Spring 2023 – SILO

SILO: TBD

SILO: Bridging Linear MDP to Deep RL via Latent Variable Representation

Zhaoran Wang, Northwestern

SILO: Universal Rates of Interactive Learning

Amin Karbasi, Yale

SILO: Looped Transformers as Programmable Computers

Dimitris Tsipras, Stanford

SILO: Controlling the Structure of Inference and Learning in Neural Networks

Yisong Yue, CalTech

SILO: Dyamic network models: one has to do the math

Shankar Bhamidi, UNC

SILO: Benign Overfitting in Two-layer ReLU Networks

Quanquan Gu, UCLA

SILO: Bottom-up manifold learning with low distortion

Gal Mishne, UCSD

NO SILO

SILO: Bagging provides assumption-free stability

Jake Soloff, University of Chicago