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JMLR Workshop and Conference Proceedings

Volume 32: Proceedings of The 31st International Conference on Machine Learning

Editors: Eric P. Xing, Tony Jebara

Contents:

Cycle 1 Papers

A Discriminative Latent Variable Model for Online Clustering

Rajhans Samdani, Kai-Wei Chang, Dan Roth

Kernel Mean Estimation and Stein Effect

Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schoelkopf

Demystifying Information-Theoretic Clustering

Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo

Covering Number for Efficient Heuristic-based POMDP Planning

Zongzhang Zhang, David Hsu, Wee Sun Lee

The Coherent Loss Function for Classification

Wenzhuo Yang, Melvyn Sim, Huan Xu

Fast Stochastic Alternating Direction Method of Multipliers

Wenliang Zhong, James Kwok

Active Detection via Adaptive Submodularity

Yuxin Chen, Hiroaki Shioi, Cesar Fuentes Montesinos, Lian Pin Koh, Serge Wich, Andreas Krause

Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization

Shai Shalev-Shwartz, Tong Zhang

An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization

Qihang Lin, Lin Xiao

Recurrent Convolutional Neural Networks for Scene Labeling

Pedro Pinheiro, Ronan Collobert

A Statistical Perspective on Algorithmic Leveraging

Ping Ma, Michael Mahoney, Bin Yu

Thompson Sampling for Complex Online Problems

Aditya Gopalan, Shie Mannor, Yishay Mansour

Boosting multi-step autoregressive forecasts

Souhaib Ben Taieb, Rob Hyndman

A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data

Arun Rajkumar, Shivani Agarwal

Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations

Timothy Mann, Shie Mannor

Latent Bandits.

Odalric-Ambrym Maillard, Shie Mannor

Fast Allocation of Gaussian Process Experts

Trung Nguyen, Edwin Bonilla

Von Mises-Fisher Clustering Models

Siddharth Gopal, Yiming Yang

Convergence rates for persistence diagram estimation in Topological Data Analysis

Frédéric Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel

Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs

Fabian Gieseke, Justin Heinermann, Cosmin Oancea, Christian Igel

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

Anoop Korattikara, Yutian Chen, Max Welling

Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis

Jian Tang, Zhaoshi Meng, Xuanlong Nguyen, Qiaozhu Mei, Ming Zhang

The Inverse Regression Topic Model

Maxim Rabinovich, David Blei

A Consistent Histogram Estimator for Exchangeable Graph Models

Stanley Chan, Edoardo Airoldi

Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data

Benjamin Letham, Wei Sun, Anshul Sheopuri

Towards Minimax Online Learning with Unknown Time Horizon

Haipeng Luo, Robert Schapire

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

Andrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry

Margins, Kernels and Non-linear Smoothed Perceptrons

Aaditya Ramdas, Javier Peña

Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models

Shike Mei, Jun Zhu, Jerry Zhu

Learning Theory and Algorithms for revenue optimization in second price auctions with reserve

Mehryar Mohri, Andres Munoz Medina

Low-density Parity Constraints for Hashing-Based Discrete Integration

Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

Prediction with Limited Advice and Multiarmed Bandits with Paid Observations

Yevgeny Seldin, Peter Bartlett, Koby Crammer, Yasin Abbasi-Yadkori

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

Tien Vu Nguyen, Dinh Phung, Xuanlong Nguyen, Swetha Venkatesh, Hung Bui

Large-Margin Metric Learning for Constrained Partitioning Problems

Rémi Lajugie, Francis Bach, Sylvain Arlot

Wasserstein Propagation for Semi-Supervised Learning

Justin Solomon, Raif Rustamov, Leonidas Guibas, Adrian Butscher

Max-Margin Infinite Hidden Markov Models

Aonan Zhang, Jun Zhu, Bo Zhang

Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function

Yong Liu, Shali Jiang, Shizhong Liao

Generalized Exponential Concentration Inequality for Renyi Divergence Estimation

Shashank Singh, Barnabas Poczos

Boosting with Online Binary Learners for the Multiclass Bandit Problem

Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu

Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm

Tasuku Soma, Naonori Kakimura, Kazuhiro Inaba, Ken-ichi Kawarabayashi

Computing Parametric Ranking Models via Rank-Breaking

Hossein Azari Soufiani, David Parkes, Lirong Xia

Tracking Adversarial Targets

Yasin Abbasi-Yadkori, Peter Bartlett, Varun Kanade

Online Bayesian Passive-Aggressive Learning

Tianlin Shi, Jun Zhu

Deterministic Policy Gradient Algorithms

David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller

Modeling Correlated Arrival Events with Latent Semi-Markov Processes

Wenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin

Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach

Rémi Bardenet, Arnaud Doucet, Chris Holmes

Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost

Ferdinando Cicalese, Eduardo Laber, Aline Medeiros Saettler

Condensed Filter Tree for Cost-Sensitive Multi-Label Classification

Chun-Liang Li, Hsuan-Tien Lin

On Measure Concentration of Random Maximum A-Posteriori Perturbations

Francesco Orabona, Tamir Hazan, Anand Sarwate, Tommi Jaakkola

Bias in Natural Actor-Critic Algorithms

Philip Thomas

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning

François Denis, Mattias Gybels, Amaury Habrard

On Modelling Non-linear Topical Dependencies

Zhixing Li, Siqiang Wen, Juanzi Li, Peng Zhang, Jie Tang

A Deep and Tractable Density Estimator

Benigno Uria, Iain Murray, Hugo Larochelle

(Near) Dimension Independent Risk Bounds for Differentially Private Learning

Prateek Jain, Abhradeep Guha Thakurta

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael Mahoney

Discriminative Features via Generalized Eigenvectors

Nikos Karampatziakis, Paul Mineiro

Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint

Ji Liu, Jieping Ye, Ryohei Fujimaki

Online Learning in Markov Decision Processes with Changing Cost Sequences

Travis Dick, Andras Gyorgy, Csaba Szepesvari

Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms

Richard Combes, Alexandre Proutiere

Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection

Arun Iyer, Saketha Nath, Sunita Sarawagi

Asymptotically consistent estimation of the number of change points in highly dependent time series

Azadeh Khaleghi, Daniil Ryabko

Coordinate-descent for learning orthogonal matrices through Givens rotations

Uri Shalit, Gal Chechik

Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search

Anshumali Shrivastava, Ping Li

A Divide-and-Conquer Solver for Kernel Support Vector Machines

Cho-Jui Hsieh, Si Si, Inderjit Dhillon

Nuclear Norm Minimization via Active Subspace Selection

Cho-Jui Hsieh, Peder Olsen

Provable Bounds for Learning Some Deep Representations

Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma

Large-scale Multi-label Learning with Missing Labels

Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit Dhillon

Learning Graphs with a Few Hubs

Rashish Tandon, Pradeep Ravikumar

Agnostic Bayesian Learning of Ensembles

Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle

Towards an optimal stochastic alternating direction method of multipliers

Samaneh Azadi, Suvrit Sra

Spherical Hamiltonian Monte Carlo for Constrained Target Distributions

Shiwei Lan, Bo Zhou, Babak Shahbaba

Efficient Continuous-Time Markov Chain Estimation

Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell

Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers

Dani Yogatama, Noah Smith

Narrowing the Gap: Random Forests In Theory and In Practice

Misha Denil, David Matheson, Nando De Freitas

Coherent Matrix Completion

Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward

Admixture of Poisson MRFs: A Topic Model with Word Dependencies

David Inouye, Pradeep Ravikumar, Inderjit Dhillon

True Online TD(lambda)

Harm van Seijen, Rich Sutton

Memory Efficient Kernel Approximation

Si Si, Cho-Jui Hsieh, Inderjit Dhillon

Learning Sum-Product Networks with Direct and Indirect Variable Interactions

Amirmohammad Rooshenas, Daniel Lowd

Hamiltonian Monte Carlo Without Detailed Balance

Jascha Sohl-Dickstein, Mayur Mudigonda, Michael DeWeese

Filtering with Abstract Particles

Jacob Steinhardt, Percy Liang

Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers

Taiji Suzuki

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction

Jian Zhou, Olga Troyanskaya

An Efficient Approach for Assessing Hyperparameter Importance

Frank Hutter, Holger Hoos, Kevin Leyton-Brown

Cycle 2 Papers

An Information Geometry of Statistical Manifold Learning

Ke Sun, Stéphane Marchand-Maillet

Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem

Masrour Zoghi, Shimon Whiteson, Remi Munos, Maarten de Rijke

Compact Random Feature Maps

Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste

Concentration in unbounded metric spaces and algorithmic stability

Aryeh Kontorovich

Heavy-tailed regression with a generalized median-of-means

Daniel Hsu, Sivan Sabato

Spectral Bandits for Smooth Graph Functions

Michal Valko, Remi Munos, Branislav Kveton, Tomáš Kocák

Robust Principal Component Analysis with Complex Noise

Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Lei Zhang

Scalable Semidefinite Relaxation for Maximum A Posterior Estimation

Qixing Huang, Yuxin Chen, Leonidas Guibas

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Cun Mu, Bo Huang, John Wright, Donald Goldfarb

Automated inference of point of view from user interactions in collective intelligence venues

