Selected Publications

Dissertation

Journal papers

Machine learning conference papers

  • Path Integral Sampler: a stochastic control approach for sampling
    Q. Zhang, and Y. Chen
    Tenth International Conference on Learning Representations, 2022.

  • On the complexity of the optimal transport problem with graph-structured 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
    Thirty-eighth International Conference on Machine Learning (Long talk), Online, 2021.

  • Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
    Y. Zhang, Q. Cai, Z. Yang, Y. Chen, and Z. Wang
    2020 Conference on Neural Information Processing Systems (ORAL), Vancouver, Canada, 2020.

  • Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
    Z. Fu, Z. Yang, Y. Chen, and Z. Wang
    Ninth International Conference on Learning Representations, Addis Ababa, Ethiopia, 2020.

  • Provably Global Convergence of Actor-Critic: 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 Nonconvex-strongly-concave Min-Max 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 Wasserstein-1 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.