Papers
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard et al.
WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Tian Lan, Sunil Srinivasa, Huan Wang et al.
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong et al.
When Hardness of Approximation Meets Hardness of Learning
Eran Malach, Shai Shalev-Shwartz
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
Yoav Freund, Yi-An Ma, Tong Zhang
XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng, Yunfan Li, Ivor W. Tsang et al.
A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
Zhao Tang Luo, Huiyan Sang, Bani Mallick
A Bayes-Optimal View on Adversarial Examples
Eitan Richardson, Yair Weiss
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Tian Tong, Cong Ma, Yuejie Chi
Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair et al.
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
Adaptive estimation of nonparametric functionals
Lin Liu, Rajarshi Mukherjee, James M. Robins et al.
A Distributed Method for Fitting Laplacian Regularized Stratified Models
Jonathan Tuck, Shane Barratt, Stephen Boyd
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
Alex Luedtke, Incheoul Chung, Oleg Sofrygin
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
Lei Yang, Jia Li, Defeng Sun et al.
A flexible model-free prediction-based framework for feature ranking
Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong
A General Framework for Adversarial Label Learning
Chidubem Arachie, Bert Huang
A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
Trambak Banerjee, Qiang Liu, Gourab Mukherjee et al.
A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families
Robert Sicks, Ralf Korn, Stefanie Schwaar
A general linear-time inference method for Gaussian Processes on one dimension
Jackson Loper, David Blei, John P. Cunningham et al.
Aggregated Hold-Out
Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle
A Greedy Algorithm for Quantizing Neural Networks
Eric Lybrand, Rayan Saab
Alibi Explain: Algorithms for Explaining Machine Learning Models
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti et al.
A Lyapunov Analysis of Accelerated Methods in Optimization
Ashia C. Wilson, Ben Recht, Michael I. Jordan
An algorithmic view of L2 regularization and some path-following algorithms
Yunzhang Zhu, Renxiong Liu