Papers
A Survey of Preference-Based Reinforcement Learning Methods
Christian Wirth, Riad Akrour, Gerhard Neumann et al.
STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Will Wei Sun, Lexin Li
Stochastic Gradient Descent as Approximate Bayesian Inference
Stephan Mandt, Matthew D. Hoffman, David M. Blei
A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations
Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot et al.
Active-set Methods for Submodular Minimization Problems
K. S. Sesh Kumar, Francis Bach
Stabilized Sparse Online Learning for Sparse Data
Yuting Ma, Tian Zheng
Knowledge Graph Completion via Complex Tensor Factorization
Théo Trouillon, Christopher R. Dance, Éric Gaussier et al.
Gap Safe Screening Rules for Sparsity Enforcing Penalties
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort et al.
Poisson Random Fields for Dynamic Feature Models
Valerio Perrone, Paul A. Jenkins, Dario Spanò et al.
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
Christophe Dupuy, Francis Bach
Statistical and Computational Guarantees for the Baum-Welch Algorithm
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
Robust and Scalable Bayes via a Median of Subset Posterior Measures
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin et al.
Kernel Partial Least Squares for Stationary Data
Marco Singer, Tatyana Krivobokova, Axel Munk
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
Jason D. Lee, Qihang Lin, Tengyu Ma et al.
Classification of Time Sequences using Graphs of Temporal Constraints
Mathieu Guillame-Bert, Artur Dubrawski
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
Ricardo Silva, Shohei Shimizu
Probabilistic Line Searches for Stochastic Optimization
Maren Mahsereci, Philipp Hennig
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Zheng-Chu Guo, Lei Shi, Qiang Wu
Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
Jiahe Lin, George Michailidis
Second-Order Stochastic Optimization for Machine Learning in Linear Time
Naman Agarwal, Brian Bullins, Elad Hazan
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
Shun Zheng, Jialei Wang, Fen Xia et al.
Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
Shannon Fenn, Pablo Moscato
Document Neural Autoregressive Distribution Estimation
Stanislas Lauly, Yin Zheng, Alexandre Allauzen et al.
Efficient Sampling from Time-Varying Log-Concave Distributions
Hariharan Narayanan, Alexer Rakhlin