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Session 1A — Optimization algorithms 1

chair Elad Hazan, room AT LT 4

  1. On the Equivalence between Herding and Conditional Gradient Algorithms Francis Bach, Simon Lacoste-Julien, Guillaume Obozinski
  2. Similarity Learning for Provably Accurate Sparse Linear Classification Aurélien Bellet, Amaury Habrard, Marc Sebban
  3. Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure Hua Ouyang, Alexander Gray
  4. Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
  5. Scaling Up Coordinate Descent Algorithms for Large ℓ_1 Regularization Problems Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, David Haglin
  6. Quasi-Newton Methods: A New Direction Philipp Hennig, Martin Kiefel
  7. A Hybrid Algorithm for Convex Semidefinite Optimization Soeren Laue
  8. Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization Haim Avron, Satyen Kale, Shiva Kasiviswanathan, Vikas Sindhwani

Session 1B — Reinforcement learning 1

chair David Silver, room AT LT 5

  1. Policy Gradients with Variance Related Risk Criteria Dotan Di Castro, Aviv Tamar, Shie Mannor
  2. Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds Marek Petrik
  3. Statistical linear estimation with penalized estimators: an application to reinforcement learning Bernardo Avila Pires, Csaba Szepesvari
  4. Approximate Modified Policy Iteration Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, Matthieu Geist
  5. A Dantzig Selector Approach to Temporal Difference Learning Matthieu Geist, Bruno Scherrer, Alessandro Lazaric, Mohammad Ghavamzadeh
  6. Linear Off-Policy Actor-Critic Thomas Degris, Martha White, Richard Sutton
  7. Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty Shie Mannor, Ofir Mebel, Huan Xu
  8. Bounded Planning in Passive POMDPs Roy Fox, Naftali Tishby

Session 1C — Neural networks and deep learning 1

chair Marc'Aurelio Ranzato, room AT LT 1

  1. Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Clément Farabet, Camille Couprie, Laurent Najman, Yann LeCun
  2. A Generative Process for Contractive Auto-Encoders Salah Rifai, Yann Dauphin, Pascal Vincent, Yoshua Bengio
  3. Deep Lambertian Networks Yichuan Tang, Ruslan Salakhutdinov, Geoffrey Hinton
  4. Deep Mixtures of Factor Analysers Yichuan Tang, Ruslan Salakhutdinov, Geoffrey Hinton
  5. Utilizing Static Analysis and Code Generation to Accelerate Neural Networks Lawrence McAfee, Kunle Olukotun
  6. Estimating the Hessian by Back-propagating Curvature James Martens, Ilya Sutskever, Kevin Swersky
  7. Training Restricted Boltzmann Machines on Word Observations George Dahl, Ryan Adams, Hugo Larochelle
  8. A fast and simple algorithm for training neural probabilistic language models Andriy Mnih, Yee Whye Teh

Session 1D — Structured output prediction

chair David McAllester, room AT LT 2

  1. Learning to Identify Regular Expressions that Describe Email Campaigns Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer
  2. Efficient Structured Prediction with Latent Variables for General Graphical Models Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
  3. Output Space Search for Structured Prediction Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli
  4. Efficient Decomposed Learning for Structured Prediction Rajhans Samdani, Dan Roth
  5. Modeling Latent Variable Uncertainty for Loss-based Learning M. Pawan Kumar, Ben Packer, Daphne Koller

Session 2A — Kernel methods 1

chair Arthur Gretton, room AT LT 4

  1. On the Size of the Online Kernel Sparsification Dictionary Yi Sun, Faustino Gomez, Juergen Schmidhuber
  2. Improved Nystrom Low-rank Decomposition with Priors Kai Zhang, Liang Lan, Jun Liu, andreas Rauber
  3. Bayesian Efficient Multiple Kernel Learning Mehmet Gönen
  4. A Binary Classification Framework for Two-Stage Multiple Kernel Learning Abhishek Kumar, Alexandru Niculescu-Mizil, Koray Kavukcuoglu, Hal Daume III
  5. Multiple Kernel Learning from Noisy Labels by Stochastic Programming Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou,
  6. Subgraph Matching Kernels for Attributed Graphs Nils Kriege, Petra Mutzel
  7. Fast Computation of Subpath Kernel for Trees Daisuke Kimura, Hisashi Kashima
  8. Hypothesis testing using pairwise distances and associated kernels Dino Sejdinovic, Arthur Gretton, Bharath Sriperumbudur, Kenji Fukumizu

Session 2B — Reinforcement learning 2

chair Geoff Gordon, room AT LT 5

  1. No-Regret Learning in Extensive-Form Games with Imperfect Recall Marc Lanctot, Richard Gibson, Neil Burch, Michael Bowling
  2. Near-Optimal BRL using Optimistic Local Transitions Mauricio Araya, Olivier Buffet, Vincent Thomas
  3. Continuous Inverse Optimal Control with Locally Optimal Examples Sergey Levine, Vladlen Koltun
  4. Monte Carlo Bayesian Reinforcement Learning Yi Wang, Kok Sung Won, David Hsu, Wee Sun Lee
  5. Apprenticeship Learning for Model Parameters of Partially Observable Environments Takaki Makino, Johane Takeuchi

Session 2C — Gaussian processes

chair Ryan Adams, room AT LT 1

  1. Gaussian Process Regression Networks Andrew Wilson, David A. Knowles, Zoubin Ghahramani
  2. Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis Zenglin Xu, Feng Yan, Alan Qi
  3. State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction Jouni Hartikainen, Mari Seppänen, Simo Särkkä
  4. Gaussian Process Quantile Regression using Expectation Propagation Alexis Boukouvalas, Remi Barillec, Dan Cornford
  5. Residual Components Analysis Alfredo Kalaitzis, Neil Lawrence
  6. Manifold Relevance Determination Andreas Damianou, Carl Ek, Michalis Titsias, Neil Lawrence

Session 2D — Statistical methods

chair Lawrence Carin, room AT LT 2

  1. Lognormal and Gamma Mixed Negative Binomial Regression Mingyuan Zhou, Lingbo Li, David Dunson, Lawrence Carin
  2. Group Sparse Additive Models Junming Yin, Xi Chen, eric xing
  3. Variance Function Estimation in High-dimensions Mladen Kolar, James Sharpnack
  4. Sparse Additive Functional and Kernel CCA Sivaraman Balakrishnan, Kriti Puniyani, John Lafferty
  5. Consistent Covariance Selection From Data With Missing Values Mladen Kolar, eric xing
  6. Conditional Sparse Coding and Grouped Multivariate Regression Min Xu, John Lafferty
  7. Is margin preserved after random projection? Qinfeng Shi, Chunhua Shen, Rhys Hill, Anton van den Hengel

Session 3A — Optimization algorithms 2

chair Tong Zhang, room AT LT 4

  1. A Discrete Optimization Approach for Supervised Ranking with an Application to Reverse-Engineering Quality Ratings Allison Chang, Cynthia Rudin, Dimitris Bertsimas, Michael Cavaretta, Robert Thomas, Gloria Chou
  2. A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem Lin Xiao, Tong Zhang
  3. Complexity Analysis of the Lasso Regularization Path Julien Mairal, Bin Yu
  4. Randomized Smoothing for (Parallel) Stochastic Optimization John Duchi, Martin Wainwright, Peter Bartlett

Session 3B — Clustering 1

chair Shai Ben-David, room AT LT 5

  1. Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events Jesse Davis, Vitor Santos Costa, Elizabeth Berg, David Page, Peggy Peissig, Michael Caldwell
  2. Clustering to Maximize the Ratio of Split to Diameter Jiabing Wang, Jiaye Chen
  3. An Iterative Locally Linear Embedding Algorithm Deguang Kong, Chris H.Q. Ding
  4. Robust Multiple Manifold Structure Learning Dian Gong, Xuemei Zhao, Gerard Medioni
  5. A Split-Merge Framework for Comparing Clusterings Qiaoliang Xiang, Qi Mao, Kian Ming Chai, Hai Leong Chieu, Ivor Tsang, Zhenddong Zhao
  6. On the Difficulty of Nearest Neighbor Search Junfeng He, Sanjiv Kumar, Shih-Fu Chang

Session 3C — Privacy, Anonymity, and Security

chair Tobias Scheffer, room AT LT 1

  1. Bayesian Watermark Attacks Ivo Shterev, David Dunson
  2. Poisoning Attacks against Support Vector Machines Battista Biggio, Blaine Nelson, Pavel Laskov
  3. Convergence Rates for Differentially Private Statistical Estimation Kamalika Chaudhuri, Daniel Hsu
  4. Finding Botnets Using Minimal Graph Clusterings Peter Haider, Tobias Scheffer

