Volume 51: Artificial Intelligence and Statistics, 9-11 May 2016, Cadiz, Spain

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Editors: Arthur Gretton, Christian C. Robert

[bib][citeproc]

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

Mario Lucic, Olivier Bachem, Andreas Krause ; PMLR 51:1-9

Revealing Graph Bandits for Maximizing Local Influence

Alexandra Carpentier, Michal Valko ; PMLR 51:10-18

Convex Block-sparse Linear Regression with Expanders – Provably

Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre, Volkan Cevher ; PMLR 51:19-27

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman ; PMLR 51:28-37

Clamping Improves TRW and Mean Field Approximations

Adrian Weller, Justin Domke ; PMLR 51:38-46

Tightness of LP Relaxations for Almost Balanced Models

Adrian Weller, Mark Rowland, David Sontag ; PMLR 51:47-55

Control Functionals for Quasi-Monte Carlo Integration

Chris Oates, Mark Girolami ; PMLR 51:56-65

Probability Inequalities for Kernel Embeddings in Sampling without Replacement

Markus Schneider ; PMLR 51:66-74

Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking

Nicolas Goix, Anne Sabourin, Stéphan Clémençon ; PMLR 51:75-83

A Robust-Equitable Copula Dependence Measure for Feature Selection

Yale Chang, Yi Li, Adam Ding, Jennifer Dy ; PMLR 51:84-92

Random Forest for the Contextual Bandit Problem

Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot ; PMLR 51:93-101

Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics

Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard ; PMLR 51:102-110

Learning Sparse Additive Models with Interactions in High Dimensions

Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause ; PMLR 51:111-120

Bipartite Correlation Clustering: Maximizing Agreements

Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros Dimakis ; PMLR 51:121-129

Breaking Sticks and Ambiguities with Adaptive Skip-gram

Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov ; PMLR 51:130-138

Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls

Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu ; PMLR 51:139-148

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Jonathan Scarlett, Volkan Cevher ; PMLR 51:149-158

Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models

Lee H. Dicker, Murat A. Erdogdu ; PMLR 51:159-167

Scalable Gaussian Process Classification via Expectation Propagation

Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato ; PMLR 51:168-176

Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates

Lingxiao Wang, Xiang Ren, Quanquan Gu ; PMLR 51:177-185

On the Reducibility of Submodular Functions

Jincheng Mei, Hao Zhang, Bao-Liang Lu ; PMLR 51:186-194

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

Atsushi Nitanda ; PMLR 51:195-203

Fast Convergence of Online Pairwise Learning Algorithms

Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou ; PMLR 51:204-212

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön ; PMLR 51:213-221

Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables

Yaniv Tenzer, Gal Elidan ; PMLR 51:222-230

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes

Alexander G. de G. Matthews, James Hensman, Richard Turner, Zoubin Ghahramani ; PMLR 51:231-239

Non-stochastic Best Arm Identification and Hyperparameter Optimization

Kevin Jamieson, Ameet Talwalkar ; PMLR 51:240-248

A Linearly-Convergent Stochastic L-BFGS Algorithm

Philipp Moritz, Robert Nishihara, Michael Jordan ; PMLR 51:249-258

No Regret Bound for Extreme Bandits

Robert Nishihara, David Lopez-Paz, Leon Bottou ; PMLR 51:259-267

Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations

Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan ; PMLR 51:268-276

Online Learning to Rank with Feedback at the Top

Sougata Chaudhuri, Ambuj Tewari Tewari ; PMLR 51:277-285

Survey Propagation beyond Constraint Satisfaction Problems

Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey ; PMLR 51:286-295

Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models

Balázs Csanád Csáji ; PMLR 51:296-304

CRAFT: ClusteR-specific Assorted Feature selecTion

Vikas K. Garg, Cynthia Rudin, Tommi Jaakkola ; PMLR 51:305-313

Time-Varying Gaussian Process Bandit Optimization

Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher ; PMLR 51:314-323

Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies

Weici Hu, Peter Frazier ; PMLR 51:324-332

Bayesian Markov Blanket Estimation

Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth ; PMLR 51:333-341

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John Fisher, Lars Hansen ; PMLR 51:342-350

Unsupervised Ensemble Learning with Dependent Classifiers

Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger ; PMLR 51:351-360

Multi-Level Cause-Effect Systems

Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona ; PMLR 51:361-369

Deep Kernel Learning

Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing ; PMLR 51:370-378

Nearly Optimal Classification for Semimetrics

Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch ; PMLR 51:379-388

Latent Point Process Allocation

Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts, Tom Nickson ; PMLR 51:389-397

