pwc-archive/papers-with-abstracts huggingface dataset:
Variational Learning for Recurrent Spiking Networks
Authors: Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Variational Inference

We derive a plausible learning rule updating the synaptic efficacies   for feedforward, feedback and lateral connections between observed and latent neurons.    Operating in the context of a generative model for distributions of spike sequences,    the learning mechanism is derived from variational inference principles. The synaptic plasticity rules found   are interesting in that they are strongly reminiscent of experimentally found results on Spike Time    Dependent Plasticity, and in that they differ for excitatory and inhibitory neurons. A simulation   confirms the method's applicability to learning both stationary and temporal spike patterns.
Multiple Instance Learning on Structured Data
Authors: Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Multiple Instance Learning

Most existing Multiple-Instance Learning (MIL) algorithms  assume data instances and/or data bags are independently and  identically distributed. But there often exists rich additional  dependency/structure information between instances/bags within many  applications of MIL. Ignoring this structure information limits the  performance of existing MIL algorithms. This paper explores the  research problem as multiple instance learning on structured  data (MILSD) and formulates a novel framework that considers  additional structure information. In particular, an effective and  efficient optimization algorithm has been proposed to solve the  original non-convex optimization problem by using a combination of  Concave-Convex Constraint Programming (CCCP) method and an adapted  Cutting Plane method, which deals with two sets of constraints caused  by learning on  instances within individual bags and learning on  structured data. Our method has the nice convergence property,  with specified precision on each set of constraints. Experimental  results on three different applications, i.e., webpage  classification, market targeting, and protein fold identification,  clearly demonstrate the advantages of the proposed method over  state-of-the-art methods.
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds
Authors: Nitesh Shroff, Pavan Turaga, Rama Chellappa
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Diversity, Document Summarization

In this paper, we consider the 'Precis' problem of sampling K representative yet diverse data points from a large dataset. This problem arises frequently in applications such as video and document summarization, exploratory data analysis, and pre-filtering. We formulate a general theory which encompasses not just traditional techniques devised for vector spaces, but also non-Euclidean manifolds, thereby enabling these techniques to shapes, human activities, textures and many other image and video based datasets. We propose intrinsic manifold measures for measuring the quality of a selection of points with respect to their representative power, and their diversity. We then propose efficient algorithms to optimize the cost function using a novel annealing-based iterative alternation algorithm. The proposed formulation is applicable to manifolds of known geometry as well as to manifolds whose geometry needs to be estimated from samples. Experimental results show the strength and generality of the proposed approach.
A Global Structural EM Algorithm for a Model of Cancer Progression
Authors: Ali Tofigh, Erik Sj̦Lund, Mattias H̦Glund, Jens Lagergren
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Cancer has complex patterns of progression that include converging as  well as diverging progressional pathways. Vogelstein's path model of  colon cancer was a pioneering contribution to cancer research.  Since  then, several attempts have been made at obtaining mathematical models  of cancer progression, devising learning algorithms, and applying  these to cross-sectional data. Beerenwinkel {\em et al.}  provided,  what they coined, EM-like algorithms for Oncogenetic Trees (OTs) and  mixtures of such. Given the small size of current and future data  sets, it is important to minimize the number of parameters of a  model. For this reason, we too focus on tree-based models and  introduce Hidden-variable Oncogenetic Trees (HOTs). In contrast to  OTs, HOTs allow for errors in the data and thereby provide more  realistic modeling. We also design global structural EM algorithms for  learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are  global in the sense that, during the M-step, they find a structure  that yields a global maximum of the expected complete log-likelihood  rather than merely one that improves it. The algorithm for single HOTs  performs very well on reasonable-sized data sets, while that for  HOT-mixtures requires data sets of sizes obtainable only with  tomorrow's more cost-efficient technologies.
Action-Gap Phenomenon in Reinforcement Learning
Authors: Amir-Massoud Farahmand
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many practitioners of reinforcement learning problems have observed that oftentimes  the performance of the agent reaches very close to the optimal performance even though the estimated (action-)value function is still far from the optimal one.  The goal of this paper is to explain and formalize this phenomenon by introducing the concept of the action-gap regularity.  As a typical result, we prove that for an agent following the greedy policy \(\hat{\pi}\) with respect to an action-value function \(\hat{Q}\), the performance loss \(E[V^*(X) - V^{\hat{X}} (X)]\) is upper bounded by \(O(|| \hat{Q} - Q^*||_\infty^{1+\zeta}\)), in which \(\zeta >= 0\) is the parameter quantifying the action-gap regularity.  For \(\zeta > 0\), our results indicate smaller performance loss compared to what previous analyses had suggested.  Finally, we show how this regularity affects the performance of the family of approximate value iteration algorithms.
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition
Authors: Nobuyuki Morioka, Shin'ichi Satoh
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Object Recognition

Sparse coding, a method of explaining sensory data with as few dictionary bases as possible, has attracted much attention in computer vision. For visual object category recognition, L1 regularized sparse coding is combined with spatial pyramid representation to obtain state-of-the-art performance. However, because of its iterative optimization, applying sparse coding onto every local feature descriptor extracted from an image database can become a major bottleneck. To overcome this computational challenge, this paper presents "Generalized Lasso based Approximation of Sparse coding" (GLAS). By representing the distribution of sparse coefficients with slice transform, we fit a piece-wise linear mapping function with generalized lasso. We also propose an efficient post-refinement procedure to perform mutual inhibition between bases which is essential for an overcomplete setting. The experiments show that GLAS obtains comparable performance to L1 regularized sparse coding, yet achieves significant speed up demonstrating its effectiveness for large-scale visual recognition problems.
Multi-View Learning of Word Embeddings via CCA
Authors: Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Chunking, MULTI-VIEW LEARNING, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

Recently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks. However, most current approaches are slow to train, do not model context of the word, and lack theoretical grounding. In this paper, we present a new learning method, Low Rank Multi-View Learning (LR-MVL) which uses a fast spectral method to estimate  low dimensional context-specific word representations from unlabeled data. These representation features can then be used with any supervised learner. LR-MVL is extremely fast, gives guaranteed convergence to a global optimum, is theoretically elegant, and achieves state-of-the-art performance on named entity recognition (NER) and chunking problems.
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent
Authors: Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Variational Bayesian matrix factorization (VBMF) efficiently   approximates the posterior distribution of factorized matrices   by assuming matrix-wise independence of the two factors.  A recent study on fully-observed VBMF  showed that, under a stronger assumption that the two factorized matrices are  column-wise independent,  the global optimal solution can be analytically computed.  However, it was not clear how restrictive  the column-wise independence assumption is.  In this paper, we prove that the global solution under matrix-wise  independence is actually column-wise independent,  implying that the column-wise independence assumption is harmless.  A practical consequence of our theoretical finding is that  the global solution under matrix-wise independence (which is a standard setup)  can be obtained analytically in a computationally very efficient way  without any iterative algorithms.  We experimentally illustrate advantages of using our analytic solution  in probabilistic principal component analysis.
Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron
Authors: Ryota Kobayashi, Yasuhiro Tsubo, Petr Lansky, Shigeru Shinomoto
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

State-of-the-art statistical methods in neuroscience have enabled us to fit mathematical models to experimental data and subsequently to infer the dynamics of hidden parameters underlying the observable phenomena. Here, we develop a Bayesian method for inferring the time-varying mean and variance of the synaptic input, along with the dynamics of each ion channel from a single voltage trace of a neuron. An estimation problem may be formulated on the basis of the state-space model with prior distributions that penalize large fluctuations in these parameters. After optimizing the hyperparameters by maximizing the marginal likelihood, the state-space model provides the time-varying parameters of the input signals and the ion channel states. The proposed method is tested not only on the simulated data from the Hodgkin-Huxley type models but also on experimental data obtained from a cortical slice in vitro.
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction
Authors: Hai-Son P. Le, Ziv Bar-Joseph
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Clustering

