Papers
Online Prediction with Selfish Experts
Tim Roughgarden, Okke Schrijvers
Online Reinforcement Learning in Stochastic Games
Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
On Optimal Generalizability in Parametric Learning
Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour et al.
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Xingguo Li, Lin Yang, Jason Ge et al.
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep K Ravikumar
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin, Francis Bach, Simon Lacoste-Julien
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi
On the Complexity of Learning Neural Networks
Le Song, Santosh Vempala, John Wilmes et al.
On the Consistency of Quick Shift
Heinrich Jiang
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Arturs Backurs, Piotr Indyk, Ludwig Schmidt
On-the-fly Operation Batching in Dynamic Computation Graphs
Graham Neubig, Yoav Goldberg, Chris Dyer
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Iku Ohama, Issei Sato, Takuya Kida et al.
On the Optimization Landscape of Tensor Decompositions
Rong Ge, Tengyu Ma
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Simon S Du, Yining Wang, Aarti Singh
Optimal Sample Complexity of M-wise Data for Top-K Ranking
Minje Jang, Sunghyun Kim, Changho Suh et al.
Optimal Shrinkage of Singular Values Under Random Data Contamination
Danny Barash, Matan Gavish
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal, Randy Jia
Optimized Pre-Processing for Discrimination Prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri et al.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun et al.
Parallel Streaming Wasserstein Barycenters
Matthew Staib, Sebastian Claici, Justin M Solomon et al.
Parameter-Free Online Learning via Model Selection
Dylan J Foster, Satyen Kale, Mehryar Mohri et al.
Parametric Simplex Method for Sparse Learning
Haotian Pang, Han Liu, Robert J Vanderbei et al.
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan Huggins, Ryan P. Adams, Tamara Broderick