Ricky T. Q. Chen

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 51

Top Topics

Point Processes

Generative Model

Stochastic Optimization

Stochastic Differential Equations

Top Venues

Publications

Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky T. Q. Chen
Reflected Schrödinger Bridge for Constrained Generative Modeling. CoRR (2024)
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen
Multisample Flow Matching: Straightening Flows with Minibatch Couplings. CoRR (2023)
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen
Multisample Flow Matching: Straightening Flows with Minibatch Couplings. ICML (2023)
Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer
Training-free Linear Image Inversion via Flows. CoRR (2023)
Neta Shaul, Juan C. Pérez, Ricky T. Q. Chen, Ali K. Thabet, Albert Pumarola, Yaron Lipman
Bespoke Solvers for Generative Flow Models. CoRR (2023)
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matt Le, Yaron Lipman
On Kinetic Optimal Probability Paths for Generative Models. CoRR (2023)
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le
Flow Matching for Generative Modeling. ICLR (2023)
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen
Generalized Schrödinger Bridge Matching. CoRR (2023)
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos
TaskMet: Task-Driven Metric Learning for Model Learning. CoRR (2023)
Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen
Stochastic Optimal Control Matching. CoRR (2023)
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos
TaskMet: Task-driven Metric Learning for Model Learning. NeurIPS (2023)
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
Latent State Marginalization as a Low-cost Approach for Improving Exploration. ICLR (2023)
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen
Guided Flows for Generative Modeling and Decision Making. CoRR (2023)
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
Distributional GFlowNets with Quantile Flows. CoRR (2023)
Ricky T. Q. Chen, Yaron Lipman
Riemannian Flow Matching on General Geometries. CoRR (2023)
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman
On Kinetic Optimal Probability Paths for Generative Models. ICML (2023)
Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel, Karen Ullrich
Latent Discretization for Continuous-time Sequence Compression. CoRR (2022)
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, Yaron Lipman
Matching Normalizing Flows and Probability Paths on Manifolds. CoRR (2022)
Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio
Unifying Generative Models with GFlowNets. CoRR (2022)
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximilian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman
Matching Normalizing Flows and Probability Paths on Manifolds. ICML (2022)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Semi-Discrete Normalizing Flows through Differentiable Tessellation. CoRR (2022)
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. AISTATS (2022)
Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen
Neural Conservation Laws: A Divergence-Free Perspective. CoRR (2022)
Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin S. Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam
Theseus: A Library for Differentiable Nonlinear Optimization. NeurIPS (2022)
Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin S. Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam
Theseus: A Library for Differentiable Nonlinear Optimization. CoRR (2022)
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le
Flow Matching for Generative Modeling. CoRR (2022)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Semi-Discrete Normalizing Flows through Differentiable Tessellation. NeurIPS (2022)
Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen
Neural Conservation Laws: A Divergence-Free Perspective. NeurIPS (2022)
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
Latent State Marginalization as a Low-cost Approach for Improving Exploration. CoRR (2022)
Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons
"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms. ICML (2021)
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. CoRR (2021)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Neural Spatio-Temporal Point Processes. ICLR (2021)
Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron C. Courville
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization. ICLR (2021)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Learning Neural Event Functions for Ordinary Differential Equations. ICLR (2021)
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. CoRR (2020)
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. ICLR (2020)
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
Scalable Gradients for Stochastic Differential Equations. AISTATS (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR (2020)
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
Scalable Gradients for Stochastic Differential Equations. CoRR (2020)
Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron C. Courville
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization. CoRR (2020)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Learning Neural Event Functions for Ordinary Differential Equations. CoRR (2020)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Neural Spatio-Temporal Point Processes. CoRR (2020)
Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons
"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code. CoRR (2020)
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen
Invertible Residual Networks. ICML (2019)
Ricky T. Q. Chen, David Duvenaud
Neural Networks with Cheap Differential Operators. CoRR (2019)
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Kristjanson Duvenaud
Scalable Gradients and Variational Inference for Stochastic Differential Equations. AABI (2019)
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
Latent ODEs for Irregularly-Sampled Time Series. CoRR (2019)
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Residual Flows for Invertible Generative Modeling. CoRR (2019)
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR (2019)
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR (2018)