Chen, Ricky T. Q.

51 publications

ICLR 2025 Adjoint Matching: Fine-Tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen

ICML 2025 Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Daniel S. Levine, Brandon M Wood, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen

ICLRW 2025 Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Daniel S. Levine, Brandon M Wood, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen

NeurIPS 2025 Adjoint Schrödinger Bridge Sampler Guan-Horng Liu, Jaemoo Choi, Yongxin Chen, Benjamin Kurt Miller, Ricky T. Q. Chen

NeurIPS 2025 Edit Flows: Variable Length Discrete Flow Matching with Sequence-Level Edit Operations Marton Havasi, Brian Karrer, Itai Gat, Ricky T. Q. Chen

ICLR 2025 Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen

ICLR 2025 FlowDec: A Flow-Based Full-Band General Audio Codec with High Perceptual Quality Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu

ICLR 2025 Generator Matching: Generative Modeling with Arbitrary Markov Processes Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman

TMLR 2025 Preference Discerning with LLM-Enhanced Generative Retrieval Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky T. Q. Chen, Zhang Gabriel Li, Xiaoli Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh

UAI 2025 Simulation-Free Differential Dynamics Through Neural Conservation Laws Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen

ICML 2024 Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali Thabet, Albert Pumarola, Yaron Lipman

ICLR 2024 Bespoke Solvers for Generative Flow Models Neta Shaul, Juan Perez, Ricky T. Q. Chen, Ali Thabet, Albert Pumarola, Yaron Lipman

ICLR 2024 Diffusion Generative Flow Samplers: Improving Learning Signals Through Partial Trajectory Optimization Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio

NeurIPS 2024 Discrete Flow Matching Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman

TMLR 2024 Distributional GFlowNets with Quantile Flows Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

ICLR 2024 Flow Matching on General Geometries Ricky T. Q. Chen, Yaron Lipman

NeurIPS 2024 FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood

ICML 2024 FlowMM: Generating Materials with Riemannian Flow Matching Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M Wood

ICLR 2024 Generalized Schrödinger Bridge Matching Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos Theodorou, Ricky T. Q. Chen

UAI 2024 Neural Optimal Transport with Lagrangian Costs Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos

NeurIPS 2024 Stochastic Optimal Control Matching Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen

TMLR 2024 Training-Free Linear Image Inverses via Flows Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer

ICML 2024 Variational Schrödinger Diffusion Models Wei Deng, Weijian Luo, Yixin Tan, Marin Biloš, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen

ICLR 2023 Flow Matching for Generative Modeling Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le

ICLR 2023 Latent State Marginalization as a Low-Cost Approach for Improving Exploration Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

ICML 2023 Multisample Flow Matching: Straightening Flows with Minibatch Couplings Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen

ICMLW 2023 Neural Optimal Transport with Lagrangian Costs Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos

ICMLW 2023 On Convergence of Approximate Schr\"odinger Bridge with Bounded Cost Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky T. Q. Chen

ICML 2023 On Kinetic Optimal Probability Paths for Generative Models Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman

NeurIPS 2023 TaskMet: Task-Driven Metric Learning for Model Learning Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos

ICMLW 2023 TaskMet: Task-Driven Metric Learning for Model Learning Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos

AISTATS 2022 Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud

ICML 2022 Matching Normalizing Flows and Probability Paths on Manifolds Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximillian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman

NeurIPS 2022 Neural Conservation Laws: A Divergence-Free Perspective Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen

NeurIPS 2022 Semi-Discrete Normalizing Flows Through Differentiable Tessellation Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

ICLRW 2022 Semi-Discrete Normalizing Flows Through Differentiable Voronoi Tessellation Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

NeurIPS 2022 Theseus: A Library for Differentiable Nonlinear Optimization Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam

ICML 2021 "Hey, That’s Not an ODE": Faster ODE Adjoints via Seminorms Patrick Kidger, Ricky T. Q. Chen, Terry J Lyons

ICLR 2021 Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville

ICLR 2021 Learning Neural Event Functions for Ordinary Differential Equations Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

ICLR 2021 Neural Spatio-Temporal Point Processes Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

ICLR 2020 SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

AISTATS 2020 Scalable Gradients for Stochastic Differential Equations Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

NeurIPSW 2020 Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

ICLR 2019 FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

ICML 2019 Invertible Residual Networks Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen

NeurIPS 2019 Latent Ordinary Differential Equations for Irregularly-Sampled Time Series Yulia Rubanova, Ricky T. Q. Chen, David K. Duvenaud

NeurIPS 2019 Neural Networks with Cheap Differential Operators Ricky T. Q. Chen, David K. Duvenaud

NeurIPS 2019 Residual Flows for Invertible Generative Modeling Ricky T. Q. Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen

NeurIPS 2018 Isolating Sources of Disentanglement in Variational Autoencoders Ricky T. Q. Chen, Xuechen Li, Roger B Grosse, David K. Duvenaud

NeurIPS 2018 Neural Ordinary Differential Equations Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David K. Duvenaud