Sanmay Das, Allen Lavoie

Rank-One Matrix Pursuit for Matrix Completion

Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye

Near-Optimal Joint Object Matching via Convex Relaxation

Yuxin Chen, Leonidas Guibas, Qixing Huang

Convex Total Least Squares

Dmitry Malioutov, Nikolai Slavov

On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection

Pratik Jawanpuria, Manik Varma, Saketha Nath

Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization

Xiaotong Yuan, Ping Li, Tong Zhang

A Unified Framework for Consistency of Regularized Loss Minimizers

Jean Honorio, Tommi Jaakkola

Geodesic Distance Function Learning via Heat Flow on Vector Fields

Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye

Near-Optimally Teaching the Crowd to Classify

Adish Singla, Ilija Bogunovic, Gabor Bartok, Amin Karbasi, Andreas Krause

On the convergence of no-regret learning in selfish routing

Walid Krichene, Benjamin Drighès, Alexandre Bayen

Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques

Jérémie Mary, Philippe Preux, Olivier Nicol

Scaling Up Robust MDPs using Function Approximation

Aviv Tamar, Shie Mannor, Huan Xu

Marginal Structured SVM with Hidden Variables

Wei Ping, Qiang Liu, Alex Ihler

Linear and Parallel Learning of Markov Random Fields

Yariv Mizrahi, Misha Denil, Nando De Freitas

Pitfalls in the use of Parallel Inference for the Dirichlet Process

Yarin Gal, Zoubin Ghahramani

Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing

Yuan Zhou, Xi Chen, Jian Li

Deep Generative Stochastic Networks Trainable by Backprop

Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski

A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models

Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, Jieping Ye

Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting

Yudong Chen, Jiaming Xu

Gaussian Process Optimization with Mutual Information

Emile Contal, Vianney Perchet, Nicolas Vayatis

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy

Dengyong Zhou, Qiang Liu, John Platt, Christopher Meek

Exchangeable Variable Models

Mathias Niepert, Pedro Domingos

Clustering in the Presence of Background Noise

Shai Ben-David, Nika Haghtalab

Safe Screening with Variational Inequalities and Its Application to Lasso

Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye

Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks

Shan-Hung Wu, Hao-Heng Chien, Kuan-Hua Lin, Philip Yu

Signal recovery from Pooling Representations

Joan Bruna Estrach, Arthur Szlam, Yann LeCun

PAC-inspired Option Discovery in Lifelong Reinforcement Learning

Emma Brunskill, Lihong Li

Multi-label Classification via Feature-aware Implicit Label Space Encoding

Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications

Sebastien Bratieres, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani

Anomaly Ranking as Supervised Bipartite Ranking

Stephan Clémençon, Sylvain Robbiano

Hierarchical Quasi-Clustering Methods for Asymmetric Networks

Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

Rectangular Tiling Process

Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda

Two-Stage Metric Learning

Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices

Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani

Elementary Estimators for High-Dimensional Linear Regression

Eunho Yang, Aurelie Lozano, Pradeep Ravikumar

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments

Eunho Yang, Aurelie Lozano, Pradeep Ravikumar

Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically

Yuan Fang, Kevin Chang, Hady Lauw

Bayesian Max-margin Multi-Task Learning with Data Augmentation

Chengtao Li, Jun Zhu, Jianfei Chen

Sparse Reinforcement Learning via Convex Optimization

Zhiwei Qin, Weichang Li, Firdaus Janoos

Gaussian Process Classification and Active Learning with Multiple Annotators

Filipe Rodrigues, Francisco Pereira, Bernardete Ribeiro

Structured Prediction of Network Response

Hongyu Su, Aristides Gionis, Juho Rousu

An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy

Gavin Taylor, Connor Geer, David Piekut

Optimization Equivalence of Divergences Improves Neighbor Embedding

Zhirong Yang, Jaakko Peltonen, Samuel Kaski

An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

Ji Liu, Steve Wright, Christopher Re, Victor Bittorf, Srikrishna Sridhar

Consistency of Causal Inference under the Additive Noise Model

Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schoelkopf

Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm

Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun

Linear Programming for Large-Scale Markov Decision Problems

Alan Malek, Yasin Abbasi-Yadkori, Peter Bartlett

Linear Time Solver for Primal SVM

Feiping Nie, Yizhen Huang, Heng Huang

Memory (and Time) Efficient Sequential Monte Carlo

Seong-Hwan Jun, Alexandre Bouchard-Côté

Scaling SVM and Least Absolute Deviations via Exact Data Reduction

Jie Wang, Peter Wonka, Jieping Ye

Latent Semantic Representation Learning for Scene Classification

Xin Li, Yuhong Guo

Least Squares Revisited: Scalable Approaches for Multi-class Prediction

Alekh Agarwal, Sham Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant

Local algorithms for interactive clustering

Pranjal Awasthi, Maria Balcan, Konstantin Voevodski

Model-Based Relational RL When Object Existence is Partially Observable

Vien Ngo, marc Toussaint

A new Q(lambda) with interim forward view and Monte Carlo equivalence

Rich Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt

On Robustness and Regularization of Structural Support Vector Machines

Mohamad Ali Torkamani, Daniel Lowd

Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting

Oscar Beijbom, Mohammad Saberian, David Kriegman, Nuno Vasconcelos

Multimodal Neural Language Models

Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli

Alternating Minimization for Mixed Linear Regression

Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

Stochastic Neighbor Compression

Matt Kusner, Stephen Tyree, Kilian Weinberger, Kunal Agrawal

Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification

Junfeng Wen, Chun-Nam Yu, Russell Greiner

Nonparametric Estimation of Multi-View Latent Variable Models

Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

Structured Generative Models of Natural Source Code

Chris Maddison, Daniel Tarlow

A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data

Jinfeng Yi, Lijun Zhang, Jun Wang, Rong Jin, Anil Jain

Statistical analysis of stochastic gradient methods for generalized linear models

