ICML 2014

310 papers

Latent Confusion Analysis by Normalized Gamma Construction

Issei Sato, Hisashi Kashima, Hiroshi Nakagawa

Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data

Naiyan Wang, Dit-Yan Yeung

Outlier Path: A Homotopy Algorithm for Robust SVM

Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi

A Physics-Based Model Prior for Object-Oriented MDPs

Jonathan Scholz, Martin Levihn, Charles Isbell, David Wingate

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

Bilal Ahmed, Thomas Thesen, Karen Blackmon, Yijun Zhao, Orrin Devinsky

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

Róbert Busa-Fekete, Eyke Huellermeier, Balázs Szörényi

Optimal Mean Robust Principal Component Analysis

Feiping Nie, Jianjun Yuan, Heng Huang

Nearest Neighbors Using Compact Sparse Codes

Anoop Cherian

Distributed Stochastic Gradient MCMC

Sungjin Ahn, Babak Shahbaba, Max Welling

Min-Max Problems on Factor Graphs

Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner

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

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

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

David Gleich, Michael Mahoney

Concept Drift Detection Through Resampling

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

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

Ohad Shamir, Nati Srebro, Tong Zhang

A PAC-Bayesian bound for Lifelong Learning

Anastasia Pentina, Christoph Lampert

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

Issei Sato, Hiroshi Nakagawa

Hierarchical Dirichlet Scaling Process

Dongwoo Kim, Alice Oh

Making Fisher Discriminant Analysis Scalable

Bojun Tu, Zhihua Zhang, Shusen Wang, Hui Qian

Multiple Testing under Dependence via Semiparametric Graphical Models

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page

Circulant Binary Embedding

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

Bayesian Optimization with Inequality Constraints

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

Robust Inverse Covariance Estimation under Noisy Measurements

Jun-Kun Wang, Shou-De Lin

Nonparametric Estimation of Renyi Divergence and Friends

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry Wasserman

Sparse meta-Gaussian information bottleneck

Melani Rey, Volker Roth, Thomas Fuchs

Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications

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

Maximum Margin Multiclass Nearest Neighbors

Aryeh Kontorovich, Roi Weiss

Hard-Margin Active Linear Regression

Elad Hazan, Zohar Karnin

Joint Inference of Multiple Label Types in Large Networks

Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus Macskassy

Large-margin Weakly Supervised Dimensionality Reduction

Chang Xu, Dacheng Tao, Chao Xu, Yong Rui

Reducing Dueling Bandits to Cardinal Bandits

Nir Ailon, Zohar Karnin, Thorsten Joachims

Local Ordinal Embedding

Yoshikazu Terada, Ulrike Luxburg

Composite Quantization for Approximate Nearest Neighbor Search

Ting Zhang, Chao Du, Jingdong Wang

Learning Complex Neural Network Policies with Trajectory Optimization

Sergey Levine, Vladlen Koltun

Efficient Algorithms for Robust One-bit Compressive Sensing

Lijun Zhang, Jinfeng Yi, Rong Jin

Putting MRFs on a Tensor Train

Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov

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

Ling Yan, Wu-jun Li, Gui-rong Xue, Dingyi Han

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

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

Efficient Label Propagation

Yasuhiro Fujiwara, Go Irie

Multivariate Maximal Correlation Analysis

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

Cold-start Active Learning with Robust Ordinal Matrix Factorization

Neil Houlsby, José Miguel Hernández-Lobato, Zoubin Ghahramani

Online Clustering of Bandits

Claudio Gentile, Shuai Li, Giovanni Zappella

A Unifying View of Representer Theorems

Andreas Argyriou, Francesco Dinuzzo

Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes

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

The Falling Factorial Basis and Its Statistical Applications

Yu-Xiang Wang, Alex Smola, Ryan Tibshirani

Learning Mixtures of Linear Classifiers

Yuekai Sun, Stratis Ioannidis, Andrea Montanari

K-means recovers ICA filters when independent components are sparse

Alon Vinnikov, Shai Shalev-shwartz

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

Zhiyuan Chen, Bing Liu

Global graph kernels using geometric embeddings

Fredrik Johansson, Vinay Jethava, Devdatt Dubhashi, Chiranjib Bhattacharyya

Fast Computation of Wasserstein Barycenters

Marco Cuturi, Arnaud Doucet

Coding for Random Projections

Ping Li, Michael Mitzenmacher, Anshumali Shrivastava

Statistical analysis of stochastic gradient methods for generalized linear models

