4 papers
Verify to Amplify: Improving Reasoning via Learned Chain-of-Thought Verification
Maria-Florina Balcan, Avrim Blum, Kiriaki Fragkia +2
Large Language Models (LLMs) using chain-of-thought have demonstrated great potential for solving complex reasoning and planning tasks. Despite these advances, LLM-generated output…
Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problem
Avrim Blum, Marten Garicano, Kavya Ravichandran +1
The improving multi-armed bandits problem is a formal model for allocating effort under uncertainty, motivated by scenarios such as investing research effort into new technologies,…
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…
Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees
Ally Yalei Du, Eric Huang, Dravyansh Sharma
Graph-based semi-supervised learning is a powerful paradigm in machine learning for modeling and exploiting the underlying graph structure that captures the relationship between la…