7 papers · 1 filter
Rankwidth of Graphs with Balanced Separations: Expansion for Dense Graphs
Emile Anand
We prove that every graph of rankwidth at least contains an induced subgraph whose minimum balanced cutrank is at least , which implies a vertex subset where every balance…
Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown
Emile Anand, Sarah Liaw
Thompson Sampling (TS) is widely used to address the exploration/exploitation tradeoff in contextual bandits, yet recent theory shows that it does not explore aggressively enough i…
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
Emile Anand, Ishani Karmarkar, Guannan Qu
Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially…
Pseudorandomness of the Sticky Random Walk
Emile Anand, Chris Umans
We extend the pseudorandomness of random walks on expander graphs using the sticky random walk. Building on prior works, it was recently shown that expander random walks can fool a…
Identifying Chemicals Through Dimensionality Reduction
Emile Anand, Charles Steinhardt, Martin Hansen
Civilizations have tried to make drinking water safe to consume for thousands of years. The process of determining water contaminants has evolved with the complexity of the contami…
Peer-to-Peer Learning Dynamics of Wide Neural Networks
Shreyas Chaudhari, Srinivasa Pranav, Emile Anand +1
Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving man…