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04/08/2023
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Optimal control is notoriously difficult for stochastic nonlinear system...
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01/10/2023
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Sliced Wasserstein (SW) distance suffers from redundant projections due ...
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12/17/2022
Latent Variable Representation for Reinforcement Learning
Deep latent variable models have achieved significant empirical successe...
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10/11/2022
Robustify Transformers with Robust Kernel Density Estimation
Recent advances in Transformer architecture have empowered its empirical...
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08/19/2022
Spectral Decomposition Representation for Reinforcement Learning
Representation learning often plays a critical role in reinforcement lea...
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07/14/2022
Making Linear MDPs Practical via Contrastive Representation Learning
It is common to address the curse of dimensionality in Markov decision p...
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05/27/2022
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
We propose a novel approach to the problem of clustering hierarchically ...
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05/23/2022
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures
The Expectation-Maximization (EM) algorithm has been predominantly used ...
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05/16/2022
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models
Using gradient descent (GD) with fixed or decaying step-size is standard...
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03/13/2022
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model
In high-stake scenarios like medical treatment and auto-piloting, it's r...
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02/09/2022
Improving Computational Complexity in Statistical Models with Second-Order Information
It is known that when the statistical models are singular, i.e., the Fis...
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11/22/2021
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning
Representation learning lies at the heart of the empirical success of de...
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10/15/2021
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
We study the statistical and computational complexities of the Polyak st...
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06/16/2021
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Recent years have witnessed an upsurge of interest in employing flexible...
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06/02/2021
Unsupervised Out-of-Domain Detection via Pre-trained Transformers
Deployed real-world machine learning applications are often subject to u...
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03/25/2021
Nearly Horizon-Free Offline Reinforcement Learning
We revisit offline reinforcement learning on episodic time-homogeneous t...
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03/03/2021
Combinatorial Bandits without Total Order for Arms
We consider the combinatorial bandits problem, where at each time step, ...
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03/03/2021
Linear Bandit Algorithms with Sublinear Time Complexity
We propose to accelerate existing linear bandit algorithms to achieve pe...
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08/15/2020
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
We consider off-policy evaluation (OPE), which evaluates the performance...
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02/21/2020
Stein Self-Repulsive Dynamics: Benefits From Past Samples
We propose a new Stein self-repulsive dynamics for obtaining diversified...
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02/20/2020
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
We propose MaxUp, an embarrassingly simple, highly effective technique f...
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11/18/2019
Implicit Regularization of Normalization Methods
Normalization methods such as batch normalization are commonly used in o...
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02/26/2019
Function Space Particle Optimization for Bayesian Neural Networks
While Bayesian neural networks (BNNs) have drawn increasing attention, t...
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01/27/2019
Reward Shaping via Meta-Learning
Reward shaping is one of the most effective methods to tackle the crucia...
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10/10/2018
Lazy-CFR: a fast regret minimization algorithm for extensive games with imperfect information
In this paper, we focus on solving two-player zero-sum extensive games w...
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12/06/2017
