nips nips2009 knowledge-graph by maker-knowledge-mining

nips 2009 knowledge graph

papers list:

1 nips-2009-$L 1$-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry

Author: Arnak Dalalyan, Renaud Keriven

Abstract: We propose a new approach to the problem of robust estimation in multiview geometry. Inspired by recent advances in the sparse recovery problem of statistics, we deﬁne our estimator as a Bayesian maximum a posteriori with multivariate Laplace prior on the vector describing the outliers. This leads to an estimator in which the ﬁdelity to the data is measured by the L∞ -norm while the regularization is done by the L1 -norm. The proposed procedure is fairly fast since the outlier removal is done by solving one linear program (LP). An important difference compared to existing algorithms is that for our estimator it is not necessary to specify neither the number nor the proportion of the outliers. We present strong theoretical results assessing the accuracy of our procedure, as well as a numerical example illustrating its efﬁciency on real data. 1

2 nips-2009-3D Object Recognition with Deep Belief Nets

Author: Vinod Nair, Geoffrey E. Hinton

Abstract: We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB database (normalized-uniform version), which contains stereo-pair images of objects under diﬀerent lighting conditions and viewpoints. Our model achieves 6.5% error on the test set, which is close to the best published result for NORB (5.9%) using a convolutional neural net that has built-in knowledge of translation invariance. It substantially outperforms shallow models such as SVMs (11.6%). DBNs are especially suited for semi-supervised learning, and to demonstrate this we consider a modiﬁed version of the NORB recognition task in which additional unlabeled images are created by applying small translations to the images in the database. With the extra unlabeled data (and the same amount of labeled data as before), our model achieves 5.2% error. 1

3 nips-2009-AUC optimization and the two-sample problem

Author: Nicolas Vayatis, Marine Depecker, Stéphan J. Clémençcon

Abstract: The purpose of the paper is to explore the connection between multivariate homogeneity tests and AUC optimization. The latter problem has recently received much attention in the statistical learning literature. From the elementary observation that, in the two-sample problem setup, the null assumption corresponds to the situation where the area under the optimal ROC curve is equal to 1/2, we propose a two-stage testing method based on data splitting. A nearly optimal scoring function in the AUC sense is ﬁrst learnt from one of the two half-samples. Data from the remaining half-sample are then projected onto the real line and eventually ranked according to the scoring function computed at the ﬁrst stage. The last step amounts to performing a standard Mann-Whitney Wilcoxon test in the onedimensional framework. We show that the learning step of the procedure does not affect the consistency of the test as well as its properties in terms of power, provided the ranking produced is accurate enough in the AUC sense. The results of a numerical experiment are eventually displayed in order to show the efﬁciency of the method. 1

4 nips-2009-A Bayesian Analysis of Dynamics in Free Recall

Author: Richard Socher, Samuel Gershman, Per Sederberg, Kenneth Norman, Adler J. Perotte, David M. Blei

Abstract: We develop a probabilistic model of human memory performance in free recall experiments. In these experiments, a subject ﬁrst studies a list of words and then tries to recall them. To model these data, we draw on both previous psychological research and statistical topic models of text documents. We assume that memories are formed by assimilating the semantic meaning of studied words (represented as a distribution over topics) into a slowly changing latent context (represented in the same space). During recall, this context is reinstated and used as a cue for retrieving studied words. By conceptualizing memory retrieval as a dynamic latent variable model, we are able to use Bayesian inference to represent uncertainty and reason about the cognitive processes underlying memory. We present a particle ﬁlter algorithm for performing approximate posterior inference, and evaluate our model on the prediction of recalled words in experimental data. By specifying the model hierarchically, we are also able to capture inter-subject variability. 1

5 nips-2009-A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation

Author: Lan Du, Lu Ren, Lawrence Carin, David B. Dunson

Abstract: A non-parametric Bayesian model is proposed for processing multiple images. The analysis employs image features and, when present, the words associated with accompanying annotations. The model clusters the images into classes, and each image is segmented into a set of objects, also allowing the opportunity to assign a word to each object (localized labeling). Each object is assumed to be represented as a heterogeneous mix of components, with this realized via mixture models linking image features to object types. The number of image classes, number of object types, and the characteristics of the object-feature mixture models are inferred nonparametrically. To constitute spatially contiguous objects, a new logistic stick-breaking process is developed. Inference is performed efﬁciently via variational Bayesian analysis, with example results presented on two image databases.

