4 papers
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…
Towards the Pseudorandomness of Expander Random Walks for Read-Once ACC0 circuits
Emile Anand
Expander graphs are among the most useful combinatorial objects in theoretical computer science. A line of work studies random walks on expander graphs for their pseudorandomness a…
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…