71 citations · 130 across the 47 of their papers we have counts for
8 papers · 1 filter
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…
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Jessica Dai, Nika Haghtalab, Eric Zhao
A canonical desideratum for prediction problems is that performance guarantees should hold not just on average over the population, but also for meaningful subpopulations within th…
Algorithmic Content Selection and the Impact of User Disengagement
Emilio Calvano, Nika Haghtalab, Ellen Vitercik +1
Digital services face a fundamental trade-off in content selection: they must balance the immediate revenue gained from high-reward content against the long-term benefits of mainta…
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata +1
When learning in strategic environments, a key question is whether agents can overcome uncertainty about their preferences to achieve outcomes they could have achieved absent any u…
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…
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
Danny Halawi, Alexander Wei, Eric Wallace +3
Black-box finetuning is an emerging interface for adapting state-of-the-art language models to user needs. However, such access may also let malicious actors undermine model safety…
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…
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Nika Haghtalab +2
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalign…