Accepted papers
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization Peilin Zhao, Tong Zhang
Approval Voting and Incentives in Crowdsourcing Nihar Shah, Dengyong Zhou, Yuval Peres
A low variance consistent test of relative dependency Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko
An Aligned Subtree Kernel for Weighted Graphs Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock
Spectral Clustering via the Power Method – Provably Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens
Information Geometry and Minimum Description Length Networks Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele
Adaptive Stochastic Alternating Direction Method of Multipliers Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li
A Lower Bound for the Optimization of Finite Sums Alekh Agarwal, Leon Bottou
Learning Word Representations with Hierarchical Sparse Coding Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith
Learning Transferable Features with Deep Adaptation Networks Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan
Robust partially observable Markov decision process Takayuki Osogami
On the Relationship between Sum-Product Networks and Bayesian Networks Han Zhao, Mazen Melibari, Pascal Poupart
Learning from Corrupted Binary Labels via Class-Probability Estimation Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate Ohad Shamir
Attribute Efficient Linear Regression with Distribution-Dependent Sampling Doron Kukliansky, Ohad Shamir
Learning Local Invariant Mahalanobis Distances Ethan Fetaya, Shimon Ullman
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis Zhuang Ma, Yichao Lu, Dean Foster
Abstraction Selection in Model-based Reinforcement Learning Nan Jiang, Alex Kulesza, Satinder Singh
Surrogate Functions for Maximizing Precision at the Top Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes Harikrishna Narasimhan, Purushottam Kar, Prateek Jain
Coresets for Nonparametric Estimation – the Case of DP-Means Olivier Bachem, Mario Lucic, Andreas Krause
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits Pratik Gajane, Tanguy Urvoy, Fabrice Clérot
Functional Subspace Clustering with Application to Time Series Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams Rose Yu, Dehua Cheng, Yan Liu
Atomic Spatial Processes Sean Jewell, Neil Spencer, Alexandre Bouchard-Côté
Classification with Low Rank and Missing Data Elad Hazan, Roi Livni, Yishay Mansour
Dynamic Sensing: Better Classification under Acquisition Constraints Oran Richman, Shie Mannor
A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis Pinghua Gong, Jieping Ye
Telling cause from effect in deterministic linear dynamical systems Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve
High Dimensional Bayesian Optimisation and Bandits via Additive Models Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos
Theory of Dual-sparse Regularized Randomized Reduction Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
Generalization error bounds for learning to rank: Does the length of document lists matter? Ambuj Tewari, Sougata Chaudhuri
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data Toby Hocking, Guillem Rigaill, Guillaume Bourque
Mind the duality gap: safer rules for the Lasso Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
A General Analysis of the Convergence of ADMM Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization Yuchen Zhang, Xiao Lin
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss Yuchen Zhang, Xiao Lin
Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons Yuxin Chen, Changho Suh
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor
Structural Maxent Models Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning Debarghya Ghoshdastidar, Ambedkar Dukkipati
The Benefits of Learning with Strongly Convex Approximate Inference Ben London, Bert Huang, Lise Getoor
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA Bo Xin, David Wipf
Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View Takanori Maehara, Akihiro Yabe, Ken-ichi Kawarabayashi
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains Katharina Blechschmidt, Joachim Giesen, Soeren Laue
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds Yuchen Zhang, Martin Wainwright, Michael Jordan
Landmarking Manifolds with Gaussian Processes Dawen Liang, John Paisley
Markov Mixed Membership Models Aonan Zhang, John Paisley
A Unified Framework for Outlier-Robust PCA-like Algorithms Wenzhuo Yang, Huan Xu
Streaming Sparse Principal Component Analysis Wenzhuo Yang, Huan Xu
A Divide and Conquer Framework for Distributed Graph Clustering Wenzhuo Yang, Huan Xu
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? Senjian An, Farid Boussaid, Mohammed Bennamoun
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning K. Lakshmanan, Ronald Ortner, Daniil Ryabko
The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling Michael Betancourt
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets Dan Garber, Elad Hazan
Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models Mrinal Das, Trapit Bansal, Chiranjib Bhattacharyya
Online Learning of Eigenvectors Dan Garber, Elad Hazan, Tengyu Ma
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low
Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz
Ordinal Mixed Membership Models Seppo Virtanen, Mark Girolami
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares Garvesh Raskutti, Michael Mahoney
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence Nathaniel Korda, Prashanth La
Learning Parametric-Output HMMs with Two Aliased States Roi Weiss, Boaz Nadler
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data Yarin Gal, Yutian Chen, Zoubin Ghahramani
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs Yarin Gal, Richard Turner
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal
Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik Csiba, Zheng Qu, Peter Richtarik
Vector-Space Markov Random Fields via Exponential Families Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka
Low Rank Approximation using Error Correcting Coding Matrices Shashanka Ubaru, Arya Mazumdar, Yousef Saad
Off-policy Model-based Learning under Unknown Factored Dynamics Assaf Hallak, Francois Schnitzler, Timothy Mann, Shie Mannor
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xianqiu Li, Xilin Chen
Asymmetric Transfer Learning with Deep Gaussian Processes Melih Kandemir
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing Rongda Zhu, Quanquan Gu
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments Stephan Gouws, Yoshua Bengio, Greg Corrado
Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization Jiangwen Sun, Jin Lu, Tingyang Xu, Jinbo Bi
Cascading Bandits: Learning to Rank in the Cascade Model Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models James Foulds, Shachi Kumar, Lise Getoor
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions Alina Ene, Huy Nguyen
Alpha-Beta Divergences Discover Micro and Macro Structures in Data Karthik Narayan, Ali Punjani, Pieter Abbeel
Fictitious Self-Play in Extensive-Form Games Johannes Heinrich, Marc Lanctot, David Silver
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback Adith Swaminathan, Thorsten Joachims
The Hedge Algorithm on a Continuum Walid Krichene, Maximilian Balandat, Claire Tomlin, Alexandre Bayen
A Linear Dynamical System Model for Text David Belanger, Sham Kakade
Unsupervised Learning of Video Representations using LSTMs Nitish Srivastava, Elman Mansimov, Ruslan Salakhudinov
Message Passing for Collective Graphical Models Tao Sun, Dan Sheldon, Akshat Kumar
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics Yining Wang, Jun Zhu
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, Yan Liu
MADE: Masked Autoencoder for Distribution Estimation Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle
An Online Learning Algorithm for Bilinear Models Yuanbin Wu, Shiliang Sun
Adaptive Belief Propagation Georgios Papachristoudis, John Fisher
Large-scale log-determinant computation through stochastic Chebyshev expansions Insu Han, Dmitry Malioutov, Jinwoo Shin
Differentially Private Bayesian Optimization Matt Kusner, Jacob Gardner, Roman Garnett, Kilian Weinberger
A Nearly-Linear Time Framework for Graph-Structured Sparsity Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
Support Matrix Machines Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li
Rademacher Observations, Private Data, and Boosting Richard Nock, Giorgio Patrini, Arik Friedman
From Word Embeddings To Document Distances Matt Kusner, Yu Sun, Nicholas Kolkin, Kilian Weinberger
Bayesian and Empirical Bayesian Forests Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle
Inferring Graphs from Cascades: A Sparse Recovery Framework Jean Pouget-Abadie, Thibaut Horel
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM Ching-Pei Lee, Dan Roth
Safe Exploration for Optimization with Gaussian Processes Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause
The Ladder: A Reliable Leaderboard for Machine Learning Competitions Avrim Blum, Moritz Hardt
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) Maurizio Filippone, Raphael Engler
Finding Galaxies in the Shadows of Quasars with Gaussian Processes Roman Garnett, Shirley Ho, Jeff Schneider
Following the Perturbed Leader for Online Structured Learning Alon Cohen, Tamir Hazan
Reified Context Models Jacob Steinhardt, Percy Liang
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen, Alan Malek
