71 citations · 115 across the 9 of their papers we have counts for
11 papers
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…
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…
Learning in Stackelberg Games with Non-myopic Agents
Nika Haghtalab, Thodoris Lykouris, Sloan Nietert +1
We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent, without knowing the agent's payoff function. Although learning in Stackelberg…
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy +2
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even whe…
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
Alexander Wei, Wei Hu, Jacob Steinhardt
Of theories for why large-scale machine learning models generalize despite being vastly overparameterized, which of their assumptions are needed to capture the qualitative phenomen…
Predicting Out-of-Distribution Error with the Projection Norm
Yaodong Yu, Zitong Yang, Alexander Wei +2
We propose a metric -- Projection Norm -- to predict a model's performance on out-of-distribution (OOD) data without access to ground truth labels. Projection Norm first uses model…
Learning Equilibria in Matching Markets from Bandit Feedback
Meena Jagadeesan, Alexander Wei, Yixin Wang +2
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of…
Designing Approximately Optimal Search on Matching Platforms
Nicole Immorlica, Brendan Lucier, Vahideh Manshadi +1
We study the design of a decentralized two-sided matching market in which agents' search is guided by the platform. There are finitely many agent types, each with (potentially rand…
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
Alexander Wei, Fred Zhang
We study the problem of improving the performance of online algorithms by incorporating machine-learned predictions. The goal is to design algorithms that are both consistent and r…
Better and Simpler Learning-Augmented Online Caching
Alexander Wei
Lykouris and Vassilvitskii (ICML 2018) introduce a model of online caching with machine-learned advice, where each page request additionally comes with a prediction of when that pa…
Optimal Las Vegas Approximate Near Neighbors in
Alexander Wei
We show that approximate near neighbor search in high dimensions can be solved in a Las Vegas fashion (i.e., without false negatives) for () while matching th…