Session 3D — Ranking and Preference Learning

chair Balazs Kegl, room AT LT 2

  1. Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis Deepti Pachauri, Maxwell Collins, Vikas SIngh
  2. Consistent Multilabel Ranking through Univariate Losses Krzysztof Dembczyński, Wojciech Kotłowski, Eyke Huellermeier
  3. Predicting Consumer Behavior in Commerce Search Or Sheffet, Nina Mishra, Samuel Ieong
  4. Adaptive Regularization for Similarity Measures Koby Crammer, Gal Chechik
  5. Online Structured Prediction via Coactive Learning Pannaga Shivaswamy, Thorsten Joachims
  6. TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings Chao Liu, Yi-Min Wang

Session 3E — Nonparametric Bayesian inference

chair Sharon Goldwater, room AT LT 3

  1. Factorized Asymptotic Bayesian Hidden Markov Models Ryohei Fujimaki, Kohei Hayashi
  2. An Infinite Latent Attribute Model for Network Data Konstantina Palla, David A. Knowles, Zoubin Ghahramani
  3. The Nonparametric Metadata Dependent Relational Model Dae Il Kim, Michael Hughes, Erik Sudderth
  4. Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling Changyou Chen, Nan Ding, Wray Buntine
  5. A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling Drausin Wulsin, Shane Jensen, Brian Litt
  6. Modeling Images using Transformed Indian Buffet Processes KE ZHAI, Yuening Hu, Jordan Boyd-Graber, Sinead Williamson
  7. A Topic Model for Melodic Sequences Athina Spiliopoulou, Amos Storkey

Session 4A — Feature selection and dimensionality reduction 1

chair Kilian Weinberger, room AT LT 4

  1. Discovering Support and Affiliated Features from Very High Dimensions Yiteng Zhai, Mingkui Tan, Ivor Tsang, Yew Soon Ong
  2. Inferring Latent Structure From Mixed Real and Categorical Relational Data Esther Salazar, Lawrence Carin
  3. Conditional Likelihood Maximization: A Unifying Framework for Information Theoretic Feature Selection Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Lujan
  4. Dimensionality Reduction by Local Discriminative Gaussians Nathan Parrish, Maya Gupta
  5. Fast Prediction of New Feature Utility Hoyt Koepke, Mikhail Bilenko

Session 4B — Online learning 1

chair Satyen Kale, room AT LT 5

  1. An Online Boosting Algorithm with Theoretical Justifications Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu
  2. An adaptive algorithm for finite stochastic partial monitoring Gabor Bartok, Navid Zolghadr, Csaba Szepesvari
  3. Online Alternating Direction Method Huahua Wang, Arindam Banerjee
  4. Projection-free Online Learning Elad Hazan, Satyen Kale
  5. PAC Subset Selection in Stochastic Multi-armed Bandits Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone
  6. On Local Regret Michael Bowling, Martin Zinkevich
  7. Exact Soft Confidence-Weighted Learning Steven C.H. Hoi, Jialei Wang, Peilin Zhao
  8. Compact Hyperplane Hashing with Bilinear Functions Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang

Session 4C — Supervised learning 1

chair Cynthia Rudin, room AT LT 1

  1. Improved Information Gain Estimates for Decision Tree Induction Sebastian Nowozin
  2. Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation Kendrick Boyd, Jesse Davis, David Page, Vitor Santos Costa
  3. The Big Data Bootstrap Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael Jordan
  4. Robust Classification with Adiabatic Quantum Optimization Vasil Denchev, Nan Ding, SVN Vishwanathan, Hartmut Neven
  5. Nonparametric Link Prediction in Dynamic Networks Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
  6. A Unified Robust Classification Model Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
  7. Maximum Margin Output Coding Yi Zhang, Jeff Schneider
  8. Structured Learning from Partial Annotations Xinghua Lou, Fred Hamprecht

