Selected Publications
Dissertation
Journal papers
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
DiffusionBased 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: LongHorizon 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.
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
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
Datadriven 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 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.
