71 citations · 130 across the 47 of their papers we have counts for
9 papers · 1 filter
Smooth Nash Equilibria: Algorithms and Complexity
Constantinos Daskalakis, Noah Golowich, Nika Haghtalab +1
A fundamental shortcoming of the concept of Nash equilibrium is its computational intractability: approximating Nash equilibria in normal-form games is PPAD-hard. In this paper, in…
Delegating Data Collection in Decentralized Machine Learning
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan +1
Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point,…
Jailbroken: How Does LLM Safety Training Fail?
Alexander Wei, Nika Haghtalab, Jacob Steinhardt
Large language models trained for safety and harmlessness remain susceptible to adversarial misuse, as evidenced by the prevalence of "jailbreak" attacks on early releases of ChatG…
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt +1
As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the…
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Nika Haghtalab, Chara Podimata, Kunhe Yang
We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework. In CSGs, a principal repeatedly interacts with an agent…
Smoothed Analysis of Sequential Probability Assignment
Alankrita Bhatt, Nika Haghtalab, Abhishek Shetty
We initiate the study of smoothed analysis for the sequential probability assignment problem with contexts. We study information-theoretically optimal minmax rates as well as a fra…
Stochastic Minimum Vertex Cover in General Graphs: a -Approximation
Mahsa Derakhshan, Naveen Durvasula, Nika Haghtalab
Our main result is designing an algorithm that returns a vertex cover of with size at most times the expected size of the minimum vertex cover, using…
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
Nika Haghtalab, Michael I. Jordan, Eric Zhao
We provide a unifying framework for the design and analysis of multicalibrated predictors. By placing the multicalibration problem in the general setting of multi-objective learnin…
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy +1
In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for…