Session 4D — Transfer and Multi-Task Learning

chair Jenn Wortman Vaughan, room AT LT 2

  1. Marginalized Denoising Autoencoders for Domain Adaptation Minmin Chen, Zhixiang Xu, Kilian Weinberger, Fei Sha
  2. Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation Yuan Shi, Fei Sha
  3. Learning Task Grouping and Overlap in Multi-task Learning Abhishek Kumar, Hal Daume III
  4. A Convex Feature Learning Formulation for Latent Task Structure Discovery Pratik Jawanpuria, J. Saketha Nath
  5. Convex Multitask Learning with Flexible Task Clusters Wenliang Zhong, James Kwok
  6. A Complete Analysis of the l_1,p Group-Lasso Julia Vogt, Volker Roth
  7. Learning with Augmented Features for Heterogeneous Domain Adaptation Lixin Duan, Dong Xu, Ivor Tsang
  8. Cross-Domain Multitask Learning with Latent Probit Models Shaobo Han, Xuejun Liao, Lawrence Carin

Session 4E — Graphical models

chair Matthias Seeger, room AT LT 3

  1. High Dimensional Semiparametric Gaussian Copula Graphical Models Han Liu, Fang Han, Ming Yuan, John Lafferty, Larry Wasserman
  2. Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models Jean Honorio
  3. On the Partition Function and Random Maximum A-Posteriori Perturbations Tamir Hazan, Tommi Jaakkola
  4. Anytime Marginal MAP Inference Denis Maua, Cassio De Campos
  5. Exact Maximum Margin Structure Learning of Bayesian Networks Robert Peharz, Franz Pernkopf
  6. LPQP for MAP: Putting LP Solvers to Better Use Patrick Pletscher, Sharon Wulff
  7. How To Grade a Test Without Knowing the Answers — A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing Yoram Bachrach, Thore Graepel, Tom Minka, John Guiver
  8. Smoothness and Structure Learning by Proxy Benjamin Yackley, Terran Lane

Session 5A — Learning theory

chair Daniel Hsu, room AT LT 4

  1. Linear Regression with Limited Observation Elad Hazan, Tomer Koren
  2. Optimizing F-measure: A Tale of Two Approaches Ye Nan, Kian Ming Chai, Wee Sun Lee, Hai Leong Chieu
  3. Conditional mean embeddings as regressors Steffen Grunewalder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massi Pontil
  4. PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification Emilie Morvant, Sokol Koço, Liva Ralaivola
  5. Tighter Variational Representations of f-Divergences via Restriction to Probability Measures Avraham Ruderman, Mark Reid, Darío García-García, James Petterson
  6. Agglomerative Bregman Clustering Matus Telgarsky, Sanjoy Dasgupta
  7. The Convexity and Design of Composite Multiclass Losses Mark Reid, Robert Williamson, Peng Sun
  8. Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan

Session 5B — Online learning 2

chair Csaba Szepesvari, room AT LT 5

  1. Hierarchical Exploration for Accelerating Contextual Bandits Yisong Yue, Sue Ann Hong, Carlos Guestrin
  2. Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret Ofer Dekel, Ambuj Tewari, Raman Arora
  3. Decoupling Exploration and Exploitation in Multi-Armed Bandits Orly Avner, Shie Mannor, Ohad Shamir
  4. Learning the Experts for Online Sequence Prediction Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson
  5. Plug-in martingales for testing exchangeability on-line Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Volodya Vovk
  6. Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization Thomas Desautels, Andreas Krause, Joel Burdick
  7. On-Line Portfolio Selection with Moving Average Reversion Bin Li, Steven C.H. Hoi
  8. Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations Nando de Freitas, Alex Smola, Masrour Zoghi

Session 5C — Neural networks and deep learning 2

chair Yoshua Bengio, room AT LT 1

  1. Large-Scale Feature Learning With Spike-and-Slab Sparse Coding Ian Goodfellow, Aaron Courville, Yoshua Bengio
  2. Learning Invariant Representations with Local Transformations Kihyuk Sohn, Honglak Lee
  3. Building high-level features using large scale unsupervised learning Quoc Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeff Dean, Andrew Ng
  4. On multi-view feature learning Roland Memisevic
  5. Learning to Label Aerial Images from Noisy Data Volodymyr Mnih, Geoffrey Hinton

Session 5D — Sparsity and compressed sensing

chair Mahdi Milani Fard, room AT LT 2

  1. Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering Gael Varoquaux, Alexandre Gramfort, Bertrand Thirion
  2. Estimation of Simultaneously Sparse and Low Rank Matrices Pierre-André Savalle, Emile Richard, Nicolas Vayatis
  3. Multi-level Lasso for Sparse Multi-task Regression Aurelie Lozano, Grzegorz Swirszcz
  4. Efficient Euclidean Projections onto the Intersection of Norm Balls Adams Wei Yu, Hao Su, Li Fei-Fei
  5. Learning Efficient Structured Sparse Models Alex Bronstein, Pablo Sprechmann, Guillermo Sapiro

Session 5E — Latent-Variable Models and Topic Models

chair Jordan Boyd-Graber, room AT LT 3

  1. Max-Margin Nonparametric Latent Feature Models for Link Prediction Jun Zhu
  2. Canonical Trends: Detecting Trend Setters in Web Data Felix Biessmann, Jens-Michalis Papaioannou, Mikio Braun, Andreas Harth
  3. Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separatio Gautham Mysore, Maneesh Sahani
  4. Sparse stochastic inference for latent Dirichlet allocation David Mimno, Matt Hoffman, David Blei
  5. Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data Dongwoo Kim, Suin Kim, Alice Oh
  6. Rethinking Collapsed Variational Bayes Inference for LDA Issei Sato, Hiroshi Nakagawa
  7. Capturing topical content with frequency and exclusivity Jonathan Bischof, Edoardo Airoldi

Session 6A — Semi-supervised learning

chair Maria Florina Balcan, room AT LT 4

  1. A convex relaxation for weakly supervised classifiers Armand Joulin, Francis Bach
  2. Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching Marthinus Du Plessis, Masashi Sugiyama
  3. A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han
  4. Information-theoretic Semi-supervised Metric Learning via Entropy Regularization Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
  5. Cross Language Text Classification via Subspace Co-regularized Multi-view Learning Yuhong Guo, Min Xiao
  6. Using CCA to improve CCA: A new spectral method for estimating vector models of words Paramveer Dhillon, Jordan Rodu, Dean Foster, Lyle Ungar
  7. Semi-Supervised Collective Classification via Hybrid Label Regularization Luke McDowell, David Aha

Session 6B — Reinforcement learning 3

chair Ron Parr, room AT LT 5

  1. Compositional Planning Using Optimal Option Models David Silver, Kamil Ciosek
  2. Learning Parameterized Skills Bruno Da Silva, George Konidaris, Andrew Barto
  3. Safe Exploration in Markov Decision Processes Teodor Mihai Moldovan, Pieter Abbeel
  4. Modelling transition dynamics in MDPs with RKHS embeddings Steffen Grunewalder, Guy Lever, Luca Baldassarre, Massi Pontil, Arthur Gretton

Session 6C — Applications

chair Tom Dietterich, room AT LT 1

  1. A Joint Model of Language and Perception for Grounded Attribute Learning Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox
  2. Predicting Manhole Events in New York City Cynthia Rudin, Rebecca Passonneau, Axinia Radeva, Steve Ierome, Delfina Isaac
  3. Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent
  4. Learning Object Arrangements in 3D Scenes using Human Context Yun Jiang, Marcus Lim, Ashutosh Saxena

Session 6D — Time-Series Analysis

chair Naoki Abe, room AT LT 2

  1. Learning the Dependence Graph of Time Series with Latent Factors Ali Jalali, Sujay Sanghavi
  2. Improved Estimation in Time Varying Models Doina Precup, Philip Bachman
  3. Bayesian Conditional Cointegration Chris Bracegirdle, David Barber
  4. Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling Yan Liu, Taha Bahadori, Hongfei Li

Session 6E — Graph-based learning

chair Charles Elkan, room AT LT 3

  1. Shortest path distance in random k-nearest neighbor graphs Morteza Alamgir, Ulrike von Luxburg
  2. Submodular Inference of Diffusion Networks from Multiple Trees Manuel Gomez Rodriguez, Bernhard Schölkopf
  3. Influence Maximization in Continuous Time Diffusion Networks Manuel Gomez Rodriguez, Bernhard Schölkopf
  4. Latent Multi-group Membership Graph Model Myunghwan Kim, Jure Leskovec
  5. The Most Persistent Soft-Clique in a Set of Sampled Graphs Novi Quadrianto, Chao Chen, Christoph Lampert
  6. Two Manifold Problems with Applications to Nonlinear System Identification Byron Boots, Geoff Gordon
  7. Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs Giorgos Borboudakis, Ioannis Tsamardinos

