | null | 1206.4628 | null | null | http://arxiv.org/pdf/1206.4628v1 | 2012-06-18T15:07:55Z | 2012-06-18T15:07:55Z | Robust PCA in High-dimension: A Deterministic Approach | We consider principal component analysis for contaminated data-set in the
high dimensional regime, where the dimensionality of each observation is
comparable or even more than the number of observations. We propose a
deterministic high-dimensional robust PCA algorithm which inherits all
theoretical properties of its randomized counterpart, i.e., it is tractable,
robust to contaminated points, easily kernelizable, asymptotic consistent and
achieves maximal robustness -- a breakdown point of 50%. More importantly, the
proposed method exhibits significantly better computational efficiency, which
makes it suitable for large-scale real applications.
| [
"Jiashi Feng (NUS), Huan Xu (NUS), Shuicheng Yan (NUS)",
"['Jiashi Feng' 'Huan Xu' 'Shuicheng Yan']"
] |
cs.LG | null | 1206.4629 | null | null | http://arxiv.org/pdf/1206.4629v1 | 2012-06-18T15:08:22Z | 2012-06-18T15:08:22Z | Multiple Kernel Learning from Noisy Labels by Stochastic Programming | We study the problem of multiple kernel learning from noisy labels. This is
in contrast to most of the previous studies on multiple kernel learning that
mainly focus on developing efficient algorithms and assume perfectly labeled
training examples. Directly applying the existing multiple kernel learning
algorithms to noisily labeled examples often leads to suboptimal performance
due to the incorrect class assignments. We address this challenge by casting
multiple kernel learning from noisy labels into a stochastic programming
problem, and presenting a minimax formulation. We develop an efficient
algorithm for solving the related convex-concave optimization problem with a
fast convergence rate of $O(1/T)$ where $T$ is the number of iterations.
Empirical studies on UCI data sets verify both the effectiveness of the
proposed framework and the efficiency of the proposed optimization algorithm.
| [
"['Tianbao Yang' 'Mehrdad Mahdavi' 'Rong Jin' 'Lijun Zhang' 'Yang Zhou']",
"Tianbao Yang (Michigan State University), Mehrdad Mahdavi (Michigan\n State University), Rong Jin (Michigan State University), Lijun Zhang\n (Michigan State University), Yang Zhou (Yahoo! Labs)"
] |
cs.LG | null | 1206.4630 | null | null | http://arxiv.org/pdf/1206.4630v1 | 2012-06-18T15:08:38Z | 2012-06-18T15:08:38Z | Efficient Decomposed Learning for Structured Prediction | Structured prediction is the cornerstone of several machine learning
applications. Unfortunately, in structured prediction settings with expressive
inter-variable interactions, exact inference-based learning algorithms, e.g.
Structural SVM, are often intractable. We present a new way, Decomposed
Learning (DecL), which performs efficient learning by restricting the inference
step to a limited part of the structured spaces. We provide characterizations
based on the structure, target parameters, and gold labels, under which DecL is
equivalent to exact learning. We then show that in real world settings, where
our theoretical assumptions may not completely hold, DecL-based algorithms are
significantly more efficient and as accurate as exact learning.
| [
"Rajhans Samdani (University of Illinois, U-C), Dan Roth (University of\n Illinois, U-C)",
"['Rajhans Samdani' 'Dan Roth']"
] |
cs.LG cs.CL cs.IR stat.ME stat.ML | null | 1206.4631 | null | null | http://arxiv.org/pdf/1206.4631v3 | 2014-07-28T03:02:39Z | 2012-06-18T15:11:38Z | A Poisson convolution model for characterizing topical content with word
frequency and exclusivity | An ongoing challenge in the analysis of document collections is how to
summarize content in terms of a set of inferred themes that can be interpreted
substantively in terms of topics. The current practice of parametrizing the
themes in terms of most frequent words limits interpretability by ignoring the
differential use of words across topics. We argue that words that are both
common and exclusive to a theme are more effective at characterizing topical
content. We consider a setting where professional editors have annotated
documents to a collection of topic categories, organized into a tree, in which
leaf-nodes correspond to the most specific topics. Each document is annotated
to multiple categories, at different levels of the tree. We introduce a
hierarchical Poisson convolution model to analyze annotated documents in this
setting. The model leverages the structure among categories defined by
professional editors to infer a clear semantic description for each topic in
terms of words that are both frequent and exclusive. We carry out a large
randomized experiment on Amazon Turk to demonstrate that topic summaries based
on the FREX score are more interpretable than currently established frequency
based summaries, and that the proposed model produces more efficient estimates
of exclusivity than with currently models. We also develop a parallelized
Hamiltonian Monte Carlo sampler that allows the inference to scale to millions
of documents.
| [
"Edoardo M Airoldi, Jonathan M Bischof",
"['Edoardo M Airoldi' 'Jonathan M Bischof']"
] |
null | null | 1206.4632 | null | null | http://arxiv.org/pdf/1206.4632v1 | 2012-06-18T15:12:01Z | 2012-06-18T15:12:01Z | A Complete Analysis of the l_1,p Group-Lasso | The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,infty} version have been studied in detail and efficient algorithms exist, there are still open questions regarding other l_{1,p} variants. We characterize conditions for solutions of the l_{1,p} Group-Lasso for all p-norms with 1 <= p <= infty, and we present a unified active set algorithm. For all p-norms, a highly efficient projected gradient algorithm is presented. This new algorithm enables us to compare the prediction performance of many variants of the Group-Lasso in a multi-task learning setting, where the aim is to solve many learning problems in parallel which are coupled via the Group-Lasso constraint. We conduct large-scale experiments on synthetic data and on two real-world data sets. In accordance with theoretical characterizations of the different norms we observe that the weak-coupling norms with p between 1.5 and 2 consistently outperform the strong-coupling norms with p >> 2. | [
"['Julia Vogt' 'Volker Roth']"
] |
cs.LG stat.ML | null | 1206.4633 | null | null | http://arxiv.org/pdf/1206.4633v1 | 2012-06-18T15:13:13Z | 2012-06-18T15:13:13Z | Fast Bounded Online Gradient Descent Algorithms for Scalable
Kernel-Based Online Learning | Kernel-based online learning has often shown state-of-the-art performance for
many online learning tasks. It, however, suffers from a major shortcoming, that
is, the unbounded number of support vectors, making it non-scalable and
unsuitable for applications with large-scale datasets. In this work, we study
the problem of bounded kernel-based online learning that aims to constrain the
number of support vectors by a predefined budget. Although several algorithms
have been proposed in literature, they are neither computationally efficient
due to their intensive budget maintenance strategy nor effective due to the use
of simple Perceptron algorithm. To overcome these limitations, we propose a
framework for bounded kernel-based online learning based on an online gradient
descent approach. We propose two efficient algorithms of bounded online
gradient descent (BOGD) for scalable kernel-based online learning: (i) BOGD by
maintaining support vectors using uniform sampling, and (ii) BOGD++ by
maintaining support vectors using non-uniform sampling. We present theoretical
analysis of regret bound for both algorithms, and found promising empirical
performance in terms of both efficacy and efficiency by comparing them to
several well-known algorithms for bounded kernel-based online learning on
large-scale datasets.
| [
"Peilin Zhao (NTU), Jialei Wang (NTU), Pengcheng Wu (NTU), Rong Jin\n (MSU), Steven C.H. Hoi (NTU)",
"['Peilin Zhao' 'Jialei Wang' 'Pengcheng Wu' 'Rong Jin' 'Steven C. H. Hoi']"
] |
cs.LG cs.GR stat.ML | 10.1587/transinf.E96.D.1134 | 1206.4634 | null | null | http://arxiv.org/abs/1206.4634v1 | 2012-06-18T15:14:24Z | 2012-06-18T15:14:24Z | Artist Agent: A Reinforcement Learning Approach to Automatic Stroke
Generation in Oriental Ink Painting | Oriental ink painting, called Sumi-e, is one of the most appealing painting
styles that has attracted artists around the world. Major challenges in
computer-based Sumi-e simulation are to abstract complex scene information and
draw smooth and natural brush strokes. To automatically find such strokes, we
propose to model the brush as a reinforcement learning agent, and learn desired
brush-trajectories by maximizing the sum of rewards in the policy search
framework. We also provide elaborate design of actions, states, and rewards
tailored for a Sumi-e agent. The effectiveness of our proposed approach is
demonstrated through simulated Sumi-e experiments.