Panagiotis Toulis, Edoardo Airoldi, Jason Rennie

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

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

Structured Generative Models of Natural Source Code

Chris Maddison, Daniel Tarlow

Nonparametric Estimation of Multi-View Latent Variable Models

Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

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

Junfeng Wen, Chun-Nam Yu, Russell Greiner

Stochastic Neighbor Compression

Matt Kusner, Stephen Tyree, Kilian Weinberger, Kunal Agrawal

Alternating Minimization for Mixed Linear Regression

Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

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

Jascha Sohl-dickstein, Ben Poole, Surya Ganguli

Multimodal Neural Language Models

Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel

Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting

Oscar Beijbom, Mohammad Saberian, David Kriegman, Nuno Vasconcelos

On Robustness and Regularization of Structural Support Vector Machines

Mohamad Ali Torkamani, Daniel Lowd

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

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

Model-Based Relational RL When Object Existence is Partially Observable

Ngo Ahn Vien, Marc Toussaint

Local algorithms for interactive clustering

Pranjal Awasthi, Maria Balcan, Konstantin Voevodski

Least Squares Revisited: Scalable Approaches for Multi-class Prediction

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

Latent Semantic Representation Learning for Scene Classification

Xin Li, Yuhong Guo

Scaling SVM and Least Absolute Deviations via Exact Data Reduction

Jie Wang, Peter Wonka, Jieping Ye

Memory (and Time) Efficient Sequential Monte Carlo

Seong-hwan Jun, Alexandre Bouchard-côté

Linear Time Solver for Primal SVM

Feiping Nie, Yizhen Huang, Heng Huang

Linear Programming for Large-Scale Markov Decision Problems

Alan Malek, Yasin Abbasi-Yadkori, Peter Bartlett

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

Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun

Consistency of Causal Inference under the Additive Noise Model

Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf

An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

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

Optimization Equivalence of Divergences Improves Neighbor Embedding

Zhirong Yang, Jaakko Peltonen, Samuel Kaski

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

Gavin Taylor, Connor Geer, David Piekut

Structured Prediction of Network Response

Hongyu Su, Aristides Gionis, Juho Rousu

Gaussian Process Classification and Active Learning with Multiple Annotators

Filipe Rodrigues, Francisco Pereira, Bernardete Ribeiro

Sparse Reinforcement Learning via Convex Optimization

Zhiwei Qin, Weichang Li, Firdaus Janoos

Bayesian Max-margin Multi-Task Learning with Data Augmentation

Chengtao Li, Jun Zhu, Jianfei Chen

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

Yuan Fang, Kevin Chang, Hady Lauw

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments

Eunho Yang, Aurélie Lozano, Pradeep Ravikumar

Elementary Estimators for High-Dimensional Linear Regression

Eunho Yang, Aurélie Lozano, Pradeep Ravikumar

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices

José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani

Two-Stage Metric Learning

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

Rectangular Tiling Process

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

Hierarchical Quasi-Clustering Methods for Asymmetric Networks

Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

Anomaly Ranking as Supervised Bipartite Ranking

Stéphan Clémençon, Sylvain Robbiano

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications

Sébastien Bratières, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani

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

Zijia Lin, guiguang ding, Mingqing Hu, Jianmin Wang

PAC-inspired Option Discovery in Lifelong Reinforcement Learning

Emma Brunskill, Lihong Li

Signal recovery from Pooling Representations

Joan Bruna Estrach, arthur szlam, Yann LeCun

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

Safe Screening with Variational Inequalities and Its Application to Lasso

Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye

Clustering in the Presence of Background Noise

Shai Ben-David, Nika Haghtalab

Exchangeable Variable Models

Mathias Niepert, Pedro Domingos

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy

Dengyong Zhou, Qiang Liu, John Platt, Christopher Meek

Gaussian Process Optimization with Mutual Information

Emile Contal, Vianney Perchet, Nicolas Vayatis

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

Yudong Chen, Jiaming Xu

A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models

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

Deep Generative Stochastic Networks Trainable by Backprop

Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski