4 papers · 1 filter
Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking
Dhruv Rohatgi, Abhishek Shetty, Donya Saless +4
Test-time algorithms that combine the generative power of language models with process verifiers that assess the quality of partial generations offer a promising lever for elicitin…
Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods
Avrim Blum, Daniel Hsu, Cyrus Rashtchian +1
Test-time augmentation, such as Retrieval-Augmented Generation (RAG) or tool use, critically depends on an interplay between a model's parametric knowledge and externally retrieved…
Proofs as Explanations: Short Certificates for Reliable Predictions
Avrim Blum, Steve Hanneke, Chirag Pabbaraju +1
We consider a model for explainable AI in which an explanation for a prediction consists of a subset of the training data (if it exists) such that all classifiers $h'…
PAC Learning with Improvements
Idan Attias, Avrim Blum, Keziah Naggita +3
One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes samples to learn to error (and more, if the…