6 papers
Sample-Adaptivity Tradeoff in On-Demand Sampling
Nika Haghtalab, Omar Montasser, Mingda Qiao
We study the tradeoff between sample complexity and round complexity in on-demand sampling, where the learning algorithm adaptively samples from distributions over a limited nu…
Truthfulness of Decision-Theoretic Calibration Measures
Mingda Qiao, Eric Zhao
Calibration measures quantify how much a forecaster's predictions violates calibration, which requires that forecasts are unbiased conditioning on the forecasted probabilities. Two…
Leakage-Robust Bayesian Persuasion
Nika Haghtalab, Mingda Qiao, Kunhe Yang
This paper introduces leakage-robust Bayesian persuasion. Situated between public Bayesian persuasion [KG11] (and its multi-receiver variants [CCG23, Xu20]) and private Bayesian pe…
Truthfulness of Calibration Measures
Nika Haghtalab, Mingda Qiao, Kunhe Yang +1
We initiate the study of the truthfulness of calibration measures in sequential prediction. A calibration measure is said to be truthful if the forecaster (approximately) minimizes…
Platforms for Efficient and Incentive-Aware Collaboration
Nika Haghtalab, Mingda Qiao, Kunhe Yang
Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by…
On the Distance from Calibration in Sequential Prediction
Mingda Qiao, Letian Zheng
We study a sequential binary prediction setting where the forecaster is evaluated in terms of the calibration distance, which is defined as the distance between the predicted…