4 citations · 5 across the 6 of their papers we have counts for
6 papers
Stronger Neyman Regret Guarantees for Adaptive Experimental Design
Georgy Noarov, Riccardo Fogliato, Martin Bertran +1
We study the design of adaptive, sequential experiments for unbiased average treatment effect (ATE) estimation in the design-based potential outcomes setting. Our goal is to develo…
Improving LLM Group Fairness on Tabular Data via In-Context Learning
Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar +4
Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instr…
Order of Magnitude Speedups for LLM Membership Inference
Rongting Zhang, Martin Bertran, Aaron Roth
Large Language Models (LLMs) have the promise to revolutionize computing broadly, but their complexity and extensive training data also expose significant privacy vulnerabilities.…
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
Martin Bertran, Shuai Tang, Michael Kearns +3
Machine unlearning is motivated by desire for data autonomy: a person can request to have their data's influence removed from deployed models, and those models should be updated as…
Multicalibration for Confidence Scoring in LLMs
Gianluca Detommaso, Martin Bertran, Riccardo Fogliato +1
This paper proposes the use of "multicalibration" to yield interpretable and reliable confidence scores for outputs generated by large language models (LLMs). Multicalibration asks…
Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models
Nitish Srivastava, Walter Talbott, Martin Bertran Lopez +2
Modeling the world can benefit robot learning by providing a rich training signal for shaping an agent's latent state space. However, learning world models in unconstrained environ…