NeurIPS 2011

306 papers

Greedy Algorithms for Structurally Constrained High Dimensional Problems

Ambuj Tewari, Inderjit S Dhillon, Pradeep K Ravikumar, Pradeep K. Ravikumar, Inderjit S. Dhillon

An ideal observer model for identifying the reference frame of objects

Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths

Gaussian process modulated renewal processes

Yee W. Teh, Vinayak Rao

How biased are maximum entropy models?

Jakob H. Macke, Iain Murray, Peter E. Latham

Pylon Model for Semantic Segmentation

Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman

Transfer from Multiple MDPs

Alessandro Lazaric, Marcello Restelli

The Fixed Points of Off-Policy TD

J. Z. Kolter

Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction

Elad Hazan, Satyen Kale

Sparse Estimation with Structured Dictionaries

David P. Wipf

Collective Graphical Models

Daniel R. Sheldon, Thomas G. Dietterich

Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities

Angela Yao, Juergen Gall, Raquel Urtasun, Luc V Gool, Luc V. Gool

Submodular Multi-Label Learning

James Petterson, Tibério S. Caetano

Sparse Manifold Clustering and Embedding

Ehsan Elhamifar, René Vidal

Joint 3D Estimation of Objects and Scene Layout

Andreas Geiger, Christian Wojek, Raquel Urtasun

Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron

Ryota Kobayashi, Yasuhiro Tsubo, Petr Lansky, Shigeru Shinomoto

TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning

George Konidaris, Scott Niekum, Philip S. Thomas

Structured sparse coding via lateral inhibition

Arthur D. Szlam, Karol Gregor, Yann L. Cun

Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis

Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen

A rational model of causal inference with continuous causes

Thomas L. Griffiths, Michael James

How Do Humans Teach: On Curriculum Learning and Teaching Dimension

Faisal Khan, Bilge Mutlu, Xiaojin Zhu

Solving Decision Problems with Limited Information

Denis D. Maua, Cassio Campos

Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms

Liefeng Bo, Xiaofeng Ren, Dieter Fox

Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification

Ichiro Takeuchi, Masashi Sugiyama

Differentially Private M-Estimators

Jing Lei

Reconstructing Patterns of Information Diffusion from Incomplete Observations

Flavio Chierichetti, David Liben-nowell, Jon M. Kleinberg

Demixed Principal Component Analysis

Wieland Brendel, Ranulfo Romo, Christian K. Machens

Learning with the weighted trace-norm under arbitrary sampling distributions

Rina Foygel, Ohad Shamir, Nati Srebro, Ruslan Salakhutdinov

Contextual Gaussian Process Bandit Optimization

Andreas Krause, Cheng S. Ong

An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments

Michael Mozer, Benjamin Link, Harold Pashler

Distributed Delayed Stochastic Optimization

Alekh Agarwal, John C. Duchi

Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels

Vikas Sindhwani, Aurelie C. Lozano

Monte Carlo Value Iteration with Macro-Actions

Zhan Lim, Lee Sun, David Hsu

Learning a Tree of Metrics with Disjoint Visual Features

Kristen Grauman, Fei Sha, Sung Ju Hwang

Learning to Search Efficiently in High Dimensions

Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong

Probabilistic Joint Image Segmentation and Labeling

Adrian Ion, Joao Carreira, Cristian Sminchisescu

Multiple Instance Learning on Structured Data

Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence

Randomized Algorithms for Comparison-based Search

Dominique Tschopp, Suhas Diggavi, Payam Delgosha, Soheil Mohajer

Group Anomaly Detection using Flexible Genre Models

Liang Xiong, Barnabás Póczos, Jeff G. Schneider

On the accuracy of l1-filtering of signals with block-sparse structure

Fatma K. Karzan, Arkadi S. Nemirovski, Boris T. Polyak, Anatoli Juditsky

A Convergence Analysis of Log-Linear Training

Simon Wiesler, Hermann Ney

Universal low-rank matrix recovery from Pauli measurements

Yi-kai Liu

Inference in continuous-time change-point models

Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor

Quasi-Newton Methods for Markov Chain Monte Carlo

Yichuan Zhang, Charles A. Sutton

Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning

Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin J. Latecki

Efficient Offline Communication Policies for Factored Multiagent POMDPs

João V. Messias, Matthijs Spaan, Pedro U. Lima

An Empirical Evaluation of Thompson Sampling

Olivier Chapelle, Lihong Li

Optimal Reinforcement Learning for Gaussian Systems

Philipp Hennig

From Bandits to Experts: On the Value of Side-Observations

Shie Mannor, Ohad Shamir

Improved Algorithms for Linear Stochastic Bandits

Dávid Pál, Csaba Szepesvári, Yasin Abbasi Yadkori, Yasin Abbasi-Yadkori

Unsupervised learning models of primary cortical receptive fields and receptive field plasticity

Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Y. Ng

Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery

Scott Niekum, Andrew G. Barto

A blind sparse deconvolution method for neural spike identification

Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli

Transfer Learning by Borrowing Examples for Multiclass Object Detection

Joseph J. Lim, Ruslan Salakhutdinov, Antonio Torralba

Blending Autonomous Exploration and Apprenticeship Learning

Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison

Selective Prediction of Financial Trends with Hidden Markov Models

Dmitry Pidan, Ran El-Yaniv

Learning a Distance Metric from a Network

Blake Shaw, Bert Huang, Tony Jebara

Kernel Embeddings of Latent Tree Graphical Models

Le Song, Eric P. Xing, Ankur P. Parikh

Noise Thresholds for Spectral Clustering

Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh

Maximum Covariance Unfolding : Manifold Learning for Bimodal Data

Vijay Mahadevan, Chi W. Wong, Jose C. Pereira, Tom Liu, Nuno Vasconcelos

θ-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding

Congcong Li, Ashutosh Saxena, Tsuhan Chen

A reinterpretation of the policy oscillation phenomenon in approximate policy iteration

Paul Wagner

Multiple Instance Filtering

Kamil A. Wnuk, Stefano Soatto

On fast approximate submodular minimization

Stefanie Jegelka, Hui Lin, Jeff A. Bilmes

Advice Refinement in Knowledge-Based SVMs

Gautam Kunapuli, Richard Maclin, Jude W. Shavlik

Variance Reduction in Monte-Carlo Tree Search

Joel Veness, Marc Lanctot, Michael Bowling

A Two-Stage Weighting Framework for Multi-Source Domain Adaptation

Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye

Sparse recovery by thresholded non-negative least squares

Martin Slawski, Matthias Hein

On Causal Discovery with Cyclic Additive Noise Models

Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

Large-Scale Category Structure Aware Image Categorization

Bin Zhao, Fei Li, Eric P. Xing

Committing Bandits

Loc X. Bui, Ramesh Johari, Shie Mannor

Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus

Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds

Nitesh Shroff, Pavan Turaga, Rama Chellappa

The Manifold Tangent Classifier

Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller

Relative Density-Ratio Estimation for Robust Distribution Comparison

Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama

Learning unbelievable probabilities

Zachary Pitkow, Yashar Ahmadian, Ken D. Miller

Inferring Interaction Networks using the IBP applied to microRNA Target Prediction

Hai-son P. Le, Ziv Bar-joseph

A Model for Temporal Dependencies in Event Streams

Asela Gunawardana, Christopher Meek, Puyang Xu

Environmental statistics and the trade-off between model-based and TD learning in humans

Dylan A. Simon, Nathaniel D. Daw

Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs

Armen Allahverdyan, Aram Galstyan

Crowdclustering

Ryan G. Gomes, Peter Welinder, Andreas Krause, Pietro Perona

Minimax Localization of Structural Information in Large Noisy Matrices

Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh

Iterative Learning for Reliable Crowdsourcing Systems

David R. Karger, Sewoong Oh, Devavrat Shah

EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning

Feng Yan, Yuan Qi

Nonlinear Inverse Reinforcement Learning with Gaussian Processes

Sergey Levine, Zoran Popovic, Vladlen Koltun

Multi-Bandit Best Arm Identification

Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck

Probabilistic amplitude and frequency demodulation

Richard Turner, Maneesh Sahani

Shaping Level Sets with Submodular Functions

Francis R. Bach

Active dendrites: adaptation to spike-based communication

Balazs B. Ujfalussy, Máté Lengyel

Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC

Trung T. Pham, Tat-jun Chin, Jin Yu, David Suter

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

Philipp Krähenbühl, Vladlen Koltun

Thinning Measurement Models and Questionnaire Design

Ricardo Silva

Semi-supervised Regression via Parallel Field Regularization

Binbin Lin, Chiyuan Zhang, Xiaofei He

Algorithms and hardness results for parallel large margin learning

Phil Long, Rocco Servedio

Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent

Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan

Energetically Optimal Action Potentials

Martin B. Stemmler, Biswa Sengupta, Simon Laughlin, Jeremy Niven

Generalizing from Several Related Classification Tasks to a New Unlabeled Sample

Gilles Blanchard, Gyemin Lee, Clayton Scott

Better Mini-Batch Algorithms via Accelerated Gradient Methods

Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan

Reinforcement Learning using Kernel-Based Stochastic Factorization

Andre Barreto, Doina Precup, Joelle Pineau

Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability

David P. Reichert, Peggy Series, Amos J. Storkey

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning

Michalis K. Titsias, Miguel Lázaro-Gredilla