Learning Fast-Mixing Models for Structured Prediction Jacob Steinhardt, Percy Liang
A Probabilistic Model for Dirty Multi-task Feature Selection Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani
On Deep Multi-View Representation Learning Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes
Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems Qiang Zhou, Qi Zhao
Efficient Learning in Large-Scale Combinatorial Semi-Bandits Zheng Wen, Branislav Kveton, Azin Ashkan
Swept Approximate Message Passing for Sparse Estimation Andre Manoel, Florent Krzakala, Eric Tramel, Lenka Zdeborovà
Simple regret for infinitely many armed bandits Alexandra Carpentier, Michal Valko
Exponential Integration for Hamiltonian Monte Carlo Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Faster cover trees Mike Izbicki, Christian Shelton
Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization Tyler Johnson, Carlos Guestrin
Unsupervised Domain Adaptation by Backpropagation Yaroslav Ganin, Victor Lempitsky
Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu
Manifold-valued Dirichlet Processes Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh
Multi-Task Learning for Subspace Segmentation Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap Tim Salimans, Diederik Kingma, Max Welling
Scalable Model Selection for Large-Scale Factorial Relational Models Chunchen Liu, Lu Feng, Ryohei Fujimaki, Yusuke Muraoka
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets Rafael Barbosa, Alina Ene, Huy Nguyen, Justin Ward
Dealing with small data: On the generalization of context trees Ralf Eggeling, Mikko Koivisto, Ivo Grosse
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin
A Bayesian nonparametric procedure for comparing algorithms Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon
Convergence rate of Bayesian tensor estimator and its minimax optimality Taiji Suzuki
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments Yifan Wu, Andras Gyorgy, Csaba Szepesvari
Nested Sequential Monte Carlo Methods Christian Naesseth, Fredrik Lindsten, Thomas Schon
Sparse Variational Inference for Generalized GP Models Rishit Sheth, Yuyang Wang, Roni Khardon
Universal Value Function Approximators Tom Schaul, Daniel Horgan, Karol Gregor, David Silver
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games Julien Perolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin
On Greedy Maximization of Entropy Dravyansh Sharma, Ashish Kapoor, Amit Deshpande
Metadata Dependent Mondrian Processes Yi Wang, Bin Li, Yang Wang, Fang Chen
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM Xiaojun Chang, Yi Yang, Eric Xing, Yaoliang Yu
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki
Double Nystr\”om Method: An Efficient and Accurate Nystr\”om Scheme for Large-Scale Data Sets Woosang Lim, Minhwan Kim, Haesun Park, Kyomin Jung
The Composition Theorem for Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Convex Formulation for Learning from Positive and Unlabeled Data Marthinus Du Plessis, Gang Niu, Masashi Sugiyama
Threshold Influence Model for Allocating Advertising Budgets Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura
Strongly Adaptive Online Learning Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
CUR Algorithm for Partially Observed Matrices Miao Xu, Rong Jin, Zhi-Hua Zhou
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data Yining Wang, Yu-Xiang Wang, Aarti Singh
MRA-based Statistical Learning from Incomplete Rankings Eric Sibony, Stéphan Clemençon, Jérémie Jakubowicz
Risk and Regret of Hierarchical Bayesian Learners Jonathan Huggins, Josh Tenenbaum
Towards a Learning Theory of Cause-Effect Inference David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin
DRAW: A Recurrent Neural Network For Image Generation Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, Daan Wierstra
Multiview Triplet Embedding: Learning Attributes in Multiple Maps Ehsan Amid, Antti Ukkonen
Distributed Gaussian Processes Marc Deisenroth, Jun Wei Ng
Guaranteed Tensor Decomposition: A Moment Approach Gongguo Tang, Parikshit Shah
$\ell_{1,p}$-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods Zirui Zhou, Qi Zhang, Anthony Man-Cho So
Consistent estimation of dynamic and multi-layer block models Qiuyi Han, Kevin Xu, Edoardo Airoldi
On the Rate of Convergence and Error Bounds for LSTD($\lambda$) Manel Tagorti, Bruno Scherrer
Variational Inference with Normalizing Flows Danilo Rezende, Shakir Mohamed
Controversy in mechanistic modelling with Gaussian processes Benn Macdonald, Catherine Higham, Dirk Husmeier
Convex Learning of Multiple Tasks and their Structure Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco
K-hyperplane Hinge-Minimax Classifier Margarita Osadchy, Tamir Hazan, Daniel Keren
