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cs.LG2024
Model Stealing for Any Low-Rank Language Model
Allen Liu, Ankur Moitra
Model stealing, where a learner tries to recover an unknown model via carefully chosen queries, is a critical problem in machine learning, as it threatens the security of proprieta…
cs.LG2024
Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics
Jason Gaitonde, Ankur Moitra, Elchanan Mossel
We consider the problem of learning graphical models, also known as Markov random fields (MRFs) from temporally correlated samples. As in many traditional statistical settings, fun…
cs.LG2024
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Supervised learning is often computationally easy in practice. But to what extent does this mean that other modes of learning, such as reinforcement learning (RL), ought to be comp…