Panagiotis Toulis, Edoardo Airoldi, Jason Rennie

Coding for Random Projections

Ping Li, Michael Mitzenmacher, Anshumali Shrivastava

Fast Computation of Wasserstein Barycenters

Marco Cuturi, Arnaud Doucet

Global graph kernels using geometric embeddings

Fredrik Johansson, Vinay Jethava, Devdatt Dubhashi, Chiranjib Bhattacharyya

Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data

Zhiyuan Chen, Bing Liu

K-means recovers ICA filters when independent components are sparse

Alon Vinnikov, Shai Shalev-Shwartz

Learning Mixtures of Linear Classifiers

Yuekai Sun, Stratis Ioannidis, Andrea Montanari

The Falling Factorial Basis and Its Statistical Applications

Yu-Xiang Wang, Alex Smola, Ryan Tibshirani

Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes

Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli

A Unifying View of Representer Theorems

Andreas Argyriou, Francesco Dinuzzo

Online Clustering of Bandits

Claudio Gentile, Shuai Li, Giovanni Zappella

Cold-start Active Learning with Robust Ordinal Matrix Factorization

Neil Houlsby, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

Multivariate Maximal Correlation Analysis

Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm

Efficient Label Propagation

Yasuhiro Fujiwara, Go Irie

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schoelkopf

Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising

Ling Yan, Wu-Jun Li, Gui-Rong Xue, Dingyi Han

Putting MRFs on a Tensor Train

Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov

Efficient Algorithms for Robust One-bit Compressive Sensing

Lijun Zhang, Jinfeng Yi, Rong Jin

Learning Complex Neural Network Policies with Trajectory Optimization

Sergey Levine, Vladlen Koltun

Composite Quantization for Approximate Nearest Neighbor Search

Ting Zhang, Chao Du, Jingdong Wang

Local Ordinal Embedding

Yoshikazu Terada, Ulrike von Luxburg

Reducing Dueling Bandits to Cardinal Bandits

Nir Ailon, Zohar Karnin, Thorsten Joachims

Large-margin Weakly Supervised Dimensionality Reduction

Chang Xu, Dacheng Tao, Chao Xu, Yong Rui

Joint Inference of Multiple Label Types in Large Networks

Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus Macskassy

Hard-Margin Active Linear Regression

Elad Hazan, Zohar Karnin

Maximum Margin Multiclass Nearest Neighbors

Aryeh Kontorovich, Roi Weiss

Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications

Tian Lin, Bruno Abrahao, Robert Kleinberg, John Lui, Wei Chen

Sparse meta-Gaussian information bottleneck

Melani Rey, Volker Roth, Thomas Fuchs

Nonparametric Estimation of Renyi Divergence and Friends

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

Robust Inverse Covariance Estimation under Noisy Measurements

Jun-Kun Wang, Shou-de Lin

Bayesian Optimization with Inequality Constraints

Jacob Gardner, Matt Kusner, Zhixiang, Kilian Weinberger, John Cunningham

Circulant Binary Embedding

Felix Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang

Multiple Testing under Dependence via Semiparametric Graphical Models

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page

Making Fisher Discriminant Analysis Scalable

Bojun Tu, Zhihua Zhang, Shusen Wang, Hui Qian

Hierarchical Dirichlet Scaling Process

Dongwoo Kim, Alice Oh

Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process

Issei Sato, Hiroshi Nakagawa

A PAC-Bayesian bound for Lifelong Learning

Anastasia Pentina, Christoph Lampert

Communication-Efficient Distributed Optimization using an Approximate Newton-type Method

Ohad Shamir, Nati Srebro, Tong Zhang

Concept Drift Detection Through Resampling

Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer

Anti-differentiating approximation algorithms:A case study with min-cuts, spectral, and flow

David Gleich, Michael Mahoney

A Bayesian Wilcoxon signed-rank test based on the Dirichlet process

Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon, Fabrizio Ruggeri

Min-Max Problems on Factor Graphs

Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner

Distributed Stochastic Gradient MCMC

Sungjin Ahn, Babak Shahbaba, Max Welling

Nearest Neighbors Using Compact Sparse Codes

Anoop Cherian

Optimal Mean Robust Principal Component Analysis

Feiping Nie, Jianjun Yuan, Heng Huang

Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows

Robert Busa-Fekete, Eyke Huellermeier, Balázs Szörényi

Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations

Bilal Ahmed, Thomas Thesen, Karen Blackmon, Yijun Zhao, Orrin Devinsky, Ruben Kuzniecky, Carla Brodley

A Physics-Based Model Prior for Object-Oriented MDPs

Jonathan Scholz, Martin Levihn, Charles Isbell, David Wingate

Outlier Path: A Homotopy Algorithm for Robust SVM

Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi

Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data

Naiyan Wang, Dit-Yan Yeung

Latent Confusion Analysis by Normalized Gamma Construction

Issei Sato, Hisashi Kashima, Hiroshi Nakagawa

Finito: A faster, permutable incremental gradient method for big data problems

Aaron Defazio, Justin Domke, tiberio Caetano

Ensemble Methods for Structured Prediction

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri

Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance

Simone Romano, James Bailey, Vinh Nguyen, Karin Verspoor

Preserving Modes and Messages via Diverse Particle Selection

Jason Pacheco, Silvia Zuffi, Michael Black, Erik Sudderth

Nonlinear Information-Theoretic Compressive Measurement Design

Liming Wang, Abolfazl Razi, Miguel Rodrigues, Robert Calderbank, Lawrence Carin

Dual Query: Practical Private Query Release for High Dimensional Data

Marco Gaboardi, Emilio Jesus Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

