Ricky T. Q. Chen

92 papers A* 30A 4Journal 55Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Discrete Adjoint Matching.
CoRR
Oswin So, Brian Karrer, Chuchu Fan, Ricky T. Q. Chen, Guan-Horng Liu
2025 A* conf
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.
ICLR
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen
2025 A* conf
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching.
ICML
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
2025 J jnl
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching.
CoRR
Aaron J. Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Brandon M. Wood, Daniel LeVine, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen
2025 J jnl
Adjoint Schrödinger Bridge Sampler.
CoRR
Guan-Horng Liu, Jaemoo Choi, Yongxin Chen, Benjamin Kurt Miller, Ricky T. Q. Chen
2025 J jnl
Edit Flows: Flow Matching with Edit Operations.
CoRR
Marton Havasi, Brian Karrer, Itai Gat, Ricky T. Q. Chen
2025 J jnl
Enhancing Diffusion-Based Sampling with Molecular Collective Variables.
CoRR
Juno Nam, Bálint Máté, Artur P. Toshev, Manasa Kaniselvan, Rafael Gómez-Bombarelli, Ricky T. Q. Chen, Brandon M. Wood, Guan-Horng Liu, Benjamin Kurt Miller
2025 A* conf
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective.
ICLR
Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen
2025 A* conf
FlowDec: A flow-based full-band general audio codec with high perceptual quality.
ICLR
Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu
2025 J jnl
FlowDec: A flow-based full-band general audio codec with high perceptual quality.
CoRR
Simon Welker, Matthew Le, Ricky T. Q. Chen, Wei-Ning Hsu, Timo Gerkmann, Alexander Richard, Yi-Chiao Wu
2025 J jnl
Flowception: Temporally Expansive Flow Matching for Video Generation.
CoRR
Tariq Berrada Ifriqi, John Nguyen, Karteek Alahari, Jakob Verbeek, Ricky T. Q. Chen
2025 J jnl
GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models.
CoRR
Peter Holderrieth, Uriel Singer, Tommi S. Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer
2025 A* conf
Generator Matching: Generative modeling with arbitrary Markov processes.
ICLR
Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman
2025 J jnl
OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit Flows.
CoRR
John Nguyen, Marton Havasi, Tariq Berrada, Luke Zettlemoyer, Ricky T. Q. Chen
2025 J jnl
Preference Discerning with LLM-Enhanced Generative Retrieval.
Trans. Mach. Learn. Res.
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
2025 A conf
Simulation-Free Differential Dynamics Through Neural Conservation Laws.
UAI
Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen
2025 J jnl
Simulation-Free Differential Dynamics through Neural Conservation Laws.
CoRR
Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen
2024 J jnl
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.
CoRR
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen
2024 A* conf
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models.
ICML
Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali K. Thabet, Albert Pumarola, Yaron Lipman
2024 J jnl
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models.
CoRR
Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali K. Thabet, Albert Pumarola, Yaron Lipman
2024 A* conf
Bespoke Solvers for Generative Flow Models.
ICLR
Neta Shaul, Juan C. Pérez, Ricky T. Q. Chen, Ali K. Thabet, Albert Pumarola, Yaron Lipman
2024 A* conf
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization.
ICLR
Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio
2024 A* conf
Discrete Flow Matching.
NeurIPS
Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman
2024 J jnl
Discrete Flow Matching.
CoRR
Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman
2024 J jnl
Distributional GFlowNets with Quantile Flows.
Trans. Mach. Learn. Res.
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
2024 J jnl
Flow Matching Guide and Code.
CoRR
Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky T. Q. Chen, David Lopez-Paz, Heli Ben-Hamu, Itai Gat
2024 A* conf
Flow Matching on General Geometries.
ICLR
Ricky T. Q. Chen, Yaron Lipman
2024 J jnl
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective.
CoRR
Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen
2024 A* conf
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions.
NeurIPS
Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood
2024 J jnl
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions.
CoRR
Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood
2024 A* conf
FlowMM: Generating Materials with Riemannian Flow Matching.
ICML
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood
2024 J jnl
FlowMM: Generating Materials with Riemannian Flow Matching.
CoRR
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood
2024 A* conf
Generalized Schrödinger Bridge Matching.
ICLR
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen
2024 J jnl
Generator Matching: Generative modeling with arbitrary Markov processes.
CoRR
Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman
2024 J jnl
Leveraging normalizing flows for orbital-free density functional theory.
Mach. Learn. Sci. Technol.
Alexandre de Camargo, Ricky T. Q. Chen, Rodrigo A. Vargas-Hernández
2024 A conf
Neural Optimal Transport with Lagrangian Costs.
UAI
Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos
2024 J jnl
Neural Optimal Transport with Lagrangian Costs.
CoRR
Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos
2024 J jnl
Reflected Schrödinger Bridge for Constrained Generative Modeling.
CoRR
Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky T. Q. Chen
2024 A* conf
Stochastic Optimal Control Matching.
NeurIPS
Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen
2024 J jnl
Training-free linear image inverses via flows.
Trans. Mach. Learn. Res.
Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer
2024 A* conf
Variational Schrödinger Diffusion Models.
ICML
Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen
2024 J jnl
Variational Schrödinger Diffusion Models.
CoRR
Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen
2023 J jnl
Bespoke Solvers for Generative Flow Models.
CoRR
Neta Shaul, Juan C. Pérez, Ricky T. Q. Chen, Ali K. Thabet, Albert Pumarola, Yaron Lipman
2023 J jnl
Distributional GFlowNets with Quantile Flows.
CoRR
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
2023 A* conf
Flow Matching for Generative Modeling.
ICLR
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le
2023 J jnl
Generalized Schrödinger Bridge Matching.
CoRR
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen
2023 J jnl
Guided Flows for Generative Modeling and Decision Making.
CoRR
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen
2023 A* conf
Latent State Marginalization as a Low-cost Approach for Improving Exploration.
ICLR
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
2023 A* conf
Multisample Flow Matching: Straightening Flows with Minibatch Couplings.
ICML
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen
2023 J jnl
Multisample Flow Matching: Straightening Flows with Minibatch Couplings.
CoRR
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen
2023 A* conf
On Kinetic Optimal Probability Paths for Generative Models.
ICML
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman
2023 J jnl
On Kinetic Optimal Probability Paths for Generative Models.
CoRR
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matt Le, Yaron Lipman
2023 J jnl
Riemannian Flow Matching on General Geometries.
CoRR
Ricky T. Q. Chen, Yaron Lipman
2023 J jnl
Stochastic Optimal Control Matching.
CoRR
Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen
2023 J jnl
TaskMet: Task-Driven Metric Learning for Model Learning.
CoRR
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos
2023 A* conf
TaskMet: Task-driven Metric Learning for Model Learning.
NeurIPS
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos
2023 J jnl
Training-free Linear Image Inversion via Flows.
CoRR
Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer
2022 J jnl
Flow Matching for Generative Modeling.
CoRR
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le
2022 A conf
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations.
AISTATS
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
2022 J jnl
Latent Discretization for Continuous-time Sequence Compression.
CoRR
Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel, Karen Ullrich
2022 J jnl
Latent State Marginalization as a Low-cost Approach for Improving Exploration.
CoRR
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
2022 A* conf
Matching Normalizing Flows and Probability Paths on Manifolds.
ICML
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximilian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman
2022 J jnl
Matching Normalizing Flows and Probability Paths on Manifolds.
CoRR
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, Yaron Lipman
2022 A* conf
Neural Conservation Laws: A Divergence-Free Perspective.
NeurIPS
Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen
2022 J jnl
Neural Conservation Laws: A Divergence-Free Perspective.
CoRR
Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen
2022 A* conf
Semi-Discrete Normalizing Flows through Differentiable Tessellation.
NeurIPS
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
2022 J jnl
Semi-Discrete Normalizing Flows through Differentiable Tessellation.
CoRR
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
2022 A* conf
Theseus: A Library for Differentiable Nonlinear Optimization.
NeurIPS
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
2022 J jnl
Theseus: A Library for Differentiable Nonlinear Optimization.
CoRR
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
2022 J jnl
Unifying Generative Models with GFlowNets.
CoRR
Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio
2021 A* conf
"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms.
ICML
Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons
2021 A* conf
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization.
ICLR
Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron C. Courville
2021 J jnl
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations.
CoRR
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
2021 A* conf
Learning Neural Event Functions for Ordinary Differential Equations.
ICLR
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
2021 A* conf
Neural Spatio-Temporal Point Processes.
ICLR
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
2020 J jnl
"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code.
CoRR
Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons
2020 J jnl
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization.
CoRR
Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron C. Courville
2020 J jnl
Learning Neural Event Functions for Ordinary Differential Equations.
CoRR
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
2020 J jnl
Neural Spatio-Temporal Point Processes.
CoRR
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
2020 A* conf
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models.
ICLR
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
2020 J jnl
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models.
CoRR
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
2020 A conf
Scalable Gradients for Stochastic Differential Equations.
AISTATS
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2020 J jnl
Scalable Gradients for Stochastic Differential Equations.
CoRR
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2020 — conf
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.
ICBINB@NeurIPS
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2020 J jnl
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.
CoRR
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2019 — conf
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models.
ICLR
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
2019 A* conf
Invertible Residual Networks.
ICML
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen
2019 J jnl
Latent ODEs for Irregularly-Sampled Time Series.
CoRR
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
2019 J jnl
Neural Networks with Cheap Differential Operators.
CoRR
Ricky T. Q. Chen, David Duvenaud
2019 J jnl
Residual Flows for Invertible Generative Modeling.
CoRR
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2019 — conf
Scalable Gradients and Variational Inference for Stochastic Differential Equations.
AABI
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2018 J jnl
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.
CoRR
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud