Papers
Learning invariant features by harnessing the aperture problem
Roland Memisevic, Georgios Exarchakis
Guaranteed Sparse Recovery under Linear Transformation
Ji Liu, Lei Yuan, Jieping Ye
MILEAGE: Multiple Instance LEArning with Global Embedding
Dan Zhang, Jingrui He, Luo Si et al.
Markov Network Estimation From Multi-attribute Data
Mladen Kolar, Han Liu, Eric Xing
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing
Xi Chen, Qihang Lin, Dengyong Zhou
Bayesian Games for Adversarial Regression Problems
Michael Großhans, Christoph Sawade, Michael Brückner et al.
Multi-Class Classification with Maximum Margin Multiple Kernel
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
Hierarchical Regularization Cascade for Joint Learning
Alon Zweig, Daphna Weinshall
The Sample-Complexity of General Reinforcement Learning
Tor Lattimore, Marcus Hutter, Peter Sunehag
Gossip-based distributed stochastic bandit algorithms
Balazs Szorenyi, Robert Busa-Fekete, Istvan Hegedus et al.
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning
Gang Niu, Wittawat Jitkrittum, Bo Dai et al.
Guided Policy Search
Sergey Levine, Vladlen Koltun
Activized Learning with Uniform Classification Noise
Liu Yang, Steve Hanneke
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Jan-Willem Meent, Jonathan Bronson, Frank Wood et al.
Sparse coding for multitask and transfer learning
Andreas Maurer, Massi Pontil, Bernardino Romera-Paredes
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization
Stefano Ermon, Carla Gomes, Ashish Sabharwal et al.
Large-Scale Learning with Less RAM via Randomization
Daniel Golovin, D. Sculley, Brendan McMahan et al.
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment
Billy Chang, Uwe Kruger, Rafal Kustra et al.
Margins, Shrinkage, and Boosting
Matus Telgarsky
An Adaptive Learning Rate for Stochastic Variational Inference
Rajesh Ranganath, Chong Wang, Blei David et al.
Distributed training of Large-scale Logistic models
Siddharth Gopal, Yiming Yang
A Practical Algorithm for Topic Modeling with Provable Guarantees
Sanjeev Arora, Rong Ge, Yonatan Halpern et al.
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions
Quoc Tran Dinh, Anastasios Kyrillidis, Volkan Cevher
Exploring the Mind: Integrating Questionnaires and fMRI
Esther Salazar, Ryan Bogdan, Adam Gorka et al.