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ORCIDRicky T. Q. Chen
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2020 – today
- 2024
[j2]Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer:
Training-free