885 citations · 2.4k across the 41 of their papers we have counts for
45 papers
ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games
Greg d'Eon, Hala Murad, Kevin Leyton-Brown +1
Behavioral game theory models serve two purposes: yielding insights into how human decision-making works, and predicting how people would behave in novel strategic settings. A syst…
Near-Linear MIR Algorithms for Stochastically-Ordered Priors
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
With the rise of online applications, recommender systems (RSs) often encounter constraints in balancing exploration and exploitation. Such constraints arise when exploration is ca…
Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning
Greg d'Eon, Neil Newman, Kevin Leyton-Brown
Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to analyze, making it difficult for bidders to determi…
Utilitarian Algorithm Configuration
Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden
We present the first nontrivial procedure for configuring heuristic algorithms to maximize the utility provided to their end users while also offering theoretical guarantees about…
How to Evaluate Behavioral Models
Greg d'Eon, Sophie Greenwood, Kevin Leyton-Brown +1
Researchers building behavioral models, such as behavioral game theorists, use experimental data to evaluate predictive models of human behavior. However, there is little agreement…
UNSAT Solver Synthesis via Monte Carlo Forest Search
Chris Cameron, Jason Hartford, Taylor Lundy +4
We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversin…