Papers
Network Diffusions via Neural Mean-Field Dynamics
Shushan He, Hongyuan Zha, Xiaojing Ye
Near-Optimal Reinforcement Learning with Self-Play
Yu Bai, Chi Jin, Tiancheng Yu
Statistical-Query Lower Bounds via Functional Gradients
Surbhi Goel, Aravind Gollakota, Adam Klivans
Biologically Inspired Mechanisms for Adversarial Robustness
Manish Reddy Vuyyuru, Andrzej Banburski, Nishka Pant et al.
Differentiable Meta-Learning of Bandit Policies
Craig Boutilier, Chih-wei Hsu, Branislav Kveton et al.
Adversarial Robustness of Supervised Sparse Coding
Jeremias Sulam, Ramchandran Muthukumar, Raman Arora
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Sinho Chewi, Thibaut Le Gouic, Chen Lu et al.
Optimizing Neural Networks via Koopman Operator Theory
Akshunna S. Dogra, William Redman
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
Guannan Qu, Yiheng Lin, Adam Wierman et al.
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
Ervine Zheng, Qi Yu, Rui Li et al.
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
Steffen Czolbe, Oswin Krause, Ingemar Cox et al.
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang et al.
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
Shinji Ito, Shuichi Hirahara, Tasuku Soma et al.
Cooperative Multi-player Bandit Optimization
Ilai Bistritz, Nicholas Bambos
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Tushar Nagarajan, Kristen Grauman
The Power of Predictions in Online Control
Chenkai Yu, Guanya Shi, Soon-Jo Chung et al.
On the equivalence of molecular graph convolution and molecular wave function with poor basis set
Masashi Tsubaki, Teruyasu Mizoguchi
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian Fuchs, Daniel Worrall, Volker Fischer et al.
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr et al.
Projected Stein Variational Gradient Descent
Peng Chen, Omar Ghattas
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Zaheen Ahmad, Levi Lelis, Michael Bowling
MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Tan Nguyen, Richard Baraniuk, Andrea Bertozzi et al.
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
Alex Tseng, Avanti Shrikumar, Anshul Kundaje
Margins are Insufficient for Explaining Gradient Boosting
Allan Grønlund, Lior Kamma, Kasper Green Larsen
Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder et al.