Non-Stationary Approximate Modified Policy Iteration Boris Lesner, Bruno Scherrer
Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees Mathieu Serrurier, Henri Prade
Geometric Conditions for Subspace-Sparse Recovery Chong You, Rene Vidal
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process Amar Shah, David Knowles, Zoubin Ghahramani
Long Short-Term Memory Over Recursive Structures Xiaodan Zhu, Parinaz Sobihani, Hongyu Guo
Weight Uncertainty in Neural Network Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
Learning Submodular Losses with the Lovasz Hinge Jiaqian Yu, Matthew Blaschko
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke
Hashing for Distributed Data Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu
Large-scale Distributed Dependent Nonparametric Trees Zhiting Hu, Ho Qirong, Avinava Dubey, Eric Xing
Qualitative Multi-Armed Bandits: A Quantile-Based Approach Balazs Szorenyi, Robert Busa-Fekete, Paul Weng, Eyke Hüllermeier
Deep Edge-Aware Filters Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia
A Convex Optimization Framework for Bi-Clustering Shiau Hong Lim, Yudong Chen, Huan Xu
Is Feature Selection Secure against Training Data Poisoning? Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani
A Theoretical Analysis of Metric Hypothesis Transfer Learning Michaël Perrot, Amaury Habrard
Generative Moment Matching Networks Yujia Li, Kevin Swersky, Rich Zemel
Stay on path: PCA along graph paths Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran
Deep Learning with Limited Numerical Precision Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices Jie Wang, Jieping Ye
Harmonic Exponential Families on Manifolds Taco Cohen, Max Welling
Training Deep Convolutional Neural Networks to Play Go Christopher Clark, Amos Storkey
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) Andrew Wilson, Hannes Nickisch
Learning Deep Structured Models Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun
Community Detection Using Time-Dependent Personalized PageRank Haim Avron, Lior Horesh
Scalable Variational Inference in Log-supermodular Models Josip Djolonga, Andreas Krause
Variational Inference for Gaussian Process Modulated Poisson Processes Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts
Scalable Deep Poisson Factor Analysis for Topic Modeling Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin
Hidden Markov Anomaly Detection Nico Goernitz, Mikio Braun, Marius Kloft
Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo
Convex Calibrated Surrogates for Hierarchical Classification Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks Jose Miguel Hernandez-Lobato, Ryan Adams
Active Nearest Neighbors in Changing Environments Christopher Berlind, Ruth Urner
Bipartite Edge Prediction via Transductive Learning over Product Graphs Hanxiao Liu, Yiming Yang
Trust Region Policy Optimization John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz
Discovering Temporal Causal Relations from Subsampled Data Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit Dhillon
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing
On Symmetric and Asymmetric LSHs for Inner Product Search Behnam Neyshabur, Nathan Srebro
The Kendall and Mallows Kernels for Permutations Yunlong Jiao, Jean-Philippe Vert
Bayesian Multiple Target Localization Purnima Rajan, Weidong Han, Raphael Sznitman, Peter Frazier, Bruno Jedynak
Submodularity in Data Subset Selection and Active Learning Kai Wei, Rishabh Iyer, Jeff Bilmes
Variational Generative Stochastic Networks with Collaborative Shaping Philip Bachman, Doina Precup
Adding vs. Averaging in Distributed Primal-Dual Optimization Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac
Feature-Budgeted Random Forest Feng Nan, Joseph Wang, Venkatesh Saligrama
Entropic Graph-based Posterior Regularization Maxwell Libbrecht, Michael Hoffman, Jeff Bilmes, William Noble
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations Tam Le, Marco Cuturi
Low-Rank Matrix Recovery from Row-and-Column Affine Measurements Or Zuk, Avishai Wagner
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction Sébastien Giguère, Amélie Rolland, Francois Laviolette, Mario Marchand
A Multitask Point Process Predictive Model Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin
A Hybrid Approach for Probabilistic Inference using Random Projections Michael Zhu, Stefano Ermon
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio
Learning to Search Better than Your Teacher Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume, John Langford
Gated Feedback Recurrent Neural Networks Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio
Context-based Unsupervised Data Fusion for Decision Making Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar
Phrase-based Image Captioning Remi Lebret, Pedro Pinheiro, Ronan Collobert
Celeste: Variational inference for a generative model of astronomical images Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat
Distributional Rank Aggregation, and an Axiomatic Analysis Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar
Gradient-based Hyperparameter Optimization through Reversible Learning Dougal Maclaurin, David Duvenaud, Ryan Adams
Bimodal Modelling of Source Code and Natural Language Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
Cheap Bandits Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Remi Munos
Subsampling Methods for Persistent Homology Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry Wasserman
An embarrassingly simple approach to zero-shot learning Bernardino Romera-Paredes, Philip Torr
Binary Embedding: Fundamental Limits and Fast Algorithm Xinyang Yi, Constantine Caramanis, Eric Price
Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, Ryan Adams
How Hard is Inference for Structured Prediction? Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim
Online Time Series Prediction with Missing Data Oren Anava, Elad Hazan, Assaf Zeevi
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach Jason Pacheco, Erik Sudderth
A Fast Variational Approach for Learning Markov Random Field Language Models Yacine Jernite, Alexander Rush, David Sontag
Removing systematic errors for exoplanet search via latent causes Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes Yves-Laurent Kom Samo, Stephen Roberts
Correlation Clustering in Data Streams KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior Qingming Tang, Siqi Sun, Jinbo Xu
Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli
Modeling Order in Neural Word Embeddings at Scale Andrew Trask, David Gilmore, Matthew Russell
Distributed Inference for Dirichlet Process Mixture Models Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani
Compressing Neural Networks with the Hashing Trick Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees Rong Ge, James Zou
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix Roger Grosse, Ruslan Salakhudinov
A Deeper Look at Planning as Learning from Replay Harm Vanseijen, Rich Sutton
Optimal and Adaptive Algorithms for Online Boosting Alina Beygelzimer, Satyen Kale, Haipeng Luo
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems Christopher De Sa, Christopher Re, Kunle Olukotun
An Empirical Exploration of Recurrent Network Architectures Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever
Complete Dictionary Recovery Using Nonconvex Optimization Ju Sun, Qing Qu, John Wright
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret Haitham Bou Ammar, Rasul Tutunov, Eric Eaton
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon
High Confidence Policy Improvement Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh
Fixed-point algorithms for learning determinantal point processes Zelda Mariet, Suvrit Sra
Consistent Multiclass Algorithms for Complex Performance Measures Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha, Shivani Agarwal
Optimizing Neural Networks with Kronecker-factored Approximate Curvature James Martens, Roger Grosse
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon
Multi-instance multi-label learning in the presence of novel class instances Anh Pham, Raviv Raich, Xiaoli Fern, Jesús Pérez Arriaga
Entropy-Based Concentration Inequalities for Dependent Variables Liva Ralaivola, Massih-Reza Amini
PU Learning for Matrix Completion Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization Necdet Aybat, Zi Wang, Garud Iyengar
Sparse Subspace Clustering with Missing Entries Congyuan Yang, Daniel Robinson, Rene Vidal
Moderated and Drifting Linear Dynamical Systems Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton Morrison, Emily Butler, Kobus Barnard
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions Taehoon Lee, Sungroh Yoon
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu-Xiang Wang, Stephen Fienberg, Alex Smola
A trust-region method for stochastic variational inference with applications to streaming data Lucas Theis, Matt Hoffman
Inference in a Partially Observed Queuing Model with Applications in Ecology Kevin Winner, Garrett Bernstein, Dan Sheldon
Deterministic Independent Component Analysis Ruitong Huang, Andras Gyorgy, Csaba Szepesvári
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property Maxime Gasse, Alexandre Aussem, Haytham Elghazel
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization Roy Frostig, Rong Ge, Sham Kakade, Aaron Sidford
A New Generalized Error Path Algorithm for Model Selection Bin Gu, Charles Ling