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Found 8 papers, 0 papers with code
Data-Driven Goal Recognition Design for General Behavioral Agents
Following existing literature, we use worst-case distinctiveness ($\textit{wcd}$) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment.
Performative Prediction with Bandit Feedback: Learning through Reparameterization
Specifically, we develop a {\em reparameterization} framework that reparametrizes the performative prediction objective as a function of the induced data distribution.
Optimal Query Complexity of Secure Stochastic Convex Optimization
The goal of the learner is to optimize the accuracy, i. e., obtaining an accurate estimate of the optimal point, while securing her privacy, i. e., making it difficult for the adversary to infer the optimal point.
Competitive Information Design for Pandora's Box
We study a natural competitive-information-design variant for the Pandora's Box problem (Weitzman 1979), where each box is associated with a strategic information sender who can design what information about the box's prize value to be revealed to the agent when the agent inspects the box.
Computer Science and Game Theory
Bandit Learning with Delayed Impact of Actions
In this paper, we formulate this delayed and long-term impact of actions within the context of multi-armed bandits.
Eliciting Categorical Data for Optimal Aggregation
Our model generalizes both categories and enables the joint exploration of optimal elicitation and aggregation.
Low-Cost Learning via Active Data Procurement
Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint $B$, we give robust risk (predictive error) bounds on the order of $1/\sqrt{B}$.
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks.
