Search Results for author:
Found 13 papers, 1 papers with code
Optimal Scoring for Dynamic Information Acquisition
A principal seeks to learn about a binary state and can do so by enlisting an agent to acquire information over time using a Poisson information arrival technology.
Managing Persuasion Robustly: The Optimality of Quota Rules
The optimal decision rule is a quota rule, i. e., the decision rule maximizes the receiver's ex-ante payoff subject to the constraint that the marginal distribution over actions adheres to a consistent quota, regardless of the sender's chosen signal structure.
Screening Signal-Manipulating Agents via Contests
We study the design of screening mechanisms subject to competition and manipulation.
Making Auctions Robust to Aftermarkets
We show that when using balanced prices, both these approaches ensure high equilibrium welfare in the combined market.
Selling Data to an Agent with Endogenous Information
We consider a model of a data broker selling information to a single agent to maximize his revenue.
Misspecified Beliefs about Time Lags
We examine the long-term behavior of a Bayesian agent who has a misspecified belief about the time lag between actions and feedback, and learns about the payoff consequences of his actions over time.
Your College Dorm and Dormmates: Fair Resource Sharing with Externalities
Clearly, the strong notion of envy-freeness, where no agent envies another for their resource or mates, cannot always be achieved and we show that even deciding the existence of such a strongly envy-free assignment is an intractable problem.
Computer Science and Game Theory
Equilibrium Behaviors in Repeated Games
We examine a patient player's behavior when he can build reputations in front of a sequence of myopic opponents.
Multinomial Logit Bandit with Low Switching Cost
We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
Stochastic Linear Optimization with Adversarial Corruption
We extend the model of stochastic bandits with adversarial corruption (Lykouriset al., 2018) to the stochastic linear optimization problem (Dani et al., 2008).
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks.
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
We study the linear contextual bandit problem with finite action sets.
Implementation of Stochastic Quasi-Newton's Method in PyTorch
In this paper, we implement the Stochastic Damped LBFGS (SdLBFGS) for stochastic non-convex optimization.
