1 citations · 1 across the 12 of their papers we have counts for
4 papers · 2 filters
Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
Eric Shen, Tony Wu, Mahbod Majid +1
We study the task of learning the structure of a -sparse Gaussian graphical model on variables from a single trajectory of Glauber dynamics. Beyond algorithmic consideration…
Learning Under Graphical Models
Gautam Chandrasekaran, Jason Gaitonde, Ankur Moitra +1
In a landmark result, Linial, Mansour and Nisan (J. ACM 1993) gave a quasipolynomial-time algorithm for learning constant-depth circuits given labeled i.i.d. samples under the unif…
Steering diffusion models with quadratic rewards: a fine-grained analysis
Ankur Moitra, Andrej Risteski, Dhruv Rohatgi
Inference-time algorithms are an emerging paradigm in which pre-trained models are used as subroutines to solve downstream tasks. Such algorithms have been proposed for tasks rangi…
Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
Ishaq Aden-Ali, Noah Golowich, Allen Liu +3
Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop te…