| [
"['Ning Xie' 'Hirotaka Hachiya' 'Masashi Sugiyama']",
"Ning Xie (Tokyo Institute of Technology), Hirotaka Hachiya (Tokyo\n Institute of Technology), Masashi Sugiyama (Tokyo Institute of Technology)"
] |
cs.LG stat.ML | null | 1206.4635 | null | null | http://arxiv.org/pdf/1206.4635v1 | 2012-06-18T15:14:57Z | 2012-06-18T15:14:57Z | Deep Mixtures of Factor Analysers | An efficient way to learn deep density models that have many layers of latent
variables is to learn one layer at a time using a model that has only one layer
of latent variables. After learning each layer, samples from the posterior
distributions for that layer are used as training data for learning the next
layer. This approach is commonly used with Restricted Boltzmann Machines, which
are undirected graphical models with a single hidden layer, but it can also be
used with Mixtures of Factor Analysers (MFAs) which are directed graphical
models. In this paper, we present a greedy layer-wise learning algorithm for
Deep Mixtures of Factor Analysers (DMFAs). Even though a DMFA can be converted
to an equivalent shallow MFA by multiplying together the factor loading
matrices at different levels, learning and inference are much more efficient in
a DMFA and the sharing of each lower-level factor loading matrix by many
different higher level MFAs prevents overfitting. We demonstrate empirically
that DMFAs learn better density models than both MFAs and two types of
Restricted Boltzmann Machine on a wide variety of datasets.
| [
"Yichuan Tang (University of Toronto), Ruslan Salakhutdinov (University\n of Toronto), Geoffrey Hinton (University of Toronto)",
"['Yichuan Tang' 'Ruslan Salakhutdinov' 'Geoffrey Hinton']"
] |
cs.LG cs.AI cs.CV | null | 1206.4636 | null | null | http://arxiv.org/pdf/1206.4636v1 | 2012-06-18T15:15:13Z | 2012-06-18T15:15:13Z | Modeling Latent Variable Uncertainty for Loss-based Learning | We consider the problem of parameter estimation using weakly supervised
datasets, where a training sample consists of the input and a partially
specified annotation, which we refer to as the output. The missing information
in the annotation is modeled using latent variables. Previous methods
overburden a single distribution with two separate tasks: (i) modeling the
uncertainty in the latent variables during training; and (ii) making accurate
predictions for the output and the latent variables during testing. We propose
a novel framework that separates the demands of the two tasks using two
distributions: (i) a conditional distribution to model the uncertainty of the
latent variables for a given input-output pair; and (ii) a delta distribution
to predict the output and the latent variables for a given input. During
learning, we encourage agreement between the two distributions by minimizing a
loss-based dissimilarity coefficient. Our approach generalizes latent SVM in
two important ways: (i) it models the uncertainty over latent variables instead
of relying on a pointwise estimate; and (ii) it allows the use of loss
functions that depend on latent variables, which greatly increases its
applicability. We demonstrate the efficacy of our approach on two challenging
problems---object detection and action detection---using publicly available
datasets.
| [
"['M. Pawan Kumar' 'Ben Packer' 'Daphne Koller']",
"M. Pawan Kumar (Ecole Centrale Paris), Ben Packer (Stanford\n University), Daphne Koller (Stanford University)"
] |
cs.LG cs.CL stat.ML | null | 1206.4637 | null | null | http://arxiv.org/pdf/1206.4637v1 | 2012-06-18T15:15:28Z | 2012-06-18T15:15:28Z | Learning to Identify Regular Expressions that Describe Email Campaigns | This paper addresses the problem of inferring a regular expression from a
given set of strings that resembles, as closely as possible, the regular
expression that a human expert would have written to identify the language.
This is motivated by our goal of automating the task of postmasters of an email
service who use regular expressions to describe and blacklist email spam
campaigns. Training data contains batches of messages and corresponding regular
expressions that an expert postmaster feels confident to blacklist. We model
this task as a learning problem with structured output spaces and an
appropriate loss function, derive a decoder and the resulting optimization
problem, and a report on a case study conducted with an email service.
| [
"['Paul Prasse' 'Christoph Sawade' 'Niels Landwehr' 'Tobias Scheffer']",
"Paul Prasse (University of Potsdam), Christoph Sawade (University of\n Potsdam), Niels Landwehr (University of Potsdam), Tobias Scheffer (University\n of Potsdam)"
] |
null | null | 1206.4638 | null | null | http://arxiv.org/pdf/1206.4638v1 | 2012-06-18T15:16:28Z | 2012-06-18T15:16:28Z | Efficient Euclidean Projections onto the Intersection of Norm Balls | Using sparse-inducing norms to learn robust models has received increasing attention from many fields for its attractive properties. Projection-based methods have been widely applied to learning tasks constrained by such norms. As a key building block of these methods, an efficient operator for Euclidean projection onto the intersection of $ell_1$ and $ell_{1,q}$ norm balls $(q=2text{or}infty)$ is proposed in this paper. We prove that the projection can be reduced to finding the root of an auxiliary function which is piecewise smooth and monotonic. Hence, a bisection algorithm is sufficient to solve the problem. We show that the time complexity of our solution is $O(n+glog g)$ for $q=2$ and $O(nlog n)$ for $q=infty$, where $n$ is the dimensionality of the vector to be projected and $g$ is the number of disjoint groups; we confirm this complexity by experimentation. Empirical study reveals that our method achieves significantly better performance than classical methods in terms of running time and memory usage. We further show that embedded with our efficient projection operator, projection-based algorithms can solve regression problems with composite norm constraints more efficiently than other methods and give superior accuracy. | [
"['Adams Wei Yu' 'Hao Su' 'Li Fei-Fei']"
] |
cs.LG cs.AI | null | 1206.4639 | null | null | http://arxiv.org/pdf/1206.4639v1 | 2012-06-18T15:17:49Z | 2012-06-18T15:17:49Z | Adaptive Regularization for Weight Matrices | Algorithms for learning distributions over weight-vectors, such as AROW were
recently shown empirically to achieve state-of-the-art performance at various
problems, with strong theoretical guaranties. Extending these algorithms to
matrix models pose challenges since the number of free parameters in the
covariance of the distribution scales as $n^4$ with the dimension $n$ of the
matrix, and $n$ tends to be large in real applications. We describe, analyze
and experiment with two new algorithms for learning distribution of matrix
models. Our first algorithm maintains a diagonal covariance over the parameters
and can handle large covariance matrices. The second algorithm factors the
covariance to capture inter-features correlation while keeping the number of
parameters linear in the size of the original matrix. We analyze both
algorithms in the mistake bound model and show a superior precision performance
of our approach over other algorithms in two tasks: retrieving similar images,
and ranking similar documents. The factored algorithm is shown to attain faster
convergence rate.