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings

Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic ; PMLR 51:398-407

Bayesian Generalised Ensemble Markov Chain Monte Carlo

Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg ; PMLR 51:408-416

A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning

Yan Li, Han Liu, Warren Powell ; PMLR 51:417-425

Optimal Statistical and Computational Rates for One Bit Matrix Completion

Renkun Ni, Quanquan Gu ; PMLR 51:426-434

PAC-Bayesian Bounds based on the Rényi Divergence

Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy ; PMLR 51:435-444

Simple and Scalable Constrained Clustering: a Generalized Spectral Method

Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary Miller, Richard Peng ; PMLR 51:445-454

Geometry Aware Mappings for High Dimensional Sparse Factors

Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Bhaskar, Suju Rajan ; PMLR 51:455-463

Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu ; PMLR 51:464-472

Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA

Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu ; PMLR 51:473-481

Quantization based Fast Inner Product Search

Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha ; PMLR 51:482-490

An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong ; PMLR 51:491-499

Learning Structured Low-Rank Representation via Matrix Factorization

Jie Shen, Ping Li ; PMLR 51:500-509

A PAC RL Algorithm for Episodic POMDPs

Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill ; PMLR 51:510-518

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

Sujith Ravi, Qiming Diao ; PMLR 51:519-528

Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models

Calvin McCarter, Seyoung Kim ; PMLR 51:528-537

Graph Connectivity in Noisy Sparse Subspace Clustering

Yining Wang, Yu-Xiang Wang, Aarti Singh ; PMLR 51:538-546

The Nonparametric Kernel Bayes Smoother

Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song ; PMLR 51:547-555

Universal Models of Multivariate Temporal Point Processes

Asela Gunawardana, Chris Meek ; PMLR 51:556-563

Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings

Zhitang Chen, Pascal Poupart, Yanhui Geng ; PMLR 51:573-581

Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines

Muneki Yasuda ; PMLR 51:582-590

Enumerating Equivalence Classes of Bayesian Networks using EC Graphs

Eunice Yuh-Jie Chen, Arthur Choi Choi, Adnan Darwiche ; PMLR 51:591-599

Low-Rank and Sparse Structure Pursuit via Alternating Minimization

Quanquan Gu, Zhaoran Wang Wang, Han Liu ; PMLR 51:600-609

NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning

Eli Meirom, Pavel Kisilev ; PMLR 51:610-619

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization

Fanhua Shang, Yuanyuan Liu, James Cheng ; PMLR 51:620-629

Fast Dictionary Learning with a Smoothed Wasserstein Loss

Antoine Rolet, Marco Cuturi, Gabriel Peyré ; PMLR 51:630-638

New Resistance Distances with Global Information on Large Graphs

Canh Hao Nguyen, Hiroshi Mamitsuka ; PMLR 51:639-647

Batch Bayesian Optimization via Local Penalization

Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence ; PMLR 51:648-657

Nonparametric Budgeted Stochastic Gradient Descent

Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Phung ; PMLR 51:654-572

Learning Relationships between Data Obtained Independently

Alexandra Carpentier, Teresa Schlueter ; PMLR 51:658-666

Fast and Scalable Structural SVM with Slack Rescaling

Heejin Choi, Ofer Meshi, Nathan Srebro ; PMLR 51:667-675

Probabilistic Approximate Least-Squares

Simon Bartels, Philipp Hennig ; PMLR 51:676-684

Approximate Inference Using DC Programming For Collective Graphical Models

Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon ; PMLR 51:685-693

Sequential Inference for Deep Gaussian Process

Yali Wang, Marcus Brubaker, Brahim Chaib-Draa, Raquel Urtasun ; PMLR 51:694-703

Variational Tempering

Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei ; PMLR 51:704-712

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System

Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric Xing ; PMLR 51:713-722

Scalable MCMC for Mixed Membership Stochastic Blockmodels

Wenzhe Li, Sungjin Ahn, Max Welling ; PMLR 51:723-731

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki ; PMLR 51:732-740

A Deep Generative Deconvolutional Image Model

Yunchen Pu, Win Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin ; PMLR 51:741-750

