Papers
The Implicit Bias of Minima Stability: A View from Function Space
Rotem Mulayoff, Tomer Michaeli, Daniel Soudry
The Inductive Bias of Quantum Kernels
Jonas Kübler, Simon Buchholz, Bernhard Schölkopf
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains
Daron Anderson, Douglas Leith
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss, John P. Cunningham
The Limits of Optimal Pricing in the Dark
Quinlan Dawkins, Minbiao Han, Haifeng Xu
The Many Faces of Adversarial Risk
Muni Sreenivas Pydi, Varun Jog
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
Peter Hase, Harry Xie, Mohit Bansal
The Pareto Frontier of model selection for general Contextual Bandits
Teodor Vanislavov Marinov, Julian Zimmert
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
Nathan Grinsztajn, Johan Ferret, Olivier Pietquin et al.
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu
The staircase property: How hierarchical structure can guide deep learning
Emmanuel Abbe, Enric Boix-Adsera, Matthew S Brennan et al.
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
Thibault Sejourne, Francois-Xavier Vialard, Gabriel Peyré
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige et al.
The Value of Information When Deciding What to Learn
Dilip Arumugam, Benjamin Van Roy
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?
Gonzalo Jaimovitch-Lopez, David Castellano Falcón, Cesar Ferri et al.
Three-dimensional spike localization and improved motion correction for Neuropixels recordings
Julien Boussard, Erdem Varol, Hyun Dong Lee et al.
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates
Alp Yurtsever, Alex Gu, Suvrit Sra
Tighter Expected Generalization Error Bounds via Wasserstein Distance
Borja Rodríguez Gálvez, German Bassi, Ragnar Thobaben et al.
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Alain Durmus, Eric Moulines, Alexey Naumov et al.
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
Seohong Park, Jaekyeom Kim, Gunhee Kim
Time-independent Generalization Bounds for SGLD in Non-convex Settings
Tyler Farghly, Patrick Rebeschini
Time-series Generation by Contrastive Imitation
Daniel Jarrett, Ioana Bica, Mihaela van der Schaar