Determining interactions between entities and the overall organization and clustering of nodes in networks is a major challenge when analyzing biological and social network data. Here we extend the Indian Buffet Process (IBP), a nonparametric Bayesian model, to integrate noisy interaction scores with properties of individual entities for inferring interaction networks and clustering nodes within these networks. We present an application of this method to study how microRNAs regulate mRNAs in cells. Analysis of synthetic and real data indicates that the method improves upon prior methods, correctly recovers interactions and clusters, and provides accurate biological predictions.
Semantic Labeling of 3D Point Clouds for Indoor Scenes
Authors: Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Object

Inexpensive RGB-D cameras that give an RGB image together with depth data have become widely available. In this paper, we use this data to build 3D point clouds of full indoor scenes such as an office and address the task of semantic labeling of these 3D point clouds. We propose a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurence relationships and geometric relationships. With a large number of object classes and relations, the model’s parsimony becomes important and we address that by using multiple types of edge potentials. The model admits efficient approximate inference, and we train it using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views, having 2495 segments labeled with 27 object classes), we get a performance of 84.06% in labeling 17 object classes for offices, and 73.38% in labeling 17 object classes for home scenes. Finally, we applied these algorithms successfully on a mobile robot for the task of finding objects in large cluttered rooms.
Learning Higher-Order Graph Structure with Features by Structure Penalty
Authors: Shilin Ding, Grace Wahba, Jerry Zhu
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure. We study the case where there is another input random vector X (e.g. observed features) such that the distribution P (Y | X) is determined by functions of X that characterize the (higher-order) interactions among the Y ’s. The main contribution of this paper is to learn the graph structure and the functions conditioned on X at the same time. We prove that discrete undirected graphical models with feature X are equivalent to mul- tivariate discrete models. The reparameterization of the potential functions in graphical models by conditional log odds ratios of the latter offers advantages in representation of the conditional independence structure. The functional spaces can be flexibly determined by kernels. Additionally, we impose a Structure Lasso (SLasso) penalty on groups of functions to learn the graph structure. These groups with overlaps are designed to enforce hierarchical function selection. In this way, we are able to shrink higher order interactions to obtain a sparse graph structure.
Analysis and Improvement of Policy Gradient Estimation
Authors: Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient is a useful model-free reinforcement learning approach,  but it tends to suffer from instability of gradient estimates.  In this paper, we analyze and improve the stability of policy gradient methods. We first prove that the variance of gradient estimates in the PGPE(policy gradients with parameter-based exploration) method is smaller than that of the classical REINFORCE method under a mild assumption.  We then derive the optimal baseline for PGPE, which contributes to further reducing the variance.  We also theoretically show that PGPE with the optimal baseline is more preferable than REINFORCE with the optimal baseline in terms of the variance of gradient estimates.  Finally, we demonstrate the usefulness of the improved PGPE method through experiments.
Dimensionality Reduction Using the Sparse Linear Model
Authors: Ioannis A. Gkioulekas, Todd Zickler
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: compressed sensing, Dimensionality Reduction, model

We propose an approach for linear unsupervised dimensionality reduction, based on the sparse linear model that has been used to probabilistically interpret sparse coding. We formulate an optimization problem for learning a linear projection from the original signal domain to a lower-dimensional one in a way that approximately preserves, in expectation, pairwise inner products in the sparse domain. We derive solutions to the problem, present nonlinear extensions, and discuss relations to compressed sensing. Our experiments using facial images, texture patches, and images of object categories suggest that the approach can improve our ability to recover meaningful structure in many classes of signals.
Robust Multi-Class Gaussian Process Classification
Authors: Daniel Hernández-Lobato, Jose M. Hernández-Lobato, Pierre Dupont
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Classification, General Classification