| [
"Koby Crammer (The Technion), Gal Chechik (Bar Ilan University and\n Google research)",
"['Koby Crammer' 'Gal Chechik']"
] |
cs.NA cs.LG stat.ML | null | 1206.4640 | null | null | http://arxiv.org/pdf/1206.4640v1 | 2012-06-18T15:18:05Z | 2012-06-18T15:18:05Z | Stability of matrix factorization for collaborative filtering | We study the stability vis a vis adversarial noise of matrix factorization
algorithm for matrix completion. In particular, our results include: (I) we
bound the gap between the solution matrix of the factorization method and the
ground truth in terms of root mean square error; (II) we treat the matrix
factorization as a subspace fitting problem and analyze the difference between
the solution subspace and the ground truth; (III) we analyze the prediction
error of individual users based on the subspace stability. We apply these
results to the problem of collaborative filtering under manipulator attack,
which leads to useful insights and guidelines for collaborative filtering
system design.
| [
"Yu-Xiang Wang (National University of Singapore), Huan Xu (National\n University of Singapore)",
"['Yu-Xiang Wang' 'Huan Xu']"
] |
cs.LG cs.CV stat.ML | null | 1206.4641 | null | null | http://arxiv.org/pdf/1206.4641v1 | 2012-06-18T15:18:20Z | 2012-06-18T15:18:20Z | Total Variation and Euler's Elastica for Supervised Learning | In recent years, total variation (TV) and Euler's elastica (EE) have been
successfully applied to image processing tasks such as denoising and
inpainting. This paper investigates how to extend TV and EE to the supervised
learning settings on high dimensional data. The supervised learning problem can
be formulated as an energy functional minimization under Tikhonov
regularization scheme, where the energy is composed of a squared loss and a
total variation smoothing (or Euler's elastica smoothing). Its solution via
variational principles leads to an Euler-Lagrange PDE. However, the PDE is
always high-dimensional and cannot be directly solved by common methods.
Instead, radial basis functions are utilized to approximate the target
function, reducing the problem to finding the linear coefficients of basis
functions. We apply the proposed methods to supervised learning tasks
(including binary classification, multi-class classification, and regression)
on benchmark data sets. Extensive experiments have demonstrated promising
results of the proposed methods.
| [
"['Tong Lin' 'Hanlin Xue' 'Ling Wang' 'Hongbin Zha']",
"Tong Lin (Peking University), Hanlin Xue (Peking University), Ling\n Wang (LTCI, Telecom ParisTech, Paris), Hongbin Zha (Peking University)"
] |
cs.DS cs.LG stat.ML | null | 1206.4642 | null | null | http://arxiv.org/pdf/1206.4642v1 | 2012-06-18T15:18:51Z | 2012-06-18T15:18:51Z | Fast Computation of Subpath Kernel for Trees | The kernel method is a potential approach to analyzing structured data such
as sequences, trees, and graphs; however, unordered trees have not been
investigated extensively. Kimura et al. (2011) proposed a kernel function for
unordered trees on the basis of their subpaths, which are vertical
substructures of trees responsible for hierarchical information in them. Their
kernel exhibits practically good performance in terms of accuracy and speed;
however, linear-time computation is not guaranteed theoretically, unlike the
case of the other unordered tree kernel proposed by Vishwanathan and Smola
(2003). In this paper, we propose a theoretically guaranteed linear-time kernel
computation algorithm that is practically fast, and we present an efficient
prediction algorithm whose running time depends only on the size of the input
tree. Experimental results show that the proposed algorithms are quite
efficient in practice.
| [
"Daisuke Kimura (The University of Tokyo), Hisashi Kashima (The\n University of Tokyo)",
"['Daisuke Kimura' 'Hisashi Kashima']"
] |
cs.LG cs.GT cs.SY | null | 1206.4643 | null | null | http://arxiv.org/pdf/1206.4643v1 | 2012-06-18T15:19:07Z | 2012-06-18T15:19:07Z | Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty | We consider Markov decision processes under parameter uncertainty. Previous
studies all restrict to the case that uncertainties among different states are
uncoupled, which leads to conservative solutions. In contrast, we introduce an
intuitive concept, termed "Lightning Does not Strike Twice," to model coupled
uncertain parameters. Specifically, we require that the system can deviate from
its nominal parameters only a bounded number of times. We give probabilistic
guarantees indicating that this model represents real life situations and
devise tractable algorithms for computing optimal control policies using this
concept.
| [
"['Shie Mannor' 'Ofir Mebel' 'Huan Xu']",
"Shie Mannor (Technion), Ofir Mebel (Technion), Huan Xu (National\n University of Singapore)"
] |
cs.LG stat.ML | null | 1206.4644 | null | null | http://arxiv.org/pdf/1206.4644v1 | 2012-06-18T15:19:22Z | 2012-06-18T15:19:22Z | Groupwise Constrained Reconstruction for Subspace Clustering | Reconstruction based subspace clustering methods compute a self
reconstruction matrix over the samples and use it for spectral clustering to
obtain the final clustering result. Their success largely relies on the
assumption that the underlying subspaces are independent, which, however, does
not always hold in the applications with increasing number of subspaces. In
this paper, we propose a novel reconstruction based subspace clustering model
without making the subspace independence assumption. In our model, certain
properties of the reconstruction matrix are explicitly characterized using the
latent cluster indicators, and the affinity matrix used for spectral clustering
can be directly built from the posterior of the latent cluster indicators
instead of the reconstruction matrix. Experimental results on both synthetic
and real-world datasets show that the proposed model can outperform the
state-of-the-art methods.
| [
"['Ruijiang Li' 'Bin Li' 'Ke Zhang' 'Cheng Jin' 'Xiangyang Xue']",
"Ruijiang Li (Fudan University), Bin Li (University of Technology,\n Sydney), Ke Zhang (Fudan Univ.), Cheng Jin (Fudan University), Xiangyang Xue\n (Fudan University)"
] |
cs.LG cs.NA stat.ME stat.ML | null | 1206.4645 | null | null | http://arxiv.org/pdf/1206.4645v1 | 2012-06-18T15:19:58Z | 2012-06-18T15:19:58Z | Ensemble Methods for Convex Regression with Applications to Geometric
Programming Based Circuit Design | Convex regression is a promising area for bridging statistical estimation and
deterministic convex optimization. New piecewise linear convex regression
methods are fast and scalable, but can have instability when used to
approximate constraints or objective functions for optimization. Ensemble
methods, like bagging, smearing and random partitioning, can alleviate this
problem and maintain the theoretical properties of the underlying estimator. We
empirically examine the performance of ensemble methods for prediction and
optimization, and then apply them to device modeling and constraint
approximation for geometric programming based circuit design.
| [
"['Lauren Hannah' 'David Dunson']",
"Lauren Hannah (Duke University), David Dunson (Duke University)"
] |
cs.LG stat.ML | null | 1206.4646 | null | null | http://arxiv.org/pdf/1206.4646v1 | 2012-06-18T15:20:14Z | 2012-06-18T15:20:14Z | Partial-Hessian Strategies for Fast Learning of Nonlinear Embeddings | Stochastic neighbor embedding (SNE) and related nonlinear manifold learning
algorithms achieve high-quality low-dimensional representations of similarity
data, but are notoriously slow to train. We propose a generic formulation of
embedding algorithms that includes SNE and other existing algorithms, and study
their relation with spectral methods and graph Laplacians. This allows us to
define several partial-Hessian optimization strategies, characterize their
global and local convergence, and evaluate them empirically. We achieve up to
two orders of magnitude speedup over existing training methods with a strategy
(which we call the spectral direction) that adds nearly no overhead to the
gradient and yet is simple, scalable and applicable to several existing and
future embedding algorithms.
