Papers
Model Selection via the VC Dimension
Merlin Mpoudeu, Bertrand Clarke
An asymptotic analysis of distributed nonparametric methods
Botond Szabó, Harry van Zanten
Streaming Principal Component Analysis From Incomplete Data
Armin Eftekhari, Gregory Ongie, Laura Balzano et al.
Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees
Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi
Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, Sébastien Racanière et al.
On the optimality of the Hedge algorithm in the stochastic regime
Jaouad Mourtada, Stéphane Gaïffas
SMART: An Open Source Data Labeling Platform for Supervised Learning
Rob Chew, Michael Wenger, Caroline Kery et al.
Tight Lower Bounds on the VC-dimension of Geometric Set Systems
Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii
Learning to Match via Inverse Optimal Transport
Ruilin Li, Xiaojing Ye, Haomin Zhou et al.
Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions
Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern
Prediction Risk for the Horseshoe Regression
Anindya Bhadra, Jyotishka Datta, Yunfan Li et al.
Nonuniformity of P-values Can Occur Early in Diverging Dimensions
Yingying Fan, Emre Demirkaya, Jinchi Lv
Generalized Score Matching for Non-Negative Data
Shiqing Yu, Mathias Drton, Ali Shojaie
Fairness Constraints: A Flexible Approach for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez et al.
Deep Optimal Stopping
Sebastian Becker, Patrick Cheridito, Arnulf Jentzen
Analysis of Langevin Monte Carlo via Convex Optimization
Alain Durmus, Szymon Majewski, Błażej Miasojedow
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Can Karakus, Yifan Sun, Suhas Diggavi et al.
Lazifying Conditional Gradient Algorithms
Gábor Braun, Sebastian Pokutta, Daniel Zink
Semi-Analytic Resampling in Lasso
Tomoyuki Obuchi, Yoshiyuki Kabashima
On Consistent Vertex Nomination Schemes
Vince Lyzinski, Keith Levin, Carey E. Priebe
Variance-based Regularization with Convex Objectives
John Duchi, Hongseok Namkoong
Learnability of Solutions to Conjunctive Queries
Hubie Chen, Matthew Valeriote
Proximal Distance Algorithms: Theory and Practice
Kevin L. Keys, Hua Zhou, Kenneth Lange
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang et al.
A Representer Theorem for Deep Kernel Learning
Bastian Bohn, Michael Griebel, Christian Rieger