Distributed Multi-Task Learning

Jialei Wang, Mladen Kolar, Nathan Srerbo ; PMLR 51:751-760

A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models

Rishit Sheth, Roni Khardon ; PMLR 51:761-769

Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation

Sebastian Tschiatschek, Josip Djolonga, Andreas Krause ; PMLR 51:770-779

Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm

Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan ; PMLR 51:780-789

GLASSES: Relieving The Myopia Of Bayesian Optimisation

Javier Gonzalez, Michael Osborne, Neil Lawrence ; PMLR 51:790-799

Stochastic Variational Inference for the HDP-HMM

Aonan Zhang, San Gultekin, John Paisley ; PMLR 51:800-808

Stochastic Neural Networks with Monotonic Activation Functions

Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner ; PMLR 51:809-818

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles

Xiaowei Hu, Prashanth L.A., András György, Csaba Szepesvari ; PMLR 51:819-828

Variational Gaussian Copula Inference

Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin ; PMLR 51:829-838

Low-Rank Approximation of Weighted Tree Automata

Guillaume Rabusseau, Borja Balle, Shay Cohen ; PMLR 51:839-847

Accelerating Online Convex Optimization via Adaptive Prediction

Mehryar Mohri, Scott Yang ; PMLR 51:848-856

Scalable geometric density estimation

Ye Wang, Antonio Canale, David Dunson ; PMLR 51:857-865

Model-based Co-clustering for High Dimensional Sparse Data

Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif ; PMLR 51:866-874

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

Christina Heinze, Brian McWilliams, Nicolai Meinshausen ; PMLR 51:875-883

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

Chun-Liang Li, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider ; PMLR 51:884-892

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games

Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin ; PMLR 51:893-901

Semi-Supervised Learning with Adaptive Spectral Transform

Hanxiao Liu, Yiming Yang ; PMLR 51:902-910

Pseudo-Marginal Slice Sampling

Iain Murray, Matthew Graham ; PMLR 51:911-919

How to Learn a Graph from Smooth Signals

Vassilis Kalofolias ; PMLR 51:920-929

Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects

Mario Figueiredo, Robert Nowak ; PMLR 51:930-938

Pareto Front Identification from Stochastic Bandit Feedback

Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan ; PMLR 51:939-947

Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces

Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian ; PMLR 51:948-956

AdaDelay: Delay Adaptive Distributed Stochastic Optimization

Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola ; PMLR 51:957-965

Exponential Stochastic Cellular Automata for Massively Parallel Inference

Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele ; PMLR 51:966-975

Globally Sparse Probabilistic PCA

Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche ; PMLR 51:976-984

Provable Bayesian Inference via Particle Mirror Descent

Bo Dai, Niao He, Hanjun Dai, Le Song ; PMLR 51:985-994

Unsupervised Feature Selection by Preserving Stochastic Neighbors

Xiaokai Wei, Philip S. Yu ; PMLR 51:995-1003

Improved Learning Complexity in Combinatorial Pure Exploration Bandits

Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett ; PMLR 51:1004-1012

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing ; PMLR 51:1013-1021

Optimization as Estimation with Gaussian Processes in Bandit Settings

Zi Wang, Bolei Zhou, Stefanie Jegelka ; PMLR 51:1022-1031

A Convex Surrogate Operator for General Non-Modular Loss Functions

Jiaqian Yu, Matthew Blaschko ; PMLR 51:1032-1041

Inference for High-dimensional Exponential Family Graphical Models

Jialei Wang, Mladen Kolar ; PMLR 51:1042-1050

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin ; PMLR 51:1051-1060

Fitting Spectral Decay with the k-Support Norm

Andrew McDonald, Massimiliano Pontil, Dimitris Stamos ; PMLR 51:1061-1069

Early Stopping as Nonparametric Variational Inference

David Duvenaud, Dougal Maclaurin, Ryan Adams ; PMLR 51:1070-1077

Bayesian Nonparametric Kernel-Learning

Junier B. Oliva, Avinava Dubey, Andrew G. Wilson, Barnabas Poczos, Jeff Schneider, Eric P. Xing ; PMLR 51:1078-1086

