Scalable Gradients and Variational Inference for Stochastic Differential Equations. - Xuechen Li 005; Ting-Kam Leonard Wong; Ricky T. Q. Chen; David Duvenaud (AABI, 2019)

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

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

Residual Flows for Invertible Generative Modeling. - Ricky T. Q. Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 2019)

Latent ODEs for Irregularly-Sampled Time Series. - Yulia Rubanova; Ricky T. Q. Chen; David Duvenaud (CoRR, 2019)

Neural Networks with Cheap Differential Operators. - Ricky T. Q. Chen; David Duvenaud (CoRR, 2019)

Scalable Gradients for Stochastic Differential Equations. - Xuechen Li 005; Ting-Kam Leonard Wong; Ricky T. Q. Chen; David Duvenaud (AISTATS, 2020)

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. - Ricky T. Q. Chen; Dami Choi; Lukas Balles; David Duvenaud; Philipp Hennig (ICBINB@NeurIPS, 2020)

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. - Yucen Luo; Alex Beatson; Mohammad Norouzi 002; Jun Zhu 001; David Duvenaud; Ryan P. Adams; Ricky T. Q. Chen (ICLR, 2020)

"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code. - Patrick Kidger; Ricky T. Q. Chen; Terry J. Lyons (CoRR, 2020)

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, 2021)

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

"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms. - Patrick Kidger; Ricky T. Q. Chen; Terry J. Lyons (ICML, 2021)

Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. - Winnie Xu; Ricky T. Q. Chen; Xuechen Li 005; David Duvenaud (AISTATS, 2022)

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

Semi-Discrete Normalizing Flows through Differentiable 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 S. Wang; Stuart Anderson; Jing Dong 002; Brandon Amos; et al. (NeurIPS, 2022)

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

Unifying Generative Models with GFlowNets. - Dinghuai Zhang; Ricky T. Q. Chen; Nikolay Malkin; Yoshua Bengio (CoRR, 2022)

Latent Discretization for Continuous-time Sequence Compression. - Ricky T. Q. Chen; Matthew Le 001; Matthew J. Muckley; Maximilian Nickel; Karen Ullrich (CoRR, 2022)

Flow Matching for Generative Modeling. - Yaron Lipman; Ricky T. Q. Chen; Heli Ben-Hamu; Maximilian Nickel; Matthew Le 001 (ICLR, 2023)

Latent State Marginalization as a Low-cost Approach for Improving Exploration. - Dinghuai Zhang; Aaron C. Courville; Yoshua Bengio; Qinqing Zheng; Amy Zhang 001; Ricky T. Q. Chen (ICLR, 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 (ICML, 2023)

On Kinetic Optimal Probability Paths for Generative Models. - Neta Shaul; Ricky T. Q. Chen; Maximilian Nickel; Matthew Le 001; Yaron Lipman (ICML, 2023)

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

Riemannian Flow Matching on General Geometries. - Ricky T. Q. Chen; Yaron Lipman (CoRR, 2023)

Training-free Linear Image Inversion via Flows. - Ashwini Pokle; Matthew J. Muckley; Ricky T. Q. Chen; Brian Karrer (CoRR, 2023)

Guided Flows for Generative Modeling and Decision Making. - Qinqing Zheng; Matt Le 001; Neta Shaul; Yaron Lipman; Aditya Grover; Ricky T. Q. Chen (CoRR, 2023)

Flow Matching on General Geometries. - Ricky T. Q. Chen; Yaron Lipman (ICLR, 2024)

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

Bespoke Solvers for Generative Flow Models. - Neta Shaul; Juan C. Pérez; Ricky T. Q. Chen; Ali K. 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 C. Courville; Yoshua Bengio (ICLR, 2024)

Variational Schrödinger Diffusion Models. - Wei Deng 002; Weijian Luo; Yixin Tan; Marin Bilos; Yu Chen; Yuriy Nevmyvaka; Ricky T. Q. Chen (ICML, 2024)

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

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models. - Neta Shaul; Uriel Singer; Ricky T. Q. Chen; Matthew Le 001; Ali K. Thabet; Albert Pumarola; Yaron Lipman (ICML, 2024)

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

Discrete Flow Matching. - Itai Gat; Tal Remez; Neta Shaul; Felix Kreuk; Ricky T. Q. Chen; Gabriel Synnaeve; Yossi Adi; 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 (NeurIPS, 2024)

Reflected Schrödinger Bridge for Constrained Generative Modeling. - Wei Deng 002; Yu Chen; Nicole Tianjiao Yang; Hengrong Du; Qi Feng 005; Ricky Tian Qi Chen (UAI, 2024)

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

Flow Matching Guide and Code. - Yaron Lipman; Marton Havasi; Peter Holderrieth; Neta Shaul; Matt Le 001; Brian Karrer; Ricky T. Q. Chen; David Lopez-Paz; Heli Ben-Hamu; Itai Gat (CoRR, 2024)

Leveraging normalizing flows for orbital-free density functional theory. - Alexandre de Camargo; Ricky T. Q. Chen; Rodrigo A. Vargas-Hernández (Mach. Learn. Sci. Technol., 2024)

Training-free linear image inverses via flows. - Ashwini Pokle; Matthew J. Muckley; Ricky T. Q. Chen; Brian Karrer (Trans. Mach. Learn. Res., 2024)

Distributional GFlowNets with Quantile Flows. - Dinghuai Zhang; Ling Pan; Ricky T. Q. Chen; Aaron C. Courville; Yoshua Bengio (Trans. Mach. Learn. Res., 2024)

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 (ICLR, 2025)

Generator Matching: Generative modeling with arbitrary Markov processes. - Peter Holderrieth; Marton Havasi; Jason Yim; Neta Shaul; Itai Gat; Tommi S. Jaakkola; Brian Karrer; Ricky T. Q. Chen; Yaron Lipman (ICLR, 2025)

Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective. - Neta Shaul; Itai Gat; Marton Havasi; Daniel Severo 001; 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 001; Ricky T. Q. Chen; Wei-Ning Hsu; Timo Gerkmann; Alexander Richard; Yi-Chiao Wu (ICLR, 2025)

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

Preference Discerning with LLM-Enhanced Generative Retrieval. - Fabian Paischer; Liu Yang 001; Linfeng Liu; Shuai Shao; Kaveh Hassani; Jiacheng Li; Ricky T. Q. Chen; Zhang Gabriel Li; Xiaoli Gao; Wei Shao; Xue Feng; Nima Noorshams; et al. (Trans. Mach. Learn. Res., 2025)

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching. - 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 005; Guan-Horng Liu; et al. (CoRR, 2025)

Edit Flows: Flow Matching with Edit Operations. - Marton Havasi; Brian Karrer; Itai Gat; Ricky T. Q. Chen (CoRR, 2025)

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