| [
"Max Vladymyrov (UC Merced), Miguel Carreira-Perpinan (UC Merced)",
"['Max Vladymyrov' 'Miguel Carreira-Perpinan']"
] |
cs.LG cs.AI cs.IR | null | 1206.4647 | null | null | http://arxiv.org/pdf/1206.4647v1 | 2012-06-18T15:22:24Z | 2012-06-18T15:22:24Z | Active Learning for Matching Problems | Effective learning of user preferences is critical to easing user burden in
various types of matching problems. Equally important is active query selection
to further reduce the amount of preference information users must provide. We
address the problem of active learning of user preferences for matching
problems, introducing a novel method for determining probabilistic matchings,
and developing several new active learning strategies that are sensitive to the
specific matching objective. Experiments with real-world data sets spanning
diverse domains demonstrate that matching-sensitive active learning
| [
"Laurent Charlin (University of Toronto), Rich Zemel (University of\n Toronto), Craig Boutilier (University of Toronto)",
"['Laurent Charlin' 'Rich Zemel' 'Craig Boutilier']"
] |
cs.LG | null | 1206.4648 | null | null | http://arxiv.org/pdf/1206.4648v1 | 2012-06-18T15:23:02Z | 2012-06-18T15:23:02Z | Two-Manifold Problems with Applications to Nonlinear System
Identification | Recently, there has been much interest in spectral approaches to learning
manifolds---so-called kernel eigenmap methods. These methods have had some
successes, but their applicability is limited because they are not robust to
noise. To address this limitation, we look at two-manifold problems, in which
we simultaneously reconstruct two related manifolds, each representing a
different view of the same data. By solving these interconnected learning
problems together, two-manifold algorithms are able to succeed where a
non-integrated approach would fail: each view allows us to suppress noise in
the other, reducing bias. We propose a class of algorithms for two-manifold
problems, based on spectral decomposition of cross-covariance operators in
Hilbert space, and discuss when two-manifold problems are useful. Finally, we
demonstrate that solving a two-manifold problem can aid in learning a nonlinear
dynamical system from limited data.
| [
"Byron Boots (Carnegie Mellon University), Geoff Gordon (Carnegie\n Mellon University)",
"['Byron Boots' 'Geoff Gordon']"
] |
cs.LG cs.CV stat.ML | null | 1206.4649 | null | null | http://arxiv.org/pdf/1206.4649v1 | 2012-06-18T15:23:19Z | 2012-06-18T15:23:19Z | Learning Efficient Structured Sparse Models | We present a comprehensive framework for structured sparse coding and
modeling extending the recent ideas of using learnable fast regressors to
approximate exact sparse codes. For this purpose, we develop a novel
block-coordinate proximal splitting method for the iterative solution of
hierarchical sparse coding problems, and show an efficient feed forward
architecture derived from its iteration. This architecture faithfully
approximates the exact structured sparse codes with a fraction of the
complexity of the standard optimization methods. We also show that by using
different training objective functions, learnable sparse encoders are no longer
restricted to be mere approximants of the exact sparse code for a pre-given
dictionary, as in earlier formulations, but can be rather used as full-featured
sparse encoders or even modelers. A simple implementation shows several orders
of magnitude speedup compared to the state-of-the-art at minimal performance
degradation, making the proposed framework suitable for real time and
large-scale applications.
| [
"Alex Bronstein (Tel Aviv University), Pablo Sprechmann (University of\n Minnesota), Guillermo Sapiro (University of Minnesota)",
"['Alex Bronstein' 'Pablo Sprechmann' 'Guillermo Sapiro']"
] |
cs.LG stat.ML | null | 1206.4650 | null | null | http://arxiv.org/pdf/1206.4650v1 | 2012-06-18T15:23:37Z | 2012-06-18T15:23:37Z | Analysis of Kernel Mean Matching under Covariate Shift | In real supervised learning scenarios, it is not uncommon that the training
and test sample follow different probability distributions, thus rendering the
necessity to correct the sampling bias. Focusing on a particular covariate
shift problem, we derive high probability confidence bounds for the kernel mean
matching (KMM) estimator, whose convergence rate turns out to depend on some
regularity measure of the regression function and also on some capacity measure
of the kernel. By comparing KMM with the natural plug-in estimator, we
establish the superiority of the former hence provide concrete
evidence/understanding to the effectiveness of KMM under covariate shift.
| [
"Yaoliang Yu (University of Alberta), Csaba Szepesvari (University of\n Alberta)",
"['Yaoliang Yu' 'Csaba Szepesvari']"
] |
cs.LG cs.CV stat.ML | null | 1206.4651 | null | null | http://arxiv.org/pdf/1206.4651v1 | 2012-06-18T15:24:01Z | 2012-06-18T15:24:01Z | Is margin preserved after random projection? | Random projections have been applied in many machine learning algorithms.
However, whether margin is preserved after random projection is non-trivial and
not well studied. In this paper we analyse margin distortion after random
projection, and give the conditions of margin preservation for binary
classification problems. We also extend our analysis to margin for multiclass
problems, and provide theoretical bounds on multiclass margin on the projected
data.
| [
"Qinfeng Shi (The University of Adelaide), Chunhua Shen (The University\n of Adelaide), Rhys Hill (The University of Adelaide), Anton van den Hengel\n (the University of Adelaide)",
"['Qinfeng Shi' 'Chunhua Shen' 'Rhys Hill' 'Anton van den Hengel']"
] |
cs.LG cs.AI | null | 1206.4652 | null | null | http://arxiv.org/pdf/1206.4652v1 | 2012-06-18T15:24:31Z | 2012-06-18T15:24:31Z | The Most Persistent Soft-Clique in a Set of Sampled Graphs | When searching for characteristic subpatterns in potentially noisy graph
data, it appears self-evident that having multiple observations would be better
than having just one. However, it turns out that the inconsistencies introduced
when different graph instances have different edge sets pose a serious
challenge. In this work we address this challenge for the problem of finding
maximum weighted cliques.
We introduce the concept of most persistent soft-clique. This is subset of
vertices, that 1) is almost fully or at least densely connected, 2) occurs in
all or almost all graph instances, and 3) has the maximum weight. We present a
measure of clique-ness, that essentially counts the number of edge missing to
make a subset of vertices into a clique. With this measure, we show that the
problem of finding the most persistent soft-clique problem can be cast either
as: a) a max-min two person game optimization problem, or b) a min-min soft
margin optimization problem. Both formulations lead to the same solution when
using a partial Lagrangian method to solve the optimization problems. By
experiments on synthetic data and on real social network data, we show that the
proposed method is able to reliably find soft cliques in graph data, even if
that is distorted by random noise or unreliable observations.
| [
"['Novi Quadrianto' 'Chao Chen' 'Christoph Lampert']",
"Novi Quadrianto (University of Cambridge), Chao Chen (IST Austria),\n Christoph Lampert (IST Austria)"
] |
cs.LG cs.CV stat.ML | null | 1206.4653 | null | null | http://arxiv.org/pdf/1206.4653v1 | 2012-06-18T15:24:49Z | 2012-06-18T15:24:49Z | Dimensionality Reduction by Local Discriminative Gaussians | We present local discriminative Gaussian (LDG) dimensionality reduction, a
supervised dimensionality reduction technique for classification. The LDG
objective function is an approximation to the leave-one-out training error of a
local quadratic discriminant analysis classifier, and thus acts locally to each
training point in order to find a mapping where similar data can be
discriminated from dissimilar data. While other state-of-the-art linear
dimensionality reduction methods require gradient descent or iterative solution
approaches, LDG is solved with a single eigen-decomposition. Thus, it scales
better for datasets with a large number of feature dimensions or training
examples. We also adapt LDG to the transfer learning setting, and show that it
achieves good performance when the test data distribution differs from that of
the training data.