Tight Variational Bounds via Random Projections and I-Projections

Lun-Kai Hsu, Tudor Achim, Stefano Ermon ; PMLR 51:1087-1095

Bethe Learning of Graphical Models via MAP Decoding

Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara ; PMLR 51:1096-1104

Determinantal Regularization for Ensemble Variable Selection

Veronika Rockova, Gemma Moran, Edward George ; PMLR 51:1105-1113

Scalable and Sound Low-Rank Tensor Learning

Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans ; PMLR 51:1114-1123

Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information

Changwei Hu, Piyush Rai, Lawrence Carin ; PMLR 51:1124-1132

Topic-Based Embeddings for Learning from Large Knowledge Graphs

Changwei Hu, Piyush Rai, Lawrence Carin ; PMLR 51:1133-1141

Consistently Estimating Markov Chains with Noisy Aggregate Data

Garrett Bernstein, Daniel Sheldon ; PMLR 51:1142-1150

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction

Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre ; PMLR 51:1151-1158

Improper Deep Kernels

Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson ; PMLR 51:1159-1167

Unbounded Bayesian Optimization via Regularization

Bobak Shahriari, Alexandre Bouchard-Cote, Nando Freitas ; PMLR 51:1168-1176

Non-Gaussian Component Analysis with Log-Density Gradient Estimation

Hiroaki Sasaki, Gang Niu, Masashi Sugiyama ; PMLR 51:1177-1185

Online Learning with Noisy Side Observations

Tomáš Kocák, Gergely Neu, Michal Valko ; PMLR 51:1186-1194

Black-Box Policy Search with Probabilistic Programs

Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood ; PMLR 51:1195-1204

Efficient Bregman Projections onto the Permutahedron and Related Polytopes

Cong Han Lim, Stephen J. Wright ; PMLR 51:1205-1213

On Searching for Generalized Instrumental Variables

Benito Zander, Maciej Liśkiewicz ; PMLR 51:1214-1222

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models

Hanie Sedghi, Majid Janzamin, Anima Anandkumar ; PMLR 51:1223-1231

Controlling Bias in Adaptive Data Analysis Using Information Theory

Daniel Russo, James Zou ; PMLR 51:1232-1240

A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees

Jean-Francis Roy, Mario Marchand, François Laviolette ; PMLR 51:1241-1249

Graph Sparsification Approaches for Laplacian Smoothing

Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani ; PMLR 51:1250-1259

Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation

Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar ; PMLR 51:1260-1269

Robust Covariate Shift Regression

Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart ; PMLR 51:1270-1279

On Lloyd’s Algorithm: New Theoretical Insights for Clustering in Practice

Cheng Tang, Claire Monteleoni ; PMLR 51:1280-1289

Towards Stability and Optimality in Stochastic Gradient Descent

Panos Toulis, Dustin Tran, Edo Airoldi ; PMLR 51:1290-1298

Communication Efficient Distributed Agnostic Boosting

Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau ; PMLR 51:1299-1307

Private Causal Inference

Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger ; PMLR 51:1308-1317

Parallel Markov Chain Monte Carlo via Spectral Clustering

Guillaume Basse, Aaron Smith, Natesh Pillai ; PMLR 51:1318-1327

Efficient Sampling for k-Determinantal Point Processes

Chengtao Li, Stefanie Jegelka, Suvrit Sra ; PMLR 51:1328-1337

A Fast and Reliable Policy Improvement Algorithm

Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright ; PMLR 51:1338-1346

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization

Zhao Song, Ricardo Henao, David Carlson, Lawrence Carin ; PMLR 51:1347-1355

Active Learning Algorithms for Graphical Model Selection

Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong H. Park ; PMLR 51:1356-1364

Streaming Kernel Principal Component Analysis

Mina Ghashami, Daniel J. Perry, Jeff Phillips ; PMLR 51:1365-1374

Back to the Future: Radial Basis Function Networks Revisited

Qichao Que, Mikhail Belkin ; PMLR 51:1375-1383

Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions

Loic Landrieu, Guillaume Obozinski ; PMLR 51:1384-1393

Loss Bounds and Time Complexity for Speed Priors

Daniel Filan, Jan Leike, Marcus Hutter ; PMLR 51:1394-1402

NYTRO: When Subsampling Meets Early Stopping

Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco ; PMLR 51:1403-1411

Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments

Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert ; PMLR 51:1412-1420

Spectral M-estimation with Applications to Hidden Markov Models

Dustin Tran, Minjae Kim, Finale Doshi-Velez ; PMLR 51:1421-1430

Chained Gaussian Processes

Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence ; PMLR 51:1431-1440

Multiresolution Matrix Compression

Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor ; PMLR 51:1441-1449

Supervised Neighborhoods for Distributed Nonparametric Regression

Adam Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu ; PMLR 51:1450-1459

Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

Dejiao Zhang, Laura Balzano ; PMLR 51:1460-1468

Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks

Abdullah Rashwan, Han Zhao, Pascal Poupart ; PMLR 51:1469-1477

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh ; PMLR 51:1478-1487

Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees

Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani ; PMLR 51:1488-1496

Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization

Yan Kaganovsky, Ikenna Odinaka, David Carlson, Lawrence Carin ; PMLR 51:1497-1505

Discriminative Structure Learning of Arithmetic Circuits

Amirmohammad Rooshenas, Daniel Lowd ; PMLR 51:1506-1514

One Scan 1-Bit Compressed Sensing

Ping Li ; PMLR 51:1515-1523

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