Deep Boosting

Corinna Cortes, Mehryar Mohri, Umar Syed

Distributed Representations of Sentences and Documents

Quoc Le, Tomas Mikolov

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

Robert McGibbon, Bharath Ramsundar, Mohammad Sultan, Gert Kiss, Vijay Pande

Online Multi-Task Learning for Policy Gradient Methods

Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, Matthew Taylor

Affinity Weighted Embedding

Jason Weston, Ron Weiss, Hector Yee

Learning the Parameters of Determinantal Point Process Kernels

Raja Hafiz Affandi, Emily Fox, Ryan Adams, Ben Taskar

Discrete Chebyshev Classifiers

Elad Eban, Elad Mezuman, Amir Globerson

Deep AutoRegressive Networks

Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra

A Convergence Rate Analysis for LogitBoost, MART and Their Variant

Peng Sun, Tong Zhang, Jie Zhou

Inferning with High Girth Graphical Models

Uri Heinemann, Amir Globerson

Learning Latent Variable Gaussian Graphical Models

Zhaoshi Meng, Brian Eriksson, Al Hero

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

One Practical Algorithm for Both Stochastic and Adversarial Bandits

Yevgeny Seldin, Aleksandrs Slivkins

Robust and Efficient Kernel Hyperparameter Paths with Guarantees

Joachim Giesen, Soeren Laue, Patrick Wieschollek

Active Transfer Learning under Model Shift

Xuezhi Wang, Tzu-Kuo Huang, Jeff Schneider

Approximate Policy Iteration Schemes: A Comparison

Bruno Scherrer

Stable and Efficient Representation Learning with Nonnegativity Constraints

Tsung-Han Lin, H. T. Kung

Sample Efficient Reinforcement Learning with Gaussian Processes

Robert Grande, Thomas Walsh, Jonathan How

Memory and Computation Efficient PCA via Very Sparse Random Projections

Farhad Pourkamali Anaraki, Shannon Hughes

Time-Regularized Interrupting Options (TRIO)

Timothy Mann, Daniel Mankowitz, Shie Mannor

Randomized Nonlinear Component Analysis

David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schoelkopf

High Order Regularization for Semi-Supervised Learning of Structured Output Problems

Yujia Li, Rich Zemel

Transductive Learning with Multi-class Volume Approximation

Gang Niu, Bo Dai, Christoffel du Plessis, Masashi Sugiyama

Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison

Borja Balle, William Hamilton, Joelle Pineau

Effective Bayesian Modeling of Groups of Related Count Time Series

Nicolas Chapados

Variational Inference for Sequential Distance Dependent Chinese Restaurant Process

