We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average $L_2$-distance between the hidden-layer features and theoretically investigate how learning non-redundant distinct features affects the performance of the network. To do so, we derive novel generalization bounds depending on feature diversity based on Rademacher complexity for such networks. Our analysis proves that more distinct features at the network's units within the hidden layer lead to better generalization. We also show how to extend our results to deeper networks and different losses.
Provably Better Explanations with Optimized Aggregation of Feature Attributions
LMU Munich（慕尼黑大学）；Siemens AG（西门子公司）；Technical University of Munich（慕尼黑工业大学）；University of Oxford（牛津大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Goethe University Frankfurt（法兰克福大学）
Stress-Testing Neural Network Verifiers with Provably Robust Instances
Department of Statistics & Data Science, University of California, Los Angeles（统计与数据科学系，加州大学洛杉矶分校）；Department of Mathematics, University of California, Los Angeles（数学系，加州大学洛杉矶分校）；Max Planck Institute for Mathematics in the Sciences, Leipzig（科学数学研究所，莱比锡）
DSReg: Provably Recovering Individual World Latents without Reconstruction
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cold Spring Harbor Laboratory（冷泉港实验室）；Brown University（布朗大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ELLIS Institute Tübingen（ELLIS图宾根研究所）
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ELLIS Institute Tübingen（图宾根ELLIS研究所）；University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；University of Cambridge（剑桥大学）；Princeton University（普林斯顿大学）
本文从可辨识表征学习角度研究互信息技能学习，证明对比后继特征方法可在线性变换意义下可证明地恢复环境真实特征，并在 MuJoCo 与 DeepMind Control 上验证了该结论。
Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
Department of Mathematics, Indian Institute of Science, Bangalore（印度科学研究所数学系，班加罗尔）；Department of Computing and Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）
