ICML 2014

310 papers

Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing

Yuan Zhou, Xi Chen, Jian Li

Pitfalls in the use of Parallel Inference for the Dirichlet Process

Yarin Gal, Zoubin Ghahramani

Linear and Parallel Learning of Markov Random Fields

Yariv Mizrahi, Misha Denil, Nando de Freitas

Marginal Structured SVM with Hidden Variables

Wei Ping, Qiang Liu, Alex Ihler

Scaling Up Robust MDPs using Function Approximation

Aviv Tamar, Shie Mannor, Huan Xu

Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques

Jeremie Mary, Philippe Preux, Olivier Nicol

On the convergence of no-regret learning in selfish routing

Walid Krichene, Benjamin Drighès, Alexandre Bayen

Near-Optimally Teaching the Crowd to Classify

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

Geodesic Distance Function Learning via Heat Flow on Vector Fields

Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye

A Unified Framework for Consistency of Regularized Loss Minimizers

Jean Honorio, Tommi Jaakkola

Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization

Xiaotong Yuan, Ping Li, Tong Zhang

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

Pratik Jawanpuria, Manik Varma, Saketha Nath

Convex Total Least Squares

Dmitry Malioutov, Nikolai Slavov

Near-Optimal Joint Object Matching via Convex Relaxation

Yuxin Chen, Leonidas Guibas, Qixing Huang

Rank-One Matrix Pursuit for Matrix Completion

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

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

Sanmay Das, Allen Lavoie

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Cun Mu, Bo Huang, John Wright, Donald Goldfarb

Scalable Semidefinite Relaxation for Maximum A Posterior Estimation

Qixing Huang, Yuxin Chen, Leonidas Guibas

Robust Principal Component Analysis with Complex Noise

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

Spectral Bandits for Smooth Graph Functions

Michal Valko, Rémi Munos, Branislav Kveton, Tomáš Kocák

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

Daniel Hsu, Sivan Sabato

Concentration in unbounded metric spaces and algorithmic stability

Aryeh Kontorovich

Compact Random Feature Maps

Raffay Hamid, Ying Xiao, Alex Gittens, Dennis DeCoste

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

Masrour Zoghi, Shimon Whiteson, Rémi Munos, Maarten Rijke

An Information Geometry of Statistical Manifold Learning

Ke Sun, Stephane Marchand-Maillet

An Efficient Approach for Assessing Hyperparameter Importance

Frank Hutter, Holger Hoos, Kevin Leyton-brown

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

Jian Zhou, Olga Troyanskaya

Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers

Taiji Suzuki

Filtering with Abstract Particles

Jacob Steinhardt, Percy Liang

Hamiltonian Monte Carlo Without Detailed Balance

Jascha Sohl-dickstein, Mayur Mudigonda, Michael DeWeese

Learning Sum-Product Networks with Direct and Indirect Variable Interactions

Amirmohammad Rooshenas, Daniel Lowd

Memory Efficient Kernel Approximation

Si Si, Cho-jui Hsieh, Inderjit Dhillon

True Online TD(lambda)

