Evanston
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
Neural Information Processing Systems http://nips.cc/
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu
Without careful design, GCNs can fail miserably even with multiple layers and nonlinear activation functions.
Efficient Graph Matching for Correlated Stochastic Block Models
We study learning problems on correlated stochastic block models with two balanced communities.
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes Jerry Y ao-Chieh Hu Dennis Wu
We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories.
Convergent Policy Optimization for Safe Reinforcement Learning
Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang
Given ,J ( )andD ( )arethesample (i.e., atrajectory) . Note J ( ) and D ( ) are randomness J ( )andD ( )todenote anda ClearlyweJ( )= E J ( ) andD( )= E D ( ) .
Provable Gaussian Embedding with One Observation
Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang
Neural Information Processing Systems http://nips.cc/