Selected Publications
Dissertation
Journal papers
Machine learning conference papers
Improved analysis for a proximal algorithm for sampling
Y. Chen, S. Chewi, A. Salim, and A. Wibisono
The 35th Annual Conference on Learning Theory, London, UK, 2022.
Variational Wasserstein gradient flow
J. Fan, Q. Zhang, A. Taghvaei, and Y. Chen
Thirtyninth International Conference on Machine Learning, Baltimore, MD, 2022.
On the complexity of the optimal transport problem with graphstructured cost
J. Fan, I. Haasler, J. Karlsson, and Y. Chen
25th International Conference on Artificial Intelligence and Statistics, 2022.
Diffusion Normalizing Flow
Q. Zhang, and Y. Chen
2021 Conference on Neural Information Processing Systems, Online, 2021.
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
J. Fan, A. Taghvaei, and Y. Chen
Thirtyeighth International Conference on Machine Learning (Long talk), Online, 2021.
Can TemporalDifference and QLearning Learn Representation? A MeanField Theory
Y. Zhang, Q. Cai, Z. Yang, Y. Chen, and Z. Wang
2020 Conference on Neural Information Processing Systems (ORAL), Vancouver, Canada, 2020.
ActorCritic Provably Finds Nash Equilibria of LinearQuadratic MeanField Games
Z. Fu, Z. Yang, Y. Chen, and Z. Wang
Ninth International Conference on Learning Representations, Addis Ababa, Ethiopia, 2020.
Provably Global Convergence of ActorCritic: A Case for Linear Quadratic Regulator with Ergodic Cost
Z. Yang, Y. Chen, M. Hong, and Z. Wang
2019 Conference on Neural Information Processing Systems, Vancouver, Canada, 2019.
Conference papers
Inference of collective Gaussian hidden Markov models
R. Singh, and Y. Chen
60th IEEE Conference on Decision and Control, Online, 2021.
Improving Robustness via Risk Averse Distributional Reinforcement Learning
R. Singh, Q. Zhang, and Y. Chen
2nd Conference on Learning for Dynamics and Control, Berkeley, CA, 2020.
Alternating Gradient Descent Ascent for Nonconvexstronglyconcave MinMax Optimization
S. Lu, R. Singh, X. Chen, Y. Chen, and M. Hong
53nd Asilomar Conference on Signals, Systems and Computers, Asilomar, USA, 2019.
Estimating Ensemble Flows on a Hidden Markov Chain
I. Haasler, A. Ringh, Y. Chen, and J. Karlsson
58th IEEE Conference on Decision and Control, Nice, France, 2019.
Sample Complexity for Nonlinear Stochastic Dynamics
Y. Chen, and U. Vaidya
2019 American Control Conference, Philadelphia, PA, 2019.
Matricial Wasserstein1 Distance
Y. Chen, T. T. Georgiou, L. Ning, and A. Tannenbaum
56th IEEE Conference on Decision and Control, Melbourne, Australia, 2017.
Brain Parcellation and Connectivity Mapping using Wasserstein Geometry
H. Farooq, Y. Chen, T.T. Georgiou, and C. Lenglet
20th International Conference on Medical Image Computing and Computer Assisted Intervention, 2017.
Steering state statistics with output feedback
Y. Chen, T. T. Georgiou, and M. Pavon
in Proceedings of the 54th IEEE Conference on Decision and Control, Osaka, Japan, 2015.
The role of past and future in estimation and the reversibility of stochastic processes
Y. Chen, J. Karlsson, and T. T. Georgiou
in Proceedings of the 21st International Symposium on Mathematical Theory of Networks and Systems, Groningen, The Netherlands, 2014.
State covariances and the matrix completion problem
Y. Chen, M. R. Jovanovic, and T. T. Georgiou
in Proceedings of the 52nd IEEE Conference on Decision and Control , Florence, Italy, 2013.
