ICML 2014

310 papers

Influence Function Learning in Information Diffusion Networks

Nan Du, Yingyu Liang, Maria Balcan, Le Song

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

Benjamin Haeffele, Eric Young, René Vidal

A reversible infinite HMM using normalised random measures

David Knowles, Zoubin Ghahramani, Konstantina Palla

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

Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal

Efficient Learning of Mahalanobis Metrics for Ranking

Daryl Lim, Gert Lanckriet

Doubly Stochastic Variational Bayes for non-Conjugate Inference

Michalis Titsias, Miguel Lázaro-Gredilla

Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes

E. Busra Celikkaya, Christian Shelton

Efficient Dimensionality Reduction for High-Dimensional Network Estimation

Safiye Celik, Benjamin Logsdon, Su-in Lee

Adaptive Monte Carlo via Bandit Allocation

James Neufeld, András György, Csaba Szepesvári, Dale Schuurmans

A Compilation Target for Probabilistic Programming Languages

Brooks Paige, Frank Wood

Sample-based approximate regularization

Philip Bachman, Amir-massoud Farahmand, Doina Precup

Exponential Family Matrix Completion under Structural Constraints

Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh

Learning Polynomials with Neural Networks

Alexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang

Compositional Morphology for Word Representations and Language Modelling

Jan Botha, Phil Blunsom

Finding Dense Subgraphs via Low-Rank Bilinear Optimization

Dimitris Papailiopoulos, Ioannis Mitliagkas, Alexandros Dimakis, Constantine Caramanis

Universal Matrix Completion

Srinadh Bhojanapalli, Prateek Jain

Estimating Latent-Variable Graphical Models using Moments and Likelihoods

Arun Tejasvi Chaganty, Percy Liang

A Clockwork RNN

Jan Koutnik, Klaus Greff, Faustino Gomez, Juergen Schmidhuber

Stochastic Variational Inference for Bayesian Time Series Models

Matthew Johnson, Alan Willsky

Learning from Contagion (Without Timestamps)