6 nips-2009-A Biologically Plausible Model for Rapid Natural Scene Identification

Author: Sennay Ghebreab, Steven Scholte, Victor Lamme, Arnold Smeulders

Abstract: Contrast statistics of the majority of natural images conform to a Weibull distribution. This property of natural images may facilitate efficient and very rapid extraction of a scene's visual gist. Here we investigated whether a neural response model based on the Wei bull contrast distribution captures visual information that humans use to rapidly identify natural scenes. In a learning phase, we measured EEG activity of 32 subjects viewing brief flashes of 700 natural scenes. From these neural measurements and the contrast statistics of the natural image stimuli, we derived an across subject Wei bull response model. We used this model to predict the EEG responses to 100 new natural scenes and estimated which scene the subject viewed by finding the best match between the model predictions and the observed EEG responses. In almost 90 percent of the cases our model accurately predicted the observed scene. Moreover, in most failed cases, the scene mistaken for the observed scene was visually similar to the observed scene itself. Similar results were obtained in a separate experiment in which 16 other subjects where presented with artificial occlusion models of natural images. Together, these results suggest that Weibull contrast statistics of natural images contain a considerable amount of visual gist information to warrant rapid image identification.

7 nips-2009-A Data-Driven Approach to Modeling Choice

Author: Vivek Farias, Srikanth Jagabathula, Devavrat Shah

Abstract: We visit the following fundamental problem: For a ‘generic’ model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from oﬀering a particular assortment of choices? This problem is central to areas within operations research, marketing and econometrics. We present a framework to answer such questions and design a number of tractable algorithms (from a data and computational standpoint) for the same. 1

8 nips-2009-A Fast, Consistent Kernel Two-Sample Test

Author: Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur

Abstract: A kernel embedding of probability distributions into reproducing kernel Hilbert spaces (RKHS) has recently been proposed, which allows the comparison of two probability measures P and Q based on the distance between their respective embeddings: for a sufﬁciently rich RKHS, this distance is zero if and only if P and Q coincide. In using this distance as a statistic for a test of whether two samples are from different distributions, a major difﬁculty arises in computing the significance threshold, since the empirical statistic has as its null distribution (where P = Q) an inﬁnite weighted sum of χ2 random variables. Prior ﬁnite sample approximations to the null distribution include using bootstrap resampling, which yields a consistent estimate but is computationally costly; and ﬁtting a parametric model with the low order moments of the test statistic, which can work well in practice but has no consistency or accuracy guarantees. The main result of the present work is a novel estimate of the null distribution, computed from the eigenspectrum of the Gram matrix on the aggregate sample from P and Q, and having lower computational cost than the bootstrap. A proof of consistency of this estimate is provided. The performance of the null distribution estimate is compared with the bootstrap and parametric approaches on an artiﬁcial example, high dimensional multivariate data, and text.

9 nips-2009-A Game-Theoretic Approach to Hypergraph Clustering

Author: Samuel R. Bulò, Marcello Pelillo

Abstract: Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of objects using high-order (rather than pairwise) similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a user-deﬁned number of classes, thereby obtaining the clusters as a by-product of the partitioning process. In this paper, we provide a radically different perspective to the problem. In contrast to the classical approach, we attempt to provide a meaningful formalization of the very notion of a cluster and we show that game theory offers an attractive and unexplored perspective that serves well our purpose. Speciﬁcally, we show that the hypergraph clustering problem can be naturally cast into a non-cooperative multi-player “clustering game”, whereby the notion of a cluster is equivalent to a classical game-theoretic equilibrium concept. From the computational viewpoint, we show that the problem of ﬁnding the equilibria of our clustering game is equivalent to locally optimizing a polynomial function over the standard simplex, and we provide a discrete-time dynamics to perform this optimization. Experiments are presented which show the superiority of our approach over state-of-the-art hypergraph clustering techniques.

10 nips-2009-A Gaussian Tree Approximation for Integer Least-Squares

Author: Jacob Goldberger, Amir Leshem

Abstract: This paper proposes a new algorithm for the linear least squares problem where the unknown variables are constrained to be in a ﬁnite set. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, applying the Belief Propagation (BP) algorithm yields very poor results. The algorithm described here is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the modiﬁed factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection.