| [
"Nathan Parrish (University of Washington), Maya Gupta (University of\n Washington)",
"['Nathan Parrish' 'Maya Gupta']"
] |
cs.AI cs.LG stat.ML | null | 1206.4654 | null | null | http://arxiv.org/pdf/1206.4654v1 | 2012-06-18T15:25:04Z | 2012-06-18T15:25:04Z | A Generalized Loop Correction Method for Approximate Inference in
Graphical Models | Belief Propagation (BP) is one of the most popular methods for inference in
probabilistic graphical models. BP is guaranteed to return the correct answer
for tree structures, but can be incorrect or non-convergent for loopy graphical
models. Recently, several new approximate inference algorithms based on cavity
distribution have been proposed. These methods can account for the effect of
loops by incorporating the dependency between BP messages. Alternatively,
region-based approximations (that lead to methods such as Generalized Belief
Propagation) improve upon BP by considering interactions within small clusters
of variables, thus taking small loops within these clusters into account. This
paper introduces an approach, Generalized Loop Correction (GLC), that benefits
from both of these types of loop correction. We show how GLC relates to these
two families of inference methods, then provide empirical evidence that GLC
works effectively in general, and can be significantly more accurate than both
correction schemes.
| [
"['Siamak Ravanbakhsh' 'Chun-Nam Yu' 'Russell Greiner']",
"Siamak Ravanbakhsh (University of Alberta), Chun-Nam Yu (University of\n Alberta), Russell Greiner (University of Alberta)"
] |
cs.LG | null | 1206.4655 | null | null | http://arxiv.org/pdf/1206.4655v1 | 2012-06-18T15:25:58Z | 2012-06-18T15:25:58Z | Modelling transition dynamics in MDPs with RKHS embeddings | We propose a new, nonparametric approach to learning and representing
transition dynamics in Markov decision processes (MDPs), which can be combined
easily with dynamic programming methods for policy optimisation and value
estimation. This approach makes use of a recently developed representation of
conditional distributions as \emph{embeddings} in a reproducing kernel Hilbert
space (RKHS). Such representations bypass the need for estimating transition
probabilities or densities, and apply to any domain on which kernels can be
defined. This avoids the need to calculate intractable integrals, since
expectations are represented as RKHS inner products whose computation has
linear complexity in the number of points used to represent the embedding. We
provide guarantees for the proposed applications in MDPs: in the context of a
value iteration algorithm, we prove convergence to either the optimal policy,
or to the closest projection of the optimal policy in our model class (an
RKHS), under reasonable assumptions. In experiments, we investigate a learning
task in a typical classical control setting (the under-actuated pendulum), and
on a navigation problem where only images from a sensor are observed. For
policy optimisation we compare with least-squares policy iteration where a
Gaussian process is used for value function estimation. For value estimation we
also compare to the NPDP method. Our approach achieves better performance in
all experiments.
| [
"Steffen Grunewalder (University College London), Guy Lever (University\n College London), Luca Baldassarre (University College London), Massi Pontil\n (University College London), Arthur Gretton (MPI for Intelligent Systems)",
"['Steffen Grunewalder' 'Guy Lever' 'Luca Baldassarre' 'Massi Pontil'\n 'Arthur Gretton']"
] |
cs.LG cs.AI stat.ML | null | 1206.4656 | null | null | http://arxiv.org/pdf/1206.4656v1 | 2012-06-18T15:26:13Z | 2012-06-18T15:26:13Z | Machine Learning that Matters | Much of current machine learning (ML) research has lost its connection to
problems of import to the larger world of science and society. From this
perspective, there exist glaring limitations in the data sets we investigate,
the metrics we employ for evaluation, and the degree to which results are
communicated back to their originating domains. What changes are needed to how
we conduct research to increase the impact that ML has? We present six Impact
Challenges to explicitly focus the field?s energy and attention, and we discuss
existing obstacles that must be addressed. We aim to inspire ongoing discussion
and focus on ML that matters.
| [
"['Kiri Wagstaff']",
"Kiri Wagstaff (Jet Propulsion Laboratory)"
] |
cs.LG cs.DS | null | 1206.4657 | null | null | http://arxiv.org/pdf/1206.4657v1 | 2012-06-18T15:26:34Z | 2012-06-18T15:26:34Z | Projection-free Online Learning | The computational bottleneck in applying online learning to massive data sets
is usually the projection step. We present efficient online learning algorithms
that eschew projections in favor of much more efficient linear optimization
steps using the Frank-Wolfe technique. We obtain a range of regret bounds for
online convex optimization, with better bounds for specific cases such as
stochastic online smooth convex optimization.
Besides the computational advantage, other desirable features of our
algorithms are that they are parameter-free in the stochastic case and produce
sparse decisions. We apply our algorithms to computationally intensive
applications of collaborative filtering, and show the theoretical improvements
to be clearly visible on standard datasets.
| [
"['Elad Hazan' 'Satyen Kale']",
"Elad Hazan (Technion), Satyen Kale (IBM T.J. Watson Research Center)"
] |
cs.LG stat.ML | null | 1206.4658 | null | null | http://arxiv.org/pdf/1206.4658v1 | 2012-06-18T15:27:40Z | 2012-06-18T15:27:40Z | Dirichlet Process with Mixed Random Measures: A Nonparametric Topic
Model for Labeled Data | We describe a nonparametric topic model for labeled data. The model uses a
mixture of random measures (MRM) as a base distribution of the Dirichlet
process (DP) of the HDP framework, so we call it the DP-MRM. To model labeled
data, we define a DP distributed random measure for each label, and the
resulting model generates an unbounded number of topics for each label. We
apply DP-MRM on single-labeled and multi-labeled corpora of documents and
compare the performance on label prediction with MedLDA, LDA-SVM, and
Labeled-LDA. We further enhance the model by incorporating ddCRP and modeling
multi-labeled images for image segmentation and object labeling, comparing the
performance with nCuts and rddCRP.
| [
"Dongwoo Kim (KAIST), Suin Kim (KAIST), Alice Oh (KAIST)",
"['Dongwoo Kim' 'Suin Kim' 'Alice Oh']"
] |
cs.LG stat.ML | null | 1206.4659 | null | null | http://arxiv.org/pdf/1206.4659v1 | 2012-06-18T15:27:56Z | 2012-06-18T15:27:56Z | Max-Margin Nonparametric Latent Feature Models for Link Prediction | We present a max-margin nonparametric latent feature model, which unites the
ideas of max-margin learning and Bayesian nonparametrics to discover
discriminative latent features for link prediction and automatically infer the
unknown latent social dimension. By minimizing a hinge-loss using the linear
expectation operator, we can perform posterior inference efficiently without
dealing with a highly nonlinear link likelihood function; by using a
fully-Bayesian formulation, we can avoid tuning regularization constants.
Experimental results on real datasets appear to demonstrate the benefits
inherited from max-margin learning and fully-Bayesian nonparametric inference.
| [
"['Jun Zhu']",
"Jun Zhu (Tsinghua University)"
] |
cs.LG | null | 1206.4660 | null | null | http://arxiv.org/pdf/1206.4660v1 | 2012-06-18T15:28:12Z | 2012-06-18T15:28:12Z | Learning with Augmented Features for Heterogeneous Domain Adaptation | We propose a new learning method for heterogeneous domain adaptation (HDA),
in which the data from the source domain and the target domain are represented
by heterogeneous features with different dimensions. Using two different
projection matrices, we first transform the data from two domains into a common
subspace in order to measure the similarity between the data from two domains.
We then propose two new feature mapping functions to augment the transformed
data with their original features and zeros. The existing learning methods
(e.g., SVM and SVR) can be readily incorporated with our newly proposed
augmented feature representations to effectively utilize the data from both
domains for HDA. Using the hinge loss function in SVM as an example, we
introduce the detailed objective function in our method called Heterogeneous
Feature Augmentation (HFA) for a linear case and also describe its
kernelization in order to efficiently cope with the data with very high
dimensions. Moreover, we also develop an alternating optimization algorithm to
effectively solve the nontrivial optimization problem in our HFA method.
Comprehensive experiments on two benchmark datasets clearly demonstrate that
HFA outperforms the existing HDA methods.
| [
"Lixin Duan (Nanyang Technological University), Dong Xu (Nanyang\n Technological University), Ivor Tsang (Nanyang Technological University)",
"['Lixin Duan' 'Dong Xu' 'Ivor Tsang']"
] |
cs.LG stat.ML | null | 1206.4661 | null | null | http://arxiv.org/pdf/1206.4661v1 | 2012-06-18T15:30:13Z | 2012-06-18T15:30:13Z | Predicting accurate probabilities with a ranking loss | In many real-world applications of machine learning classifiers, it is
essential to predict the probability of an example belonging to a particular
class. This paper proposes a simple technique for predicting probabilities
based on optimizing a ranking loss, followed by isotonic regression. This
semi-parametric technique offers both good ranking and regression performance,
and models a richer set of probability distributions than statistical
workhorses such as logistic regression. We provide experimental results that
show the effectiveness of this technique on real-world applications of
probability prediction.
| [
"Aditya Menon (UC San Diego), Xiaoqian Jiang (UC San Diego), Shankar\n Vembu (University of Toronto), Charles Elkan (UC San Diego), Lucila\n Ohno-Machado (UC San Diego)",
"['Aditya Menon' 'Xiaoqian Jiang' 'Shankar Vembu' 'Charles Elkan'\n 'Lucila Ohno-Machado']"
] |
cs.CR cs.LG cs.MM | null | 1206.4662 | null | null | http://arxiv.org/pdf/1206.4662v1 | 2012-06-18T15:30:35Z | 2012-06-18T15:30:35Z | Bayesian Watermark Attacks | This paper presents an application of statistical machine learning to the
field of watermarking. We propose a new attack model on additive
spread-spectrum watermarking systems. The proposed attack is based on Bayesian
statistics. We consider the scenario in which a watermark signal is repeatedly
embedded in specific, possibly chosen based on a secret message bitstream,
segments (signals) of the host data. The host signal can represent a patch of
pixels from an image or a video frame. We propose a probabilistic model that
infers the embedded message bitstream and watermark signal, directly from the
watermarked data, without access to the decoder. We develop an efficient Markov
chain Monte Carlo sampler for updating the model parameters from their
conjugate full conditional posteriors. We also provide a variational Bayesian
solution, which further increases the convergence speed of the algorithm.
Experiments with synthetic and real image signals demonstrate that the attack
model is able to correctly infer a large part of the message bitstream and
obtain a very accurate estimate of the watermark signal.
| [
"Ivo Shterev (Duke University), David Dunson (Duke University)",
"['Ivo Shterev' 'David Dunson']"
] |
cs.LG stat.ML | null | 1206.4663 | null | null | http://arxiv.org/pdf/1206.4663v1 | 2012-06-18T15:30:52Z | 2012-06-18T15:30:52Z | The Convexity and Design of Composite Multiclass Losses | We consider composite loss functions for multiclass prediction comprising a
proper (i.e., Fisher-consistent) loss over probability distributions and an
inverse link function. We establish conditions for their (strong) convexity and
explore the implications. We also show how the separation of concerns afforded
by using this composite representation allows for the design of families of
losses with the same Bayes risk.
| [
"Mark Reid (The Australian National University and NICTA), Robert\n Williamson (The Australian National University and NICTA), Peng Sun (Tsinghua\n University)",
"['Mark Reid' 'Robert Williamson' 'Peng Sun']"
] |
cs.LG stat.ML | null | 1206.4664 | null | null | http://arxiv.org/pdf/1206.4664v1 | 2012-06-18T15:31:13Z | 2012-06-18T15:31:13Z | Tighter Variational Representations of f-Divergences via Restriction to
Probability Measures | We show that the variational representations for f-divergences currently used
in the literature can be tightened. This has implications to a number of
methods recently proposed based on this representation. As an example
application we use our tighter representation to derive a general f-divergence
estimator based on two i.i.d. samples and derive the dual program for this
estimator that performs well empirically. We also point out a connection
between our estimator and MMD.
| [
"['Avraham Ruderman' 'Mark Reid' 'Dario Garcia-Garcia' 'James Petterson']",
"Avraham Ruderman (Australian National University and NICTA), Mark Reid\n (Australian National University and NICTA), Dario Garcia-Garcia (Australian\n National University and NICTA), James Petterson (NICTA)"
] |
cs.LG stat.ML | null | 1206.4665 | null | null | http://arxiv.org/pdf/1206.4665v1 | 2012-06-18T15:32:05Z | 2012-06-18T15:32:05Z | Nonparametric variational inference | Variational methods are widely used for approximate posterior inference.
However, their use is typically limited to families of distributions that enjoy
particular conjugacy properties. To circumvent this limitation, we propose a
family of variational approximations inspired by nonparametric kernel density
estimation. The locations of these kernels and their bandwidth are treated as
variational parameters and optimized to improve an approximate lower bound on
the marginal likelihood of the data. Using multiple kernels allows the
approximation to capture multiple modes of the posterior, unlike most other
variational approximations. We demonstrate the efficacy of the nonparametric
approximation with a hierarchical logistic regression model and a nonlinear
matrix factorization model. We obtain predictive performance as good as or
better than more specialized variational methods and sample-based
approximations. The method is easy to apply to more general graphical models
for which standard variational methods are difficult to derive.
| [
"Samuel Gershman (Princeton University), Matt Hoffman (Princeton\n University), David Blei (Princeton University)",
"['Samuel Gershman' 'Matt Hoffman' 'David Blei']"
] |
stat.CO cs.LG stat.ME | null | 1206.4666 | null | null | http://arxiv.org/pdf/1206.4666v1 | 2012-06-18T15:32:46Z | 2012-06-18T15:32:46Z | A Bayesian Approach to Approximate Joint Diagonalization of Square
Matrices | We present a Bayesian scheme for the approximate diagonalisation of several
square matrices which are not necessarily symmetric. A Gibbs sampler is derived
to simulate samples of the common eigenvectors and the eigenvalues for these
matrices. Several synthetic examples are used to illustrate the performance of
the proposed Gibbs sampler and we then provide comparisons to several other
joint diagonalization algorithms, which shows that the Gibbs sampler achieves
the state-of-the-art performance on the examples considered. As a byproduct,
the output of the Gibbs sampler could be used to estimate the log marginal
likelihood, however we employ the approximation based on the Bayesian
information criterion (BIC) which in the synthetic examples considered
correctly located the number of common eigenvectors. We then succesfully
applied the sampler to the source separation problem as well as the common
principal component analysis and the common spatial pattern analysis problems.
| [
"Mingjun Zhong (Dalian University of Tech.), Mark Girolami (University\n College London)",
"['Mingjun Zhong' 'Mark Girolami']"
] |
cs.LG cs.AI cs.IR | null | 1206.4667 | null | null | http://arxiv.org/pdf/1206.4667v2 | 2012-07-18T18:54:06Z | 2012-06-18T15:33:05Z | Unachievable Region in Precision-Recall Space and Its Effect on
Empirical Evaluation | Precision-recall (PR) curves and the areas under them are widely used to
summarize machine learning results, especially for data sets exhibiting class
skew. They are often used analogously to ROC curves and the area under ROC
curves. It is known that PR curves vary as class skew changes. What was not
recognized before this paper is that there is a region of PR space that is
completely unachievable, and the size of this region depends only on the skew.
This paper precisely characterizes the size of that region and discusses its
implications for empirical evaluation methodology in machine learning.
| [
"['Kendrick Boyd' 'Vitor Santos Costa' 'Jesse Davis' 'David Page']",
"Kendrick Boyd (University of Wisconsin Madison), Vitor Santos Costa\n (University of Porto), Jesse Davis (KU Leuven), David Page (University of\n Wisconsin Madison)"
] |
cs.LG cs.DS stat.ML | null | 1206.4668 | null | null | http://arxiv.org/pdf/1206.4668v1 | 2012-06-18T15:33:25Z | 2012-06-18T15:33:25Z | Approximate Principal Direction Trees | We introduce a new spatial data structure for high dimensional data called
the \emph{approximate principal direction tree} (APD tree) that adapts to the
intrinsic dimension of the data. Our algorithm ensures vector-quantization
accuracy similar to that of computationally-expensive PCA trees with similar
time-complexity to that of lower-accuracy RP trees.