Kareem Amin, Hoda Heidari, Michael Kearns

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

Hua Wang, Feiping Nie, Heng Huang

Saddle Points and Accelerated Perceptron Algorithms

Adams Wei Yu, Fatma Kilinc-Karzan, Jaime Carbonell

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

Cícero dos Santos, Bianca Zadrozny

Beta Diffusion Trees

Creighton Heaukulani, David Knowles, Zoubin Ghahramani

Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors

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

Neural Variational Inference and Learning in Belief Networks

Andriy Mnih, Karol Gregor

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

Diederik Kingma, Max Welling

Multi-period Trading Prediction Markets with Connections to Machine Learning

Jinli Hu, Amos Storkey

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

Alex Graves, Navdeep Jaitly

Learning the Irreducible Representations of Commutative Lie Groups

Taco Cohen, Max Welling

Learning Ordered Representations with Nested Dropout

Oren Rippel, Michael Gelbart, Ryan Adams

Active Learning of Parameterized Skills

Bruno da Silva, George Konidaris, Andrew Barto

Nonnegative Sparse PCA with Provable Guarantees

Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros Dimakis

Learning by Stretching Deep Networks

Gaurav Pandey, Ambedkar Dukkipati

Spectral Regularization for Max-Margin Sequence Tagging

Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson

Asynchronous Distributed ADMM for Consensus Optimization

Ruiliang Zhang, James Kwok

A Deep Semi-NMF Model for Learning Hidden Representations

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern Schuller

Stochastic Gradient Hamiltonian Monte Carlo

Tianqi Chen, Emily Fox, Carlos Guestrin

Input Warping for Bayesian Optimization of Non-Stationary Functions

Jasper Snoek, Kevin Swersky, Rich Zemel, Ryan Adams

Kernel Adaptive Metropolis-Hastings

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

Scalable and Robust Bayesian Inference via the Median Posterior

Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David Dunson

Structured Recurrent Temporal Restricted Boltzmann Machines

Roni Mittelman, Benjamin Kuipers, Silvio Savarese, Honglak Lee

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

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

Learnability of the Superset Label Learning Problem

Liping Liu, Thomas Dietterich

Multiresolution Matrix Factorization

Risi Kondor, Nedelina Teneva, Vikas Garg

On learning to localize objects with minimal supervision

Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaïd Harchaoui

Gaussian Approximation of Collective Graphical Models

Liping Liu, Daniel Sheldon, Thomas Dietterich

Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm

Jacob Steinhardt, Percy Liang

A Bayesian Framework for Online Classifier Ensemble

Qinxun Bai, Henry Lam, Stan Sclaroff

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

Philip Thomas

Weighted Graph Clustering with Non-Uniform Uncertainties

Yudong Chen, Shiau Hong Lim, Huan Xu

Online Stochastic Optimization under Correlated Bandit Feedback

Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill

Dynamic Programming Boosting for Discriminative Macro-Action Discovery

Leonidas Lefakis, Francois Fleuret

Riemannian Pursuit for Big Matrix Recovery

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

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

Sven Kurras, Ulrike Luxburg, Gilles Blanchard

Pursuit-Evasion Without Regret, with an Application to Trading

Lili Dworkin, Michael Kearns, Yuriy Nevmyvaka

Probabilistic Matrix Factorization with Non-random Missing Data

José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani

Programming by Feedback

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

Fast Multi-stage Submodular Maximization

Kai Wei, Rishabh Iyer, Jeff Bilmes

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations

David Barber, Yali Wang

Marginalized Denoising Auto-encoders for Nonlinear Representations

Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua Bengio

Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians

Christopher Tosh, Sanjoy Dasgupta

Skip Context Tree Switching

Marc Bellemare, Joel Veness, Erik Talvitie

Probabilistic Partial Canonical Correlation Analysis

Yusuke Mukuta, Harada

Learning Modular Structures from Network Data and Node Variables

Elham Azizi, Edoardo Airoldi, James Galagan

Learning to Disentangle Factors of Variation with Manifold Interaction

Scott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee

A Kernel Independence Test for Random Processes

Kacper Chwialkowski, Arthur Gretton

Discovering Latent Network Structure in Point Process Data

Scott Linderman, Ryan Adams

Variational Inference for Sequential Distance Dependent Chinese Restaurant Process

Sergey Bartunov, Dmitry Vetrov

Effective Bayesian Modeling of Groups of Related Count Time Series

Nicolas Chapados

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

Borja Balle, William Hamilton, Joelle Pineau

Transductive Learning with Multi-class Volume Approximation

Gang Niu, Bo Dai, Christoffel Plessis, Masashi Sugiyama

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

Yujia Li, Rich Zemel

Randomized Nonlinear Component Analysis

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

Time-Regularized Interrupting Options (TRIO)

Timothy Mann, Daniel Mankowitz, Shie Mannor

Memory and Computation Efficient PCA via Very Sparse Random Projections

Farhad Pourkamali Anaraki, Shannon Hughes

Sample Efficient Reinforcement Learning with Gaussian Processes

Robert Grande, Thomas Walsh, Jonathan How

Stable and Efficient Representation Learning with Nonnegativity Constraints

Tsung-Han Lin, H. T. Kung

Approximate Policy Iteration Schemes: A Comparison

Bruno Scherrer

Active Transfer Learning under Model Shift

Xuezhi Wang, Tzu-kuo Huang, Jeff Schneider

Robust and Efficient Kernel Hyperparameter Paths with Guarantees

Joachim Giesen, Soeren Laue, Patrick Wieschollek

One Practical Algorithm for Both Stochastic and Adversarial Bandits

Yevgeny Seldin, Aleksandrs Slivkins

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

Learning Latent Variable Gaussian Graphical Models

Zhaoshi Meng, Brian Eriksson, Al Hero

Inferning with High Girth Graphical Models

Uri Heinemann, Amir Globerson

A Convergence Rate Analysis for LogitBoost, MART and Their Variant

peng sun, Tong Zhang, Jie Zhou

Deep AutoRegressive Networks

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

Discrete Chebyshev Classifiers

Elad Eban, Elad Mezuman, Amir Globerson

Learning the Parameters of Determinantal Point Process Kernels

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

Affinity Weighted Embedding

Jason Weston, Ron Weiss, Hector Yee

Online Multi-Task Learning for Policy Gradient Methods

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

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

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

Distributed Representations of Sentences and Documents

Quoc Le, Tomas Mikolov

Deep Boosting

Corinna Cortes, Mehryar Mohri, Umar Syed

Dual Query: Practical Private Query Release for High Dimensional Data

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

Nonlinear Information-Theoretic Compressive Measurement Design

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

Preserving Modes and Messages via Diverse Particle Selection

Jason Pacheco, Silvia Zuffi, Michael Black, Erik Sudderth

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

Simone Romano, James Bailey, Vinh Nguyen, Karin Verspoor

Ensemble Methods for Structured Prediction

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri

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

Aaron Defazio, Justin Domke, Caetano