Multi-class Gaussian Process Classifiers (MGPCs) are often affected by over-fitting problems when labeling errors occur far from the decision boundaries. To prevent this, we investigate a robust MGPC (RMGPC) which considers labeling errors independently of their distance to the decision boundaries. Expectation propagation is used for approximate inference. Experiments with several datasets in which noise is injected in the class labels illustrate the benefits of RMGPC. This method performs better than other Gaussian process alternatives based on considering latent Gaussian noise or heavy-tailed processes. When no noise is injected in the labels, RMGPC still performs equal or better than the other methods. Finally, we show how RMGPC can be used for successfully identifying data instances which are difficult to classify accurately in practice.
Maximum Margin Multi-Label Structured Prediction
Authors: Christoph H. Lampert
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Generalization Bounds, Graph Matching, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, object-detection, Object Detection, Prediction, Structured Prediction

We study multi-label prediction for structured output spaces, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multi-label classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label space, which is infeasible in case of structured outputs. Relying on techniques originally designed for single- label structured prediction, in particular structured support vector machines, results in reduced prediction accuracy, or leads to infeasible optimization problems.  In this work we derive a maximum-margin training formulation for multi-label structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with single-label maximum-margin approaches, in particular a formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds.
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network
Authors: Ke Chen, Ahmad Salman
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Speaker Recognition

Speech conveys different yet mixed information ranging from linguistic to speaker-specific components, and each of them should be exclusively used in a specific task. However, it is extremely difficult to extract a specific information component given the fact that nearly all existing acoustic representations carry all types of speech information. Thus, the use of the same representation in both speech and speaker recognition hinders a system from producing better performance due to interference of irrelevant information. In this paper, we present a deep neural architecture to extract speaker-specific information from MFCCs. As a result, a multi-objective loss function is proposed for learning speaker-specific characteristics and regularization via normalizing interference of non-speaker related information and avoiding information loss. With LDC benchmark corpora and a Chinese speech corpus, we demonstrate that a resultant speaker-specific representation is insensitive to text/languages spoken and environmental mismatches and hence outperforms MFCCs and other state-of-the-art techniques in speaker recognition. We discuss relevant issues and relate our approach to previous work.
Thinning Measurement Models and Questionnaire Design
Authors: Ricardo Silva
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Marketing

Inferring key unobservable features of individuals is an important task in the applied sciences. In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features. While comprehensive surveys help to better estimate the latent variables of interest, aiming at a high number of questions comes at a price: refusal to participate in surveys can go up, as well as the rate of missing data; quality of answers can decline; costs associated with applying such questionnaires can also increase. In this paper, we cast the problem of refining existing models for questionnaire data as follows: solve a constrained optimization problem of preserving the maximum amount of information found in a latent variable model using only a subset of existing questions. The goal is to find an optimal subset of a given size. For that, we first define an information theoretical measure for quantifying the quality of a reduced questionnaire. Three different approximate inference methods are introduced to solve this problem. Comparisons against a simple but powerful heuristic are presented.
Inductive reasoning about chimeric creatures
Authors: Charles Kemp
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Given one feature of a novel animal, humans readily make inferences about other features of the animal. For example, winged creatures often fly, and creatures that eat fish often live in the water. We explore the knowledge that supports these inferences and compare two approaches. The first approach proposes that humans rely on abstract representations of dependency relationships between features, and is formalized here as a graphical model.  The second approach proposes that humans rely on specific knowledge of previously encountered animals, and is formalized here as a family of exemplar models. We evaluate these models using a task where participants reason about chimeras, or animals with pairs of features that have not previously been observed to co-occur. The results support the hypothesis that humans rely on explicit representations of relationships between features.
Optimal Reinforcement Learning for Gaussian Systems
Authors: Philipp Hennig
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Gaussian Processes, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The exploration-exploitation trade-off is among the central challenges of reinforcement learning. The optimal Bayesian solution is intractable in general. This paper studies to what extent analytic statements about optimal learning are possible if all beliefs are Gaussian processes. A first order approximation of learning of both loss and dynamics, for nonlinear, time-varying systems in continuous time and space, subject to a relatively weak restriction on the dynamics, is described by an infinite-dimensional partial differential equation. An approximate finite-dimensional projection gives an impression for how this result may be helpful.