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09/21/2023
Smooth Nash Equilibria: Algorithms and Complexity
A fundamental shortcoming of the concept of Nash equilibrium is its comp...
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07/22/2023
The Sample Complexity of Multi-Distribution Learning for VC Classes
Multi-distribution learning is a natural generalization of PAC learning ...
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07/05/2023
Jailbroken: How Does LLM Safety Training Fail?
Large language models trained for safety and harmlessness remain suscept...
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06/26/2023
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
As the scale of machine learning models increases, trends such as scalin...
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06/05/2023
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
In this paper, we introduce a generalization of the standard Stackelberg...
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03/08/2023
Smoothed Analysis of Sequential Probability Assignment
We initiate the study of smoothed analysis for the sequential probabilit...
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02/21/2023
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning
We provide a unifying framework for the design and analysis of multi-cal...
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02/20/2023
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
In online marketplaces, customers have access to hundreds of reviews for...
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02/06/2023
Stochastic Minimum Vertex Cover in General Graphs: a 3/2-Approximation
Our main result is designing an algorithm that returns a vertex cover of...
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10/22/2022
On-Demand Sampling: Learning Optimally from Multiple Distributions
Social and real-world considerations such as robustness, fairness, socia...
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08/30/2022
Competition, Alignment, and Equilibria in Digital Marketplaces
Competition between traditional platforms is known to improve user utili...
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08/19/2022
Learning in Stackelberg Games with Non-myopic Agents
We study Stackelberg games where a principal repeatedly interacts with a...
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05/26/2022
Communicating with Anecdotes
We study a communication game between a sender and receiver where the se...
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02/17/2022
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries
In this paper, we study oracle-efficient algorithms for beyond worst-cas...
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03/04/2021
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
In recent years, federated learning has been embraced as an approach for...
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02/16/2021
Smoothed Analysis with Adaptive Adversaries
We prove novel algorithmic guarantees for several online problems in the...
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11/03/2020
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
Motivated by applications such as college admission and insurance rate d...
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10/10/2020
Noise in Classification
This chapter considers the computational and statistical aspects of lear...
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06/17/2020
Smoothed Analysis of Online and Differentially Private Learning
Practical and pervasive needs for robustness and privacy in algorithms h...
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10/04/2019
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
The long-term impact of algorithmic decision making is shaped by the dyn...
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06/06/2019
Toward a Characterization of Loss Functions for Distribution Learning
In this work we study loss functions for learning and evaluating probabi...
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10/11/2017
The Provable Virtue of Laziness in Motion Planning
The Lazy Shortest Path (LazySP) class consists of motion-planning algori...
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03/14/2017
Weighted Voting Via No-Regret Learning
Voting systems typically treat all voters equally. We argue that perhaps...
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11/04/2016