11 nips-2009-A General Projection Property for Distribution Families

Author: Yao-liang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári

Abstract: Surjectivity of linear projections between distribution families with ﬁxed mean and covariance (regardless of dimension) is re-derived by a new proof. We further extend this property to distribution families that respect additional constraints, such as symmetry, unimodality and log-concavity. By combining our results with classic univariate inequalities, we provide new worst-case analyses for natural risk criteria arising in classiﬁcation, optimization, portfolio selection and Markov decision processes. 1

12 nips-2009-A Generalized Natural Actor-Critic Algorithm

Author: Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Kenji Doya

Abstract: Policy gradient Reinforcement Learning (RL) algorithms have received substantial attention, seeking stochastic policies that maximize the average (or discounted cumulative) reward. In addition, extensions based on the concept of the Natural Gradient (NG) show promising learning efﬁciency because these regard metrics for the task. Though there are two candidate metrics, Kakade’s Fisher Information Matrix (FIM) for the policy (action) distribution and Morimura’s FIM for the stateaction joint distribution, but all RL algorithms with NG have followed Kakade’s approach. In this paper, we describe a generalized Natural Gradient (gNG) that linearly interpolates the two FIMs and propose an efﬁcient implementation for the gNG learning based on a theory of the estimating function, the generalized Natural Actor-Critic (gNAC) algorithm. The gNAC algorithm involves a near optimal auxiliary function to reduce the variance of the gNG estimates. Interestingly, the gNAC can be regarded as a natural extension of the current state-of-the-art NAC algorithm [1], as long as the interpolating parameter is appropriately selected. Numerical experiments showed that the proposed gNAC algorithm can estimate gNG efﬁciently and outperformed the NAC algorithm.

13 nips-2009-A Neural Implementation of the Kalman Filter

Author: Robert Wilson, Leif Finkel

Abstract: Recent experimental evidence suggests that the brain is capable of approximating Bayesian inference in the face of noisy input stimuli. Despite this progress, the neural underpinnings of this computation are still poorly understood. In this paper we focus on the Bayesian ﬁltering of stochastic time series and introduce a novel neural network, derived from a line attractor architecture, whose dynamics map directly onto those of the Kalman ﬁlter in the limit of small prediction error. When the prediction error is large we show that the network responds robustly to changepoints in a way that is qualitatively compatible with the optimal Bayesian model. The model suggests ways in which probability distributions are encoded in the brain and makes a number of testable experimental predictions. 1

14 nips-2009-A Parameter-free Hedging Algorithm

Author: Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

Abstract: We study the problem of decision-theoretic online learning (DTOL). Motivated by practical applications, we focus on DTOL when the number of actions is very large. Previous algorithms for learning in this framework have a tunable learning rate parameter, and a barrier to using online-learning in practical applications is that it is not understood how to set this parameter optimally, particularly when the number of actions is large. In this paper, we offer a clean solution by proposing a novel and completely parameter-free algorithm for DTOL. We introduce a new notion of regret, which is more natural for applications with a large number of actions. We show that our algorithm achieves good performance with respect to this new notion of regret; in addition, it also achieves performance close to that of the best bounds achieved by previous algorithms with optimally-tuned parameters, according to previous notions of regret. 1

15 nips-2009-A Rate Distortion Approach for Semi-Supervised Conditional Random Fields

Author: Yang Wang, Gholamreza Haffari, Shaojun Wang, Greg Mori

Abstract: We propose a novel information theoretic approach for semi-supervised learning of conditional random ﬁelds that deﬁnes a training objective to combine the conditional likelihood on labeled data and the mutual information on unlabeled data. In contrast to previous minimum conditional entropy semi-supervised discriminative learning methods, our approach is grounded on a more solid foundation, the rate distortion theory in information theory. We analyze the tractability of the framework for structured prediction and present a convergent variational training algorithm to defy the combinatorial explosion of terms in the sum over label conﬁgurations. Our experimental results show the rate distortion approach outperforms standard l2 regularization, minimum conditional entropy regularization as well as maximum conditional entropy regularization on both multi-class classiﬁcation and sequence labeling problems. 1

16 nips-2009-A Smoothed Approximate Linear Program

Author: Vijay Desai, Vivek Farias, Ciamac C. Moallemi

Abstract: We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP naturally restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program – the ‘smoothed approximate linear program’ – relaxes this restriction in an appropriate fashion while remaining computationally tractable. Doing so appears to have several advantages: First, we demonstrate superior bounds on the quality of approximation to the optimal cost-to-go function aﬀorded by our approach. Second, experiments with our approach on a challenging problem (the game of Tetris) show that the approach outperforms the existing LP approach (which has previously been shown to be competitive with several ADP algorithms) by an order of magnitude. 1

17 nips-2009-A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds

Author: Paris Smaragdis, Madhusudana Shashanka, Bhiksha Raj

Abstract: In this paper we present an algorithm for separating mixed sounds from a monophonic recording. Our approach makes use of training data which allows us to learn representations of the types of sounds that compose the mixture. In contrast to popular methods that attempt to extract compact generalizable models for each sound from training data, we employ the training data itself as a representation of the sources in the mixture. We show that mixtures of known sounds can be described as sparse combinations of the training data itself, and in doing so produce signiﬁcantly better separation results as compared to similar systems based on compact statistical models. Keywords: Example-Based Representation, Signal Separation, Sparse Models. 1

18 nips-2009-A Stochastic approximation method for inference in probabilistic graphical models

Author: Peter Carbonetto, Matthew King, Firas Hamze

Abstract: We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation (LDA). Our framework adopts the methodology of variational inference, but unlike existing variational methods such as mean ﬁeld and expectation propagation it is not restricted to tractable classes of approximating distributions. Our approach can also be viewed as a “population-based” sequential Monte Carlo (SMC) method, but unlike existing SMC methods there is no need to design the artiﬁcial sequence of distributions. Signiﬁcantly, our framework oﬀers a principled means to exchange the variance of an importance sampling estimate for the bias incurred through variational approximation. We conduct experiments on a diﬃcult inference problem in population genetics, a problem that is related to inference for LDA. The results of these experiments suggest that our method can oﬀer improvements in stability and accuracy over existing methods, and at a comparable cost. 1

19 nips-2009-A joint maximum-entropy model for binary neural population patterns and continuous signals

Author: Sebastian Gerwinn, Philipp Berens, Matthias Bethge

Abstract: Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains. Here, we extend this approach to take continuous stimuli into account as well. By constraining the joint secondorder statistics, we obtain a joint Gaussian-Boltzmann distribution of continuous stimuli and binary neural ﬁring patterns, for which we also compute marginal and conditional distributions. This model has the same computational complexity as pure binary models and ﬁtting it to data is a convex problem. We show that the model can be seen as an extension to the classical spike-triggered average/covariance analysis and can be used as a non-linear method for extracting features which a neural population is sensitive to. Further, by calculating the posterior distribution of stimuli given an observed neural response, the model can be used to decode stimuli and yields a natural spike-train metric. Therefore, extending the framework of maximum-entropy models to continuous variables allows us to gain novel insights into the relationship between the ﬁring patterns of neural ensembles and the stimuli they are processing. 1

20 nips-2009-A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers

Author: Sahand Negahban, Bin Yu, Martin J. Wainwright, Pradeep K. Ravikumar

Abstract: High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain consistent procedures unless p/n → 0, a line of recent work has studied models with various types of structure (e.g., sparse vectors; block-structured matrices; low-rank matrices; Markov assumptions). In such settings, a general approach to estimation is to solve a regularized convex program (known as a regularized M -estimator) which combines a loss function (measuring how well the model ﬁts the data) with some regularization function that encourages the assumed structure. The goal of this paper is to provide a uniﬁed framework for establishing consistency and convergence rates for such regularized M estimators under high-dimensional scaling. We state one main theorem and show how it can be used to re-derive several existing results, and also to obtain several new results on consistency and convergence rates. Our analysis also identiﬁes two key properties of loss and regularization functions, referred to as restricted strong convexity and decomposability, that ensure the corresponding regularized M -estimators have fast convergence rates. 1

21 nips-2009-Abstraction and Relational learning

22 nips-2009-Accelerated Gradient Methods for Stochastic Optimization and Online Learning

23 nips-2009-Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models

24 nips-2009-Adapting to the Shifting Intent of Search Queries

25 nips-2009-Adaptive Design Optimization in Experiments with People

26 nips-2009-Adaptive Regularization for Transductive Support Vector Machine

27 nips-2009-Adaptive Regularization of Weight Vectors

28 nips-2009-An Additive Latent Feature Model for Transparent Object Recognition

29 nips-2009-An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism

30 nips-2009-An Integer Projected Fixed Point Method for Graph Matching and MAP Inference

31 nips-2009-An LP View of the M-best MAP problem

32 nips-2009-An Online Algorithm for Large Scale Image Similarity Learning