research
∙
05/22/2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leible...
share
research
∙
09/15/2022
Langevin Autoencoders for Learning Deep Latent Variable Models
Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for...
research
∙
10/15/2021
Equivariant and Invariant Reynolds Networks
Invariant and equivariant networks are useful in learning data with symm...
research
∙
02/17/2021
Group Equivariant Conditional Neural Processes
We present the group equivariant conditional neural process (EquivCNP), ...
research
∙
12/27/2020
Universal Approximation Theorem for Equivariant Maps by Group CNNs
Group symmetry is inherent in a wide variety of data distributions. Data...
research
∙
06/02/2018
Variable Selection for Nonparametric Learning with Power Series Kernels
In this paper, we propose a variable selection method for general nonpar...
research
∙
11/21/2017
Regret Analysis for Continuous Dueling Bandit
The dueling bandit is a learning framework wherein the feedback informat...
research
∙
10/27/2016
