ICML 2014
309 papers
A Bayesian Wilcoxon Signed-Rank Test Based on the Dirichlet Process
Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon, Fabrizio Ruggeri A Deep and Tractable Density Estimator
Benigno Uria, Iain Murray, Hugo Larochelle A Deep Semi-NMF Model for Learning Hidden Representations
George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern Schuller A Physics-Based Model Prior for Object-Oriented MDPs
Jonathan Scholz, Martin Levihn, Charles Isbell, David Wingate Active Detection via Adaptive Submodularity
Yuxin Chen, Hiroaki Shioi, Cesar Fuentes Montesinos, Lian Pin Koh, Serge Wich, Andreas Krause Active Learning of Parameterized Skills
Bruno Da Silva, George Konidaris, Andrew Barto Active Transfer Learning Under Model Shift
Xuezhi Wang, Tzu-Kuo Huang, Jeff Schneider Adaptive Monte Carlo via Bandit Allocation
James Neufeld, Andras Gyorgy, Csaba Szepesvari, Dale Schuurmans Affinity Weighted Embedding
Jason Weston, Ron Weiss, Hector Yee Agnostic Bayesian Learning of Ensembles
Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle An Asynchronous Parallel Stochastic Coordinate Descent Algorithm
Ji Liu, Steve Wright, Christopher Re, Victor Bittorf, Srikrishna Sridhar Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
Tien Vu Nguyen, Dinh Phung, Xuanlong Nguyen, Swetha Venkatesh, Hung Bui Bayesian Optimization with Inequality Constraints
Jacob Gardner, Matt Kusner, Zhixiang, Kilian Weinberger, John Cunningham Beta Diffusion Trees
Creighton Heaukulani, David Knowles, Zoubin Ghahramani Circulant Binary Embedding
Felix Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang Coding for Random Projections
Ping Li, Michael Mitzenmacher, Anshumali Shrivastava Coherent Matrix Completion
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward Compact Random Feature Maps
Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste Concept Drift Detection Through Resampling
Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer Consistency of Causal Inference Under the Additive Noise Model
Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf Convex Total Least Squares
Dmitry Malioutov, Nikolai Slavov DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell Deep AutoRegressive Networks
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra Deep Boosting
Corinna Cortes, Mehryar Mohri, Umar Syed Deep Generative Stochastic Networks Trainable by Backprop
Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski Demystifying Information-Theoretic Clustering
Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo Deterministic Policy Gradient Algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller Discrete Chebyshev Classifiers
Elad Eban, Elad Mezuman, Amir Globerson Distributed Stochastic Gradient MCMC
Sungjin Ahn, Babak Shahbaba, Max Welling Dual Query: Practical Private Query Release for High Dimensional Data
Marco Gaboardi, Emilio Jesus Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu Efficient Continuous-Time Markov Chain Estimation
Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté Efficient Label Propagation
Yasuhiro Fujiwara, Go Irie Ensemble Methods for Structured Prediction
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri Exchangeable Variable Models
Mathias Niepert, Pedro Domingos Finding Dense Subgraphs via Low-Rank Bilinear Optimization
Dimitris Papailiopoulos, Ioannis Mitliagkas, Alexandros Dimakis, Constantine Caramanis Global Graph Kernels Using Geometric Embeddings
Fredrik Johansson, Vinay Jethava, Devdatt Dubhashi, Chiranjib Bhattacharyya Guess-Averse Loss Functions for Cost-Sensitive Multiclass Boosting
Oscar Beijbom, Mohammad Saberian, David Kriegman, Nuno Vasconcelos Hamiltonian Monte Carlo Without Detailed Balance
Jascha Sohl-Dickstein, Mayur Mudigonda, Michael DeWeese Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra Joint Inference of Multiple Label Types in Large Networks
Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus Macskassy Kernel Adaptive Metropolis-Hastings
Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton Kernel Mean Estimation and Stein Effect
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schoelkopf Large-Scale Multi-Label Learning with Missing Labels
Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit Dhillon Latent Bandits.
Odalric-Ambrym Maillard, Shie Mannor Learning Graphs with a Few Hubs
Rashish Tandon, Pradeep Ravikumar Learning Mixtures of Linear Classifiers
Yuekai Sun, Stratis Ioannidis, Andrea Montanari Learning Polynomials with Neural Networks
Alexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang Least Squares Revisited: Scalable Approaches for Multi-Class Prediction
Alekh Agarwal, Sham Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant Linear Time Solver for Primal SVM
Feiping Nie, Yizhen Huang, Heng Huang Local Algorithms for Interactive Clustering
Pranjal Awasthi, Maria Balcan, Konstantin Voevodski Local Ordinal Embedding
Yoshikazu Terada, Ulrike Luxburg Making Fisher Discriminant Analysis Scalable
Bojun Tu, Zhihua Zhang, Shusen Wang, Hui Qian Memory Efficient Kernel Approximation
Si Si, Cho-Jui Hsieh, Inderjit Dhillon Min-Max Problems on Factor Graphs
Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel Multiresolution Matrix Factorization
Risi Kondor, Nedelina Teneva, Vikas Garg Multivariate Maximal Correlation Analysis
Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm Near-Optimally Teaching the Crowd to Classify
Adish Singla, Ilija Bogunovic, Gabor Bartok, Amin Karbasi, Andreas Krause Nonlinear Information-Theoretic Compressive Measurement Design
Liming Wang, Abolfazl Razi, Miguel Rodrigues, Robert Calderbank, Lawrence Carin Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes
Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli Nonnegative Sparse PCA with Provable Guarantees
Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros Dimakis Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman On Learning to Localize Objects with Minimal Supervision
Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell On Modelling Non-Linear Topical Dependencies
Zhixing Li, Siqiang Wen, Juanzi Li, Peng Zhang, Jie Tang Online Clustering of Bandits
Claudio Gentile, Shuai Li, Giovanni Zappella Online Multi-Task Learning for Policy Gradient Methods
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, Matthew Taylor Outlier Path: A Homotopy Algorithm for Robust SVM
Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi Programming by Feedback
Marc Schoenauer, Riad Akrour, Michele Sebag, Jean-Christophe Souplet Putting MRFs on a Tensor Train
Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov Randomized Nonlinear Component Analysis
David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schoelkopf Rank-One Matrix Pursuit for Matrix Completion
Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye Rectangular Tiling Process
Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda Riemannian Pursuit for Big Matrix Recovery
Mingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan Robust Principal Component Analysis with Complex Noise
Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Lei Zhang Saddle Points and Accelerated Perceptron Algorithms
Adams Wei Yu, Fatma Kilinc-Karzan, Jaime Carbonell Sample-Based Approximate Regularization
Philip Bachman, Amir-Massoud Farahmand, Doina Precup Scalable and Robust Bayesian Inference via the Median Posterior
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David Dunson Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors
Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David Dunson, Lawrence Carin Signal Recovery from Pooling Representations
Joan Bruna Estrach, Arthur Szlam, Yann LeCun Skip Context Tree Switching
Marc Bellemare, Joel Veness, Erik Talvitie Spectral Bandits for Smooth Graph Functions
Michal Valko, Remi Munos, Branislav Kveton, Tomáš Kocák Spectral Regularization for Max-Margin Sequence Tagging
Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson Stochastic Neighbor Compression
Matt Kusner, Stephen Tyree, Kilian Weinberger, Kunal Agrawal 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, Robert Schapire Time-Regularized Interrupting Options (TRIO)
Timothy Mann, Daniel Mankowitz, Shie Mannor Tracking Adversarial Targets
Yasin Abbasi-Yadkori, Peter Bartlett, Varun Kanade True Online TD(lambda)
Harm Seijen, Rich Sutton Two-Stage Metric Learning
Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis Universal Matrix Completion
Srinadh Bhojanapalli, Prateek Jain Wasserstein Propagation for Semi-Supervised Learning
Justin Solomon, Raif Rustamov, Leonidas Guibas, Adrian Butscher