Session 7A — Invited Applications

chair Samy Bengio, room AT LT 4

  1. Conversational Speech Transcription Using Context-Dependent Deep Neural Networks Dong Yu, Frank Seide, Gang Li
  2. Data-driven Web Design Ranjitha Kumar, Jerry Talton, Salman Ahmad, Scott Klemmer
  3. Learning the Central Events and Participants in Unlabeled Text Nathanael Chambers, Dan Jurafsky
  4. Exemplar-SVMs for Visual Object Detection, Label Transfer and Image Retrieval Tomasz Malisiewicz, Abhinav Shrivastava, Abhinav Gupta, Alexei Efros
  5. Learning Force Control Policies for Compliant Robotic Manipulation Mrinal Kalakrishnan, Ludovic Righetti, Peter Pastor, Stefan Schaal

Session 7B — Reinforcement learning 4

chair Michael Bowling, room AT LT 5

  1. Agnostic System Identification for Model-Based Reinforcement Learning Stephane Ross, Drew Bagnell
  2. Greedy Algorithms for Sparse Reinforcement Learning Christopher Painter-Wakefield, Ronald Parr
  3. On the Sample Complexity of Reinforcement Learning with a Generative Model Mohammad Gheshlaghi Azar, Remi Munos, Bert Kappen
  4. Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting Ning Xie, Hirotaka Hachiya, Masashi Sugiyama
  5. Path Integral Policy Improvement with Covariance Matrix Adaptation Freek Stulp, Olivier Sigaud

Session 7C — Clustering 2

chair Raquel Urtasun, room AT LT 1

  1. On causal and anticausal learning Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij
  2. Revisiting k-means: New Algorithms via Bayesian Nonparametrics Brian Kulis, Michael Jordan
  3. Approximate Principal Direction Trees Mark McCartin-Lim, Andrew McGregor, Rui Wang
  4. Clustering using Max-norm Constrained Optimization Ali Jalali, Nathan Srebro
  5. Efficient Active Algorithms for Hierarchical Clustering Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
  6. Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients Iftekhar Naim, Daniel Gildea
  7. Groupwise Constrained Reconstruction for Subspace Clustering Ruijiang Li, Bin Li, Cheng Jin, Xiangyang Xue
  8. Clustering by Low-Rank Doubly Stochastic Matrix Decomposition Zhirong Yang, Erkki Oja

Session 7D — Supervised learning 2

chair Leon Bottou, room AT LT 2

  1. Total Variation and Euler's Elastica for Supervised Learning Tong Lin, Hanlin Xue, Ling Wang, Hongbin Zha
  2. Flexible Modeling of Latent Task Structures in Multitask Learning Alexandre Passos, Piyush Rai, Jacques Wainer, Hal Daume III
  3. Fast classification using sparse decision DAGs Robert Busa-Fekete, Djalel Benbouzid, Balazs Kegl
  4. An Efficient Approach to Sparse Linear Discriminant Analysis Luis Francisco Sánchez Merchante, Yves Grandvalet, Gérrad Govaert
  5. Sequential Nonparametric Regression Haijie Gu, John Lafferty
  6. The Landmark Selection Method for Multiple Output Prediction Krishnakumar Balasubramanian, Guy Lebanon
  7. Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design Lauren Hannah, David Dunson
  8. AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem Peng Sun, Mark Reid, Jie Zhou

Session 7E — Probabilistic Models

chair Erik Sudderth, room AT LT 3

  1. Local Loss Optimization in Operator Models: A New Insight into Spectral Learning Borja Balle, Ariadna Quattoni, Xavier Carreras
  2. Discriminative Probabilistic Prototype Learning Edwin Bonilla, Antonio Robles-Kelly
  3. Isoelastic Agents and Wealth Updates in Machine Learning Markets Amos Storkey, Jono Millin, Krzysztof Geras
  4. Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning Shakir Mohamed, Katherine Heller, Zoubin Ghahramani
  5. Nonparametric variational inference Samuel Gershman, Matt Hoffman, David Blei
  6. Levy Measure Decompositions for the Beta and Gamma Processes Yingjian Wang, Lawrence Carin
  7. Copula Mixture Model for Dependency-seeking Clustering Melanie Rey, Volker Roth
  8. Predicting accurate probabilities with a ranking loss Aditya Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado

Session 8A — Kernel methods 2

chair Mario Marchand, room AT LT 4

  1. Copula-based Kernel Dependency Measures Barnabas Poczos, Zoubin Ghahramani, Jeff Schneider
  2. The Kernelized Stochastic Batch Perceptron Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
  3. Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin, Pengcheng Wu
  4. Distributed Tree Kernels Fabio Massimo Zanzotto, Lorenzo Dell'Arciprete
  5. Analysis of Kernel Mean Matching under Covariate Shift Yaoliang Yu, Csaba Szepesvari

Session 8B — Active and cost-sensitive learning

chair Andreas Krause, room AT LT 5

  1. The Greedy Miser: Learning under Test-time Budgets Zhixiang Xu, Kilian Weinberger, Olivier Chapelle
  2. Joint Optimization and Variable Selection of High-dimensional Gaussian Processes Bo Chen, Rui Castro, Andreas Krause
  3. Comparison-Based Learning with Rank Nets Amin Karbasi, Stratis Ioannidis, laurent Massoulie
  4. Bayesian Optimal Active Search and Surveying Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff Schneider, Richard Mann
  5. Hybrid Batch Bayesian Optimization Javad Azimi, Ali Jalali, Xiaoli Zhang-Fern
  6. Batch Active Learning via Coordinated Matching Javad Azimi, Alan Fern, Xiaoli Zhang-Fern, Glencora Borradaile, Brent Heeringa
  7. Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes Murat Dundar, Ferit Akova, Alan Qi, Bartek Rajwa

Session 8C — Feature selection and dimensionality reduction

chair Andrea Danyluk, room AT LT 1

  1. Robust PCA in High-dimension: A Deterministic Approach Jiashi Feng, Huan Xu, Shuicheng Yan
  2. Communications Inspired Linear Discriminant Analysis Minhua Chen, William Carson, Miguel Rodrigues, Lawrence Carin, Robert Calderbank
  3. Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans
  4. Fast Training of Nonlinear Embedding Algorithms Max Vladymyrov, Miguel Carreira-Perpinan
  5. Sparse Support Vector Infinite Push Alain Rakotomamonjy
  6. Adaptive Canonical Correlation Analysis Based On Matrix Manifolds Florian Yger, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy
  7. Fast approximation of matrix coherence and statistical leverage Michael Mahoney, Petros Drineas, Malik Magdon-Ismail, David Woodruff
  8. Feature Selection via Probabilistic Outputs Andrea Danyluk, Nicholas Arnosti

Session 8D — Recommendation and Matrix Factorization

chair Thorsten Joachims, room AT LT 2

  1. A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion Franz Király, Ryota Tomioka
  2. Gap Filling in the Plant Kingdom—Trait Prediction Using Hierarchical Probabilistic Matrix Factorization Hanhuai Shan, Jens Kattge, Peter Reich, Arindam Banerjee, Franziska Schrodt, Markus Reichstein
  3. Stability of matrix factorization for collaborative filtering Yu-Xiang Wang, Huan Xu
  4. Latent Collaborative Retrieval Jason Weston, Chong Wang, Ron Weiss, Adam Berenzweig
  5. A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices Mingjun Zhong, Mark Girolami
  6. Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems Sanjay Purushotham, Yan Liu
  7. Active Learning for Matching Problems Laurent Charlin, Rich Zemel, Craig Boutilier
  8. A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training Aaron Defazio, Tiberio Caetano

Session 8E — Graphical models

chair Ricardo Silva, room AT LT 3

  1. Variational Bayesian Inference with Stochastic Search John Paisley, David Blei, Michael Jordan
  2. Large Scale Variational Bayesian Inference for Structured Scale Mixture Models Young Jun Ko, Matthias Seeger
  3. A Generalized Loop Correction Method for Approximate Inference in Graphical Models Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner
  4. Distributed Parameter Estimation via Pseudo-likelihood Qiang Liu, Alexander Ihler
  5. High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains Majid Janzamin, Animashree Anandkumar

Word storms by Quim Castella Charles Sutton.