885 citations · 2.4k across the 41 of their papers we have counts for
18 papers · 1 filter
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…
Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates
Natalie Collina, Nicole Immorlica, Kevin Leyton-Brown +2
We study a dynamic non-bipartite matching problem. There is a fixed set of agent types, and agents of a given type arrive and depart according to type-specific Poisson processes. A…
Learning under Invariable Bayesian Safety
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
A recent body of work addresses safety constraints in explore-and-exploit systems. Such constraints arise where, for example, exploration is carried out by individuals whose welfar…
Report-Sensitive Spot-Checking in Peer-Grading Systems
Hedayat Zarkoob, Hu Fu, Kevin Leyton-Brown
Peer grading systems make large courses more scalable, provide students with faster and more detailed feedback, and help students to learn by thinking critically about the work of…
Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user. Such system…