Workshops

Convexity Exploiting Newton-Type Optimization for Learning and Control

Distributed and Multiagent Reinforcement Learning

Model misspecification in reinforcement learning

A fresh look at some classical system identification methods

On the statistical complexity of reinforcement learning

From pixels to torques: output feedback for robotics

Learning Control from Minimal Prior Knowledge

Trying to Make Sense of Control from Pixels

Curbing Our Enthusiasm: Constraining Decision Policies Learned from the Past to Ensure Good Futures

Learning Intended Cost Functions: Extracting all the right information from all the right places

Safe and Efficient Exploration in Reinforcement Learning

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Integrating optimization, constraints, and control within deep learning models

Learning-based Model Predictive Control - Towards Safe Learning in Control

Sample-Based Learning Model Predictive Control

Finite-sample System Identification: Optimal Rates and the Role of Regularization

Can We Really Use Machine Learning in Safety Critical Systems?

Beyond Theory of Mind: Learning and Influencing Conventions in Multi-Agent Interactions

Industrial, large-scale model predictive control with deep neural networks

Nonconvex optimization in matrix optimization and distributionally robust optimization

Predict, then smart optimize with stochastic programming

A Unifying Framework for Subspace Identification and Dynamic Mode Decomposition

Polynomial Optimization for Bounding Lipschitz Constants of Deep Networks

Distributionally Robust Optimization and Control using RKHS Embedding

Bregman proximal methods for semidefinite optimization.

Machine Learning for Robotics: Achieving Safety, Performance and Reliability by Combining Models and Data in a Closed-Loop System Architecture