Sergey Bartunov, Dmitry Vetrov

Discovering Latent Network Structure in Point Process Data

Scott Linderman, Ryan Adams

A Kernel Independence Test for Random Processes

Kacper Chwialkowski, Arthur Gretton

Learning to Disentangle Factors of Variation with Manifold Interaction

Scott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee

Learning Modular Structures from Network Data and Node Variables

Elham Azizi, Edoardo Airoldi, James Galagan

Probabilistic Partial Canonical Correlation Analysis

Yusuke Mukuta, tatsuya Harada

Skip Context Tree Switching

Marc Bellemare, Joel Veness, Erik Talvitie

Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians

Christopher Tosh, Sanjoy Dasgupta

Marginalized Denoising Auto-encoders for Nonlinear Representations

Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua Bengio

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations

David Barber, Yali Wang

Fast Multi-stage Submodular Maximization

Kai Wei, Rishabh Iyer, Jeff Bilmes

Programming by Feedback

Marc Schoenauer, Riad Akrour, Michele Sebag, Jean-Christophe Souplet

Probabilistic Matrix Factorization with Non-random Missing Data

Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani

Pursuit-Evasion Without Regret, with an Application to Trading

Lili Dworkin, Michael Kearns, Yuriy Nevmyvaka

The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation

Sven Kurras, Ulrike von Luxburg, Gilles Blanchard

Riemannian Pursuit for Big Matrix Recovery

Mingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan

Dynamic Programming Boosting for Discriminative Macro-Action Discovery

Leonidas Lefakis, Francois Fleuret

Online Stochastic Optimization under Correlated Bandit Feedback

Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill

Weighted Graph Clustering with Non-Uniform Uncertainties

Yudong Chen, Shiau Hong Lim, Huan Xu

GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results

Philip Thomas

A Bayesian Framework for Online Classifier Ensemble

Qinxun Bai, Henry Lam, Stan Sclaroff

Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm

Jacob Steinhardt, Percy Liang

Gaussian Approximation of Collective Graphical Models

Liping Liu, Daniel Sheldon, Thomas Dietterich

On learning to localize objects with minimal supervision

Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell

Multiresolution Matrix Factorization

Risi Kondor, Nedelina Teneva, Vikas Garg

Learnability of the Superset Label Learning Problem

Liping Liu, Thomas Dietterich

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert Schapire

Structured Recurrent Temporal Restricted Boltzmann Machines

Roni Mittelman, Benjamin Kuipers, Silvio Savarese, Honglak Lee

Scalable and Robust Bayesian Inference via the Median Posterior

Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David Dunson

Kernel Adaptive Metropolis-Hastings

Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton

Input Warping for Bayesian Optimization of Non-Stationary Functions

Jasper Snoek, Kevin Swersky, Rich Zemel, Ryan Adams

Stochastic Gradient Hamiltonian Monte Carlo

Tianqi Chen, Emily Fox, Carlos Guestrin

A Deep Semi-NMF Model for Learning Hidden Representations

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern Schuller

Asynchronous Distributed ADMM for Consensus Optimization

Ruiliang Zhang, James Kwok

Spectral Regularization for Max-Margin Sequence Tagging

Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson

Learning by Stretching Deep Networks

Gaurav Pandey, Ambedkar Dukkipati

Nonnegative Sparse PCA with Provable Guarantees

Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros Dimakis

Active Learning of Parameterized Skills

Bruno Da Silva, George Konidaris, Andrew Barto

Learning Ordered Representations with Nested Dropout

Oren Rippel, Michael Gelbart, Ryan Adams

Learning the Irreducible Representations of Commutative Lie Groups

Taco Cohen, Max Welling

Towards End-To-End Speech Recognition with Recurrent Neural Networks

Alex Graves, Navdeep Jaitly

Multi-period Trading Prediction Markets with Connections to Machine Learning

Jinli Hu, Amos Storkey

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets

Diederik Kingma, Max Welling

Neural Variational Inference and Learning in Belief Networks

Andriy Mnih, Karol Gregor

Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors

Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David Dunson, Lawrence Carin

Beta Diffusion Trees

Creighton Heaukulani, David Knowles, Zoubin Ghahramani

Learning Character-level Representations for Part-of-Speech Tagging

Cicero Dos Santos, Bianca Zadrozny

Saddle Points and Accelerated Perceptron Algorithms

Adams Wei Yu, Fatma Kilinc-Karzan, Jaime Carbonell

Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization

Hua Wang, Feiping Nie, Heng Huang

Learning from Contagion (Without Timestamps)

Kareem Amin, Hoda Heidari, Michael Kearns

Stochastic Variational Inference for Bayesian Time Series Models

Matthew Johnson, Alan Willsky

A Clockwork RNN

Jan Koutnik, Klaus Greff, Faustino Gomez, Juergen Schmidhuber

Estimating Latent-Variable Graphical Models using Moments and Likelihoods

Arun Tejasvi Chaganty, Percy Liang

Universal Matrix Completion

Srinadh Bhojanapalli, Prateek Jain

Finding Dense Subgraphs via Low-Rank Bilinear Optimization

Dimitris Papailiopoulos, Ioannis Mitliagkas, Alexandros Dimakis, Constantine Caramanis

Compositional Morphology for Word Representations and Language Modelling

Jan Botha, Phil Blunsom

Learning Polynomials with Neural Networks

Alexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang

Exponential Family Matrix Completion under Structural Constraints

Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh

Sample-based approximate regularization

Philip Bachman, Amir-Massoud Farahmand, Doina Precup

A Compilation Target for Probabilistic Programming Languages

Brooks Paige, Frank Wood

Adaptive Monte Carlo via Bandit Allocation

James Neufeld, Andras Gyorgy, Csaba Szepesvari, Dale Schuurmans

Efficient Dimensionality Reduction for High-Dimensional Network Estimation

Safiye Celik, Benjamin Logsdon, Su-In Lee

Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes

E. Busra Celikkaya, Christian Shelton

Doubly Stochastic Variational Bayes for non-Conjugate Inference

Michalis Titsias, Miguel Lázaro-Gredilla

Efficient Learning of Mahalanobis Metrics for Ranking

Daryl Lim, Gert Lanckriet

GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare

Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal

A reversible infinite HMM using normalised random measures

David Knowles, Zoubin Ghahramani, Konstantina Palla

Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing

Benjamin Haeffele, Eric Young, Rene Vidal

Influence Function Learning in Information Diffusion Networks

Nan Du, Yingyu Liang, Maria Balcan, Le Song