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cs.LG2026
The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives
Ankur Moitra, Andrej Risteski, Dhruv Rohatgi
Inference-time reward alignment asks how to turn a pre-trained diffusion model with base law into a sampler that favors a reward while remaining close to . Since there i…
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…