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Found 17 papers, 8 papers with code
Analysis of Kernel Mirror Prox for Measure Optimization
By choosing a suitable function space as the dual to the non-negative measure cone, we study in a unified framework a class of functional saddle-point optimization problems, which we term the Mixed Functional Nash Equilibrium (MFNE), that underlies several existing machine learning algorithms, such as implicit generative models, distributionally robust optimization (DRO), and Wasserstein barycenters.
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
The Halpern iteration for solving monotone inclusion problems has gained increasing interests in recent years due to its simple form and appealing convergence properties.
Estimation Beyond Data Reweighting: Kernel Method of Moments
We provide a variant of our estimator for conditional moment restrictions and show that it is asymptotically first-order optimal for such problems.
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models
This paper provides answers to an open problem: given a nonlinear data-driven dynamical system model, e. g., kernel conditional mean embedding (CME) and Koopman operator, how can one propagate the ambiguity sets forward for multiple steps?
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Important problems in causal inference, economics, and, more generally, robust machine learning can be expressed as conditional moment restrictions, but estimation becomes challenging as it requires solving a continuum of unconditional moment restrictions.
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control
We provide a functional view of distributional robustness motivated by robust statistics and functional analysis.
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics
Random features is a powerful universal function approximator that inherits the theoretical rigor of kernel methods and can scale up to modern learning tasks.
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid control.
Adversarially Robust Kernel Smoothing
We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization.
Kernel Distributionally Robust Optimization
We prove a theorem that generalizes the classical duality in the mathematical problem of moments.
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 the worst-case risk under distributional ambiguity using a recent development in kernel methods -- the kernel mean embedding.
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
We apply kernel mean embedding methods to sample-based stochastic optimization and control.
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 probabilistic programming in the context of stochastic systems.
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 the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems.
Control What You Can: Intrinsically Motivated Task-Planning Agent
We present a novel intrinsically motivated agent that learns how to control the environment in the fastest possible manner by optimizing learning progress.
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods.
Generative Adversarial Active Learning
We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN).
