Cycle I Accepted Papers

Cycle I proceedings are available at

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, Guibas Leonidas, 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, Franois 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 II Accepted Papers

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, Ryan Tibshirani, Alex Smola
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
Zohar Karnin, Elad Hazan
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, Ting-Wei Lin, Shou-de Lin
Bayesian Optimization with Inequality Constraints
Jacob Gardner, Matt Kusner, Kilian Weinberger, John Cunningham, Zhixiang (Eddie) Xu
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, Hui Qian, Zhihua Zhang
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, Balázs Szörényi, Eyke Huellermeier
Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations
Bilal Ahmed, Karen Blackmon, Thomas Thesen, Ruben Kuzniecky, Chad Carlson, Jacqueline French, Werner Doyle, Carla Brodley
A Physics-Based Model Prior for Object-Oriented MDPs
Jonathan Scholz, Martin Levihn, Charles Isbell
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, Kashima Hisashi, 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
Robust and Efficient Representation Learning with Nonnegativity Constraints
Tsung-Han Lin
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 Representations for Interacting Manifolds with Higher-order Boltzmann Machines
Scott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee
Learning Modular Structures from Network Data and Node Variables
Elham Azizi, Edoardo Airoldi
Probabilistic Partial Canonical Correlation Analysis
Yusuke Mukuta, Tatsuya Harada
Skip Context Tree Switching
Marc Bellemare, Joel Veness, Erik Talvitie, Alex Graves
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 Regrets, with an Application to Trading
Lili Dworkin, Michael Kearns, Yuriy Nevmyvaka
The f-Adjusted Laplacian: a Diagonal Perturbation with a Geometric Interpretation
Sven Kurras, Ulrike von Luxburg, Gilles Blanchard
Riemannian Pursuit for Big Matrix Recovery
Mingkui Tan, Ivor W. Tsang, Li Wang, Jialin Pan, Bart Vandereycken
Dynamic Programming Boosting for Discriminative Macro-Action Discovery
Leonidas Lefakis, Francois Fleuret
Resource-Efficient Stochastic Optimization of a Locally Smooth Function 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
One-Bit Object Detection: 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 Algorithm for Global Variable 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, Alexandros Dimakis, Dimitris Papailiopoulos
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 Nonparametric Bayesian Analysis of Incomplete Multiway Data
Piyush Rai, Yingjian Wang, 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
Konstantina Palla, David Knowles, Zoubin Ghahramani
Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing
Benjamin Haeffele, Rene Vidal, Eric Young
Influence Function Learning in Information Diffusion Networks
Nan Du, Yingyu Liang, Le Song, Maria Balcan
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, Alex Gittens, Ying Xiao, 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
Remi Munos, Michal Valko, Branislav Kveton, Tomas Kocak
Robust Principal Component Analysis with Complex Noise
Qian Zhao, Deyu Meng, Lei Zhang, Wangmeng Zuo, Zongben Xu
Scalable Semidefinite Relaxation for Maximum A Posteriori Estimation
Qixing Huang, Yuxin Chen, Guibas Leonidas
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
Orthogonal 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, Guibas Leonidas, 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
Learning With Priors
Jean Honorio, Tommi Jaakkola
Geodesic Distance Function Learning via Heat Flows on Vector Fields
Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye
Active Teaching for Crowdsourcing Classification
Adish Singla, Ilija Bogunovic, Gabor Bartok, Amin Karbasi, Andreas Krause
On the Convergence of No-regret Learning in Selfish Routing
Benjamin Drighès, Walid Krichene, Alexandre Bayen
Offline Evaluation of Recommendation Systems
Olivier Nicol, Jérémie Mary, Philippe Preux
Scaling Up Robust MDPs by Reinforcement Learning
Aviv Tamar, Huan Xu, Shie Mannor
Marginal Structured SVM with Hidden Variables
Wei Ping, Qiang Liu, Alex Ihler
From Exponential to Linear Complexity When Learning Practical Markov Random Fields
Yariv Mizrahi, Nando De Freitas, Luis Tenorio
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, Jason Yosinski
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models
Jie Wang, Qingyang Li, Sen Yang, Wei Fan, 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, 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
Nika Haghtalab, Shai Ben-David
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 $\ell_p$ 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
Learning with Smoothness: Pointwise, Graph-based, Probabilistic
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
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, Srikrishna Sridhar, Vicotr Bittorf
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
Implicit Particle Sequential Monte Carlo
Seong-Hwan Jun, Alexandre Bouchard-Côté
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
Jie Wang, 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, Konstantin Voevodski, Maria Balcan
Learning and Planning with Relational Uncertainty Predicates over the Existence of Objects
Vien Ngo, Marc Toussaint
A New Q(\lambda)
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, Nuno Vasconcelos, David Kriegman
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel
An Adaptive Low Dimensional quasi-Newton Sum of Functions Optimizer
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 for High-dimensional Data
Jinfeng Yi, Lijun Zhang, Jun Wang, Rong Jin, Anil Jain
Stochastic Approximation with Implicit Updates. Applications in Robust Online Learning of GLMs
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
2013-2014 ICML | International Conference on Machine Learning