165 citations · 950 across the 71 of their papers we have counts for
3 papers · 2 filters
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
The key assumption underlying linear Markov Decision Processes (MDPs) is that the learner has access to a known feature map that maps state-action pairs to -dimensiona…
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
Ainesh Bakshi, Allen Liu, Ankur Moitra +1
Recently Chen and Poor initiated the study of learning mixtures of linear dynamical systems. While linear dynamical systems already have wide-ranging applications in modeling time-…
Provable benefits of score matching
Chirag Pabbaraju, Dhruv Rohatgi, Anish Sevekari +3
Score matching is an alternative to maximum likelihood (ML) for estimating a probability distribution parametrized up to a constant of proportionality. By fitting the ''score'' of…