FODSI
Publishes 2 feeds
Ryan O'Donnell Theoretical Computer Science Talks
15 posts · contributor
Learning under complex structure-MIT-2020
10 posts · contributor
Lately
The Quantum Computing Advantage - Prof. Ryan O'Donnell
Aude Genevay (MIT) -- Learning with Sinkhorn divergences: from optimal transport to MMD
Nima Anari (Stanford) - Limited correlations, fractional log-concavity, and fast mixing random walks
Sham Kakade (U. of Washington) -- The provable effectiveness of policy gradient methods in RL
Cynthia Vinzant (NC State) -- Log-concave polynomials, matroids, and expanders
Elina Robeva (U. of British Columbia) -- Learning totally positive distributions
Caroline Uhler (MIT) -- Causal inference through permutation-based algorithms
Andrej Risteski (CMU) -- Fast convergence for Langevin diffusion with matrix manifold structure
Miklos Racz (Princeton) -- Trace reconstruction problems with applications to DNA data storage
Ryan O'Donnell (CMU) -- Learning quantum states
Ryan O'Donnell (CMU) -- Learning quantum states
Vasilis Syrgkanis (Microsoft Research) -- Statistical learning for causal inference
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