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05/18/2023
Estimation Beyond Data Reweighting: Kernel Method of Moments
Moment restrictions and their conditional counterparts emerge in many ar...
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04/27/2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models
This paper provides answers to an open problem: given a nonlinear data-d...
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07/11/2022
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Important problems in causal inference, economics, and, more generally, ...
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06/24/2021
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics
Random features is a powerful universal function approximator that inher...
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03/29/2021
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions
Trajectory optimization and model predictive control are essential techn...
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02/16/2021
From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing
We study the function approximation aspect of distributionally robust op...
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06/12/2020
Kernel Distributionally Robust Optimization
This paper is an in-depth investigation of using kernel methods to immun...
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03/31/2020
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
In order to anticipate rare and impactful events, we propose to quantify...
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01/28/2020
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
We apply kernel mean embedding methods to sample-based stochastic optimi...
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11/25/2019
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
This work presents the concept of kernel mean embedding and kernel proba...
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11/20/2019
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control
Today's fast linear algebra and numerical optimization tools have pushed...
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07/10/2019
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning
Trajectory optimization (TO) is one of the most powerful tools for gener...
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06/19/2019
Control What You Can: Intrinsically Motivated Task-Planning Agent
We present a novel intrinsically motivated agent that learns how to cont...
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06/09/2019
Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning
Trajectory optimization (TO) is one of the most powerful tools for gener...
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09/13/2018
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance contr...
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02/25/2017
