Optimization
SuperDeepFool: a new fast and accurate minimal adversarial attack
However, existing methods for evaluating this robustness metric are either computationally expensive or not very accurate.
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.
Learning-Augmented Algorithms with Explicit Predictors
Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data.
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
We further discuss the sample complexity for a desired approximation accuracy.
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
We derive the distortion-rate function for this setup as a linear program, and provide an efficient algorithm to compute this fundamental limit via the dual of the linear program.
Soft-Label Integration for Robust Toxicity Classification
This paper contains uncensored toxic content that might be offensive. Toxicity classification in textual content remains a significant problem.