Foundations of Learning And Intelligent Robots

Foundations of Learning And Intelligent Robots

Inference for large populations with aggregate observations

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Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
International Conference on Machine Learning (ICML) (long talk) 2021 PDF Code
Inference with Aggregate Data: An Optimal Transport Approach
Under review 2020 PDF Code
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Conference on Neural Information Processing Systems (Neurips) (Oral) 2020 PDF
On the matrix Monge–Kantorovich problem
European Journal of Applied Mathematics 2020 PDF
Improving Robustness via Risk Averse Distributional Reinforcement Learning
Learning for Dynamics and Control (L4DC) 2020 PDF
Incremental inference of collective graphical models.
IEEE Control Systems Letters 2020 PDF
Multi-marginal optimal transport and probabilistic graphical models
Under review 2020 PDF Code
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
Ninth International Conference on Learning Representations 2020 PDF
Nonlinear Covariance Control via Differential Dynamic Programming
American Control Conference (ACC) 2020 PDF

More Publications

A fast algorithm for graphical optimal transport

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Yongxin Chen

Assistant Professor, GaTech

Rahul Singh

ML PhD, GaTech

Qinsheng Zhang

Robotics PhD, GaTech

Jiaojiao Fan

ML Ph.D, GaTech

Hongzhe Yu

Robotics Ph.D, GaTech

Full team

In this project, you are challenged to design autonomous robots.

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All open projects