Harm Seijen, Rich Sutton

Admixture of Poisson MRFs: A Topic Model with Word Dependencies

David Inouye, Pradeep Ravikumar, Inderjit Dhillon

Coherent Matrix Completion

Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward

Narrowing the Gap: Random Forests In Theory and In Practice

Misha Denil, David Matheson, Nando de Freitas

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

Dani Yogatama, Noah Smith

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang

Efficient Continuous-Time Markov Chain Estimation

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

Spherical Hamiltonian Monte Carlo for Constrained Target Distributions

Shiwei Lan, Bo Zhou, Babak Shahbaba

Towards an optimal stochastic alternating direction method of multipliers

Samaneh Azadi, Suvrit Sra

Agnostic Bayesian Learning of Ensembles

Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle

Learning Graphs with a Few Hubs

Rashish Tandon, Pradeep Ravikumar

Large-scale Multi-label Learning with Missing Labels

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

Provable Bounds for Learning Some Deep Representations

Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma

Nuclear Norm Minimization via Active Subspace Selection

Cho-jui Hsieh, Peder Olsen

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

Cho-jui Hsieh, Si Si, Inderjit Dhillon

Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search

Anshumali Shrivastava, Ping Li

Coordinate-descent for learning orthogonal matrices through Givens rotations

Uri Shalit, Gal Chechik

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

Azadeh Khaleghi, Daniil Ryabko

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

Arun Iyer, Saketha Nath, Sunita Sarawagi

Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms

Richard Combes, Alexandre Proutiere

Online Learning in Markov Decision Processes with Changing Cost Sequences

Travis Dick, András György, Csaba Szepesvári

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

Ji Liu, Jieping Ye, Ryohei Fujimaki

Discriminative Features via Generalized Eigenvectors

Nikos Karampatziakis, Paul Mineiro

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael Mahoney

(Near) Dimension Independent Risk Bounds for Differentially Private Learning

Prateek Jain, Abhradeep Guha Thakurta

A Deep and Tractable Density Estimator

Benigno Uria, Iain Murray, Hugo Larochelle

On Modelling Non-linear Topical Dependencies

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

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning

François Denis, Mattias Gybels, Amaury Habrard

Bias in Natural Actor-Critic Algorithms

Philip Thomas

On Measure Concentration of Random Maximum A-Posteriori Perturbations

Francesco Orabona, Tamir Hazan, Anand Sarwate, Tommi Jaakkola

Condensed Filter Tree for Cost-Sensitive Multi-Label Classification

Chun-Liang Li, Hsuan-tien Lin

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

Ferdinando Cicalese, Eduardo Laber, Aline Medeiros Saettler

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

Rémi Bardenet, Arnaud Doucet, Chris Holmes

Wenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin

Deterministic Policy Gradient Algorithms

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

Online Bayesian Passive-Aggressive Learning

Tianlin Shi, Jun Zhu

Tracking Adversarial Targets

Yasin Abbasi-Yadkori, Peter Bartlett, Varun Kanade

Computing Parametric Ranking Models via Rank-Breaking

Hossein Azari Soufiani, David Parkes, Lirong Xia

Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm

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

Boosting with Online Binary Learners for the Multiclass Bandit Problem

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

Generalized Exponential Concentration Inequality for Renyi Divergence Estimation

Shashank Singh, Barnabás Póczos

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

Yong Liu, Shali Jiang, Shizhong Liao

Max-Margin Infinite Hidden Markov Models

Aonan Zhang, Jun Zhu, Bo Zhang

Wasserstein Propagation for Semi-Supervised Learning

Justin Solomon, Raif Rustamov, Leonidas Guibas, Adrian Butscher

Large-Margin Metric Learning for Constrained Partitioning Problems

Rémi Lajugie, Francis Bach, Sylvain Arlot

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

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

Prediction with Limited Advice and Multiarmed Bandits with Paid Observations

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

Low-density Parity Constraints for Hashing-Based Discrete Integration

Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

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

Mehryar Mohri, Andres Munoz Medina

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

Shike Mei, Jun Zhu, Jerry Zhu

Margins, Kernels and Non-linear Smoothed Perceptrons

Aaditya Ramdas, Javier Pena

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

Andrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry

Towards Minimax Online Learning with Unknown Time Horizon

Haipeng Luo, Robert Schapire

Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data

Benjamin Letham, Wei Sun, Anshul Sheopuri

A Consistent Histogram Estimator for Exchangeable Graph Models

Stanley Chan, Edoardo Airoldi

The Inverse Regression Topic Model

Maxim Rabinovich, David Blei

Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis

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

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

Anoop Korattikara, Yutian Chen, Max Welling

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

Fabian Gieseke, Justin Heinermann, Cosmin Oancea, Christian Igel

Convergence rates for persistence diagram estimation in Topological Data Analysis

Frederic Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel

Von Mises-Fisher Clustering Models

Siddharth Gopal, Yiming Yang

Fast Allocation of Gaussian Process Experts

Trung Nguyen, Edwin Bonilla

Latent Bandits.

Odalric-ambrym Maillard, Shie Mannor

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

Timothy Mann, Shie Mannor

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

Arun Rajkumar, Shivani Agarwal

Boosting multi-step autoregressive forecasts

Souhaib Ben Taieb, Rob Hyndman

Thompson Sampling for Complex Online Problems

Aditya Gopalan, Shie Mannor, Yishay Mansour

A Statistical Perspective on Algorithmic Leveraging

Ping Ma, Michael Mahoney, Bin Yu