Yongxin Chen
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Machine learning conference papers

  • Toward effective protection against diffusion based mimicry through score distillation
    H. Xue, C. Liang, X. Wu, and Y. Chen
    12th International Conference on Learning Representations, 2024

  • Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
    H. Xue, A. Araujo, B. Hu, and Y. Chen
    2023 Conference on Neural Information Processing Systems, 2023.

  • Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models
    U. Mishra, S. Xue, Y. Chen, and D. Xu
    2023 Conference on Robot Learning, 2023

  • Improved dimension dependence of a proximal algorithm for sampling
    J. Fan, B. Yuan, and Y. Chen
    The 36th Annual Conference on Learning Theory, 2023.

  • On a Class of Gibbs Sampling over Networks
    J. Fan, B. Yuan, J. Liang, A. Wibisono, and Y. Chen
    The 36th Annual Conference on Learning Theory, 2023.

  • Fast Sampling of Diffusion Models with Exponential Integrator
    Q. Zhang, and Y. Chen
    Eleventh International Conference on Learning Representations, 2023.

  • gDDIM: Generalized Denoising Diffusion Implicit Models
    Q. Zhang, M. Tao, and Y. Chen
    Eleventh International Conference on Learning Representations (Spotlight), 2023.

  • DiffCollage: Parallel Generation of Large Content with Diffusion Models
    Q. Zhang, J. Song, X. Huang, Y. Chen, and M. Liu
    Conference on Computer Vision and Pattern Recognition, 2023.

  • 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
    Thirty-ninth International Conference on Machine Learning, Baltimore, MD, 2022.

  • 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

  • Data-driven optimal control under safety constraints using sparse Koopman approximation
    H. Yu, J. Moyalan, U. Vaidya, and Y. Chen
    International Conference on Robotics and Automation, 2022.

  • 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.