APD trees use a small number of power-method iterations to find splitting
planes for recursively partitioning the data. As such they provide a natural
trade-off between the running-time and accuracy achieved by RP and PCA trees.
Our theoretical results establish a) strong performance guarantees regardless
of the convergence rate of the power-method and b) that $O(\log d)$ iterations
suffice to establish the guarantee of PCA trees when the intrinsic dimension is
$d$. We demonstrate this trade-off and the efficacy of our data structure on
both the CPU and GPU.
| [
"['Mark McCartin-Lim' 'Andrew McGregor' 'Rui Wang']",
"Mark McCartin-Lim (University of Massachusetts), Andrew McGregor\n (University of Massachusetts), Rui Wang (University of Massachusetts)"
] |
cs.LG stat.ML | null | 1206.4669 | null | null | http://arxiv.org/pdf/1206.4669v1 | 2012-06-18T15:34:07Z | 2012-06-18T15:34:07Z | Sparse Additive Functional and Kernel CCA | Canonical Correlation Analysis (CCA) is a classical tool for finding
correlations among the components of two random vectors. In recent years, CCA
has been widely applied to the analysis of genomic data, where it is common for
researchers to perform multiple assays on a single set of patient samples.
Recent work has proposed sparse variants of CCA to address the high
dimensionality of such data. However, classical and sparse CCA are based on
linear models, and are thus limited in their ability to find general
correlations. In this paper, we present two approaches to high-dimensional
nonparametric CCA, building on recent developments in high-dimensional
nonparametric regression. We present estimation procedures for both approaches,
and analyze their theoretical properties in the high-dimensional setting. We
demonstrate the effectiveness of these procedures in discovering nonlinear
correlations via extensive simulations, as well as through experiments with
genomic data.
| [
"Sivaraman Balakrishnan (Carnegie Mellon University), Kriti Puniyani\n (Carnegie Mellon University), John Lafferty (Carnegie Mellon University)",
"['Sivaraman Balakrishnan' 'Kriti Puniyani' 'John Lafferty']"
] |
cs.IT astro-ph.EP cs.LG math.IT physics.data-an | null | 1206.4670 | null | null | http://arxiv.org/pdf/1206.4670v1 | 2012-06-18T15:34:23Z | 2012-06-18T15:34:23Z | State-Space Inference for Non-Linear Latent Force Models with
Application to Satellite Orbit Prediction | Latent force models (LFMs) are flexible models that combine mechanistic
modelling principles (i.e., physical models) with non-parametric data-driven
components. Several key applications of LFMs need non-linearities, which
results in analytically intractable inference. In this work we show how
non-linear LFMs can be represented as non-linear white noise driven state-space
models and present an efficient non-linear Kalman filtering and smoothing based
method for approximate state and parameter inference. We illustrate the
performance of the proposed methodology via two simulated examples, and apply
it to a real-world problem of long-term prediction of GPS satellite orbits.
| [
"Jouni Hartikainen (Aalto University), Mari Seppanen (Tampere\n University of Technology), Simo Sarkka (Aalto University)",
"['Jouni Hartikainen' 'Mari Seppanen' 'Simo Sarkka']"
] |
cs.LG stat.ML | null | 1206.4671 | null | null | http://arxiv.org/pdf/1206.4671v1 | 2012-06-18T15:35:02Z | 2012-06-18T15:35:02Z | Dependent Hierarchical Normalized Random Measures for Dynamic Topic
Modeling | We develop dependent hierarchical normalized random measures and apply them
to dynamic topic modeling. The dependency arises via superposition, subsampling
and point transition on the underlying Poisson processes of these measures. The
measures used include normalised generalised Gamma processes that demonstrate
power law properties, unlike Dirichlet processes used previously in dynamic
topic modeling. Inference for the model includes adapting a recently developed
slice sampler to directly manipulate the underlying Poisson process.
Experiments performed on news, blogs, academic and Twitter collections
demonstrate the technique gives superior perplexity over a number of previous
models.
| [
"Changyou Chen (ANU & NICTA), Nan Ding (Purdue University), Wray\n Buntine (NICTA)",
"['Changyou Chen' 'Nan Ding' 'Wray Buntine']"
] |
cs.LG stat.ML | null | 1206.4672 | null | null | http://arxiv.org/pdf/1206.4672v1 | 2012-06-18T15:35:20Z | 2012-06-18T15:35:20Z | Efficient Active Algorithms for Hierarchical Clustering | Advances in sensing technologies and the growth of the internet have resulted
in an explosion in the size of modern datasets, while storage and processing
power continue to lag behind. This motivates the need for algorithms that are
efficient, both in terms of the number of measurements needed and running time.
To combat the challenges associated with large datasets, we propose a general
framework for active hierarchical clustering that repeatedly runs an
off-the-shelf clustering algorithm on small subsets of the data and comes with
guarantees on performance, measurement complexity and runtime complexity. We
instantiate this framework with a simple spectral clustering algorithm and
provide concrete results on its performance, showing that, under some
assumptions, this algorithm recovers all clusters of size ?(log n) using O(n
log^2 n) similarities and runs in O(n log^3 n) time for a dataset of n objects.
Through extensive experimentation we also demonstrate that this framework is
practically alluring.
| [
"Akshay Krishnamurthy (Carnegie Mellon University), Sivaraman\n Balakrishnan (Carnegie Mellon University), Min Xu (Carnegie Mellon\n University), Aarti Singh (Carnegie Mellon University)",
"['Akshay Krishnamurthy' 'Sivaraman Balakrishnan' 'Min Xu' 'Aarti Singh']"
] |
cs.LG stat.ML | null | 1206.4673 | null | null | http://arxiv.org/pdf/1206.4673v1 | 2012-06-18T15:35:38Z | 2012-06-18T15:35:38Z | Group Sparse Additive Models | We consider the problem of sparse variable selection in nonparametric
additive models, with the prior knowledge of the structure among the covariates
to encourage those variables within a group to be selected jointly. Previous
works either study the group sparsity in the parametric setting (e.g., group
lasso), or address the problem in the non-parametric setting without exploiting
the structural information (e.g., sparse additive models). In this paper, we
present a new method, called group sparse additive models (GroupSpAM), which
can handle group sparsity in additive models. We generalize the l1/l2 norm to
Hilbert spaces as the sparsity-inducing penalty in GroupSpAM. Moreover, we
derive a novel thresholding condition for identifying the functional sparsity
at the group level, and propose an efficient block coordinate descent algorithm
for constructing the estimate. We demonstrate by simulation that GroupSpAM
substantially outperforms the competing methods in terms of support recovery
and prediction accuracy in additive models, and also conduct a comparative
experiment on a real breast cancer dataset.
| [
"Junming Yin (Carnegie Mellon University), Xi Chen (Carnegie Mellon\n University), Eric Xing (Carnegie Mellon University)",
"['Junming Yin' 'Xi Chen' 'Eric Xing']"
] |
cs.LG cs.DS stat.ML | null | 1206.4674 | null | null | http://arxiv.org/pdf/1206.4674v1 | 2012-06-18T15:36:16Z | 2012-06-18T15:36:16Z | Comparison-Based Learning with Rank Nets | We consider the problem of search through comparisons, where a user is
presented with two candidate objects and reveals which is closer to her
intended target. We study adaptive strategies for finding the target, that
require knowledge of rank relationships but not actual distances between
objects. We propose a new strategy based on rank nets, and show that for target
distributions with a bounded doubling constant, it finds the target in a number
of comparisons close to the entropy of the target distribution and, hence, of
the optimum. We extend these results to the case of noisy oracles, and compare
this strategy to prior art over multiple datasets.
| [
"Amin Karbasi (EPFL), Stratis Ioannidis (Technicolor), laurent\n Massoulie (Technicolor)",
"['Amin Karbasi' 'Stratis Ioannidis' 'laurent Massoulie']"
] |
cs.CR cs.DC cs.LG | null | 1206.4675 | null | null | http://arxiv.org/pdf/1206.4675v1 | 2012-06-18T15:36:32Z | 2012-06-18T15:36:32Z | Finding Botnets Using Minimal Graph Clusterings | We study the problem of identifying botnets and the IP addresses which they
comprise, based on the observation of a fraction of the global email spam
traffic. Observed mailing campaigns constitute evidence for joint botnet
membership, they are represented by cliques in the graph of all messages. No
evidence against an association of nodes is ever available. We reduce the
problem of identifying botnets to a problem of finding a minimal clustering of
the graph of messages. We directly model the distribution of clusterings given
the input graph; this avoids potential errors caused by distributional
assumptions of a generative model. We report on a case study in which we
evaluate the model by its ability to predict the spam campaign that a given IP
address is going to participate in.
| [
"Peter Haider (University of Potsdam), Tobias Scheffer (University of\n Potsdam)",
"['Peter Haider' 'Tobias Scheffer']"
] |
cs.LG cs.CV cs.NA stat.ML | null | 1206.4676 | null | null | http://arxiv.org/pdf/1206.4676v1 | 2012-06-18T15:36:49Z | 2012-06-18T15:36:49Z | Clustering by Low-Rank Doubly Stochastic Matrix Decomposition | Clustering analysis by nonnegative low-rank approximations has achieved
remarkable progress in the past decade. However, most approximation approaches
in this direction are still restricted to matrix factorization. We propose a
new low-rank learning method to improve the clustering performance, which is
beyond matrix factorization. The approximation is based on a two-step bipartite
random walk through virtual cluster nodes, where the approximation is formed by
only cluster assigning probabilities. Minimizing the approximation error
measured by Kullback-Leibler divergence is equivalent to maximizing the
likelihood of a discriminative model, which endows our method with a solid
probabilistic interpretation. The optimization is implemented by a relaxed
Majorization-Minimization algorithm that is advantageous in finding good local
minima. Furthermore, we point out that the regularized algorithm with Dirichlet
prior only serves as initialization. Experimental results show that the new
method has strong performance in clustering purity for various datasets,
especially for large-scale manifold data.
| [
"['Zhirong Yang' 'Erkki Oja']",
"Zhirong Yang (Aalto University), Erkki Oja (Aalto University)"
] |
cs.LG stat.ML | null | 1206.4677 | null | null | http://arxiv.org/pdf/1206.4677v1 | 2012-06-18T15:37:07Z | 2012-06-18T15:37:07Z | Semi-Supervised Learning of Class Balance under Class-Prior Change by
Distribution Matching | In real-world classification problems, the class balance in the training
dataset does not necessarily reflect that of the test dataset, which can cause
significant estimation bias. If the class ratio of the test dataset is known,
instance re-weighting or resampling allows systematical bias correction.
However, learning the class ratio of the test dataset is challenging when no
labeled data is available from the test domain. In this paper, we propose to
estimate the class ratio in the test dataset by matching probability
distributions of training and test input data. We demonstrate the utility of
the proposed approach through experiments.
| [
"['Marthinus Du Plessis' 'Masashi Sugiyama']",
"Marthinus Du Plessis (Tokyo Institute of Technology), Masashi Sugiyama\n (Tokyo Institute of Technology)"
] |
cs.LG stat.ML | null | 1206.4678 | null | null | http://arxiv.org/pdf/1206.4678v1 | 2012-06-18T15:37:23Z | 2012-06-18T15:37:23Z | Linear Regression with Limited Observation | We consider the most common variants of linear regression, including Ridge,
Lasso and Support-vector regression, in a setting where the learner is allowed
to observe only a fixed number of attributes of each example at training time.
We present simple and efficient algorithms for these problems: for Lasso and
Ridge regression they need the same total number of attributes (up to
constants) as do full-information algorithms, for reaching a certain accuracy.
For Support-vector regression, we require exponentially less attributes
compared to the state of the art. By that, we resolve an open problem recently
posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to
be justified by superior performance compared to the state of the art.
| [
"['Elad Hazan' 'Tomer Koren']",
"Elad Hazan (Technion), Tomer Koren (Technion)"
] |
cs.LG stat.ML | null | 1206.4679 | null | null | http://arxiv.org/pdf/1206.4679v1 | 2012-06-18T15:37:59Z | 2012-06-18T15:37:59Z | Factorized Asymptotic Bayesian Hidden Markov Models | This paper addresses the issue of model selection for hidden Markov models
(HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has
been recently developed for model selection on independent hidden variables
(i.e., mixture models), for time-dependent hidden variables. As with FAB in
mixture models, FAB for HMMs is derived as an iterative lower bound
maximization algorithm of a factorized information criterion (FIC). It
inherits, from FAB for mixture models, several desirable properties for
learning HMMs, such as asymptotic consistency of FIC with marginal
log-likelihood, a shrinkage effect for hidden state selection, monotonic
increase of the lower FIC bound through the iterative optimization. Further, it
does not have a tunable hyper-parameter, and thus its model selection process
can be fully automated. Experimental results shows that FAB outperforms
states-of-the-art variational Bayesian HMM and non-parametric Bayesian HMM in
terms of model selection accuracy and computational efficiency.
| [
"Ryohei Fujimaki (NEC Laboratories America), Kohei Hayashi (Nara\n Institute of Science and Technology)",
"['Ryohei Fujimaki' 'Kohei Hayashi']"
] |
cs.LG math.ST stat.TH | null | 1206.4680 | null | null | http://arxiv.org/pdf/1206.4680v1 | 2012-06-18T15:38:18Z | 2012-06-18T15:38:18Z | Fast Prediction of New Feature Utility | We study the new feature utility prediction problem: statistically testing
whether adding a new feature to the data representation can improve predictive
accuracy on a supervised learning task. In many applications, identifying new
informative features is the primary pathway for improving performance. However,
evaluating every potential feature by re-training the predictor with it can be
costly. The paper describes an efficient, learner-independent technique for
estimating new feature utility without re-training based on the current
predictor's outputs. The method is obtained by deriving a connection between
loss reduction potential and the new feature's correlation with the loss
gradient of the current predictor. This leads to a simple yet powerful
hypothesis testing procedure, for which we prove consistency. Our theoretical
analysis is accompanied by empirical evaluation on standard benchmarks and a
large-scale industrial dataset.
| [
"Hoyt Koepke (University of Washington), Mikhail Bilenko (Microsoft\n Research)",
"['Hoyt Koepke' 'Mikhail Bilenko']"
] |
cs.LG stat.ML | null | 1206.4681 | null | null | http://arxiv.org/pdf/1206.4681v1 | 2012-06-18T15:40:11Z | 2012-06-18T15:40:11Z | LPQP for MAP: Putting LP Solvers to Better Use | MAP inference for general energy functions remains a challenging problem.
While most efforts are channeled towards improving the linear programming (LP)
based relaxation, this work is motivated by the quadratic programming (QP)
relaxation. We propose a novel MAP relaxation that penalizes the
Kullback-Leibler divergence between the LP pairwise auxiliary variables, and QP
equivalent terms given by the product of the unaries. We develop two efficient
algorithms based on variants of this relaxation. The algorithms minimize the
non-convex objective using belief propagation and dual decomposition as
building blocks. Experiments on synthetic and real-world data show that the
solutions returned by our algorithms substantially improve over the LP
relaxation.
| [
"Patrick Pletscher (ETH Zurich), Sharon Wulff (ETH Zurich)",
"['Patrick Pletscher' 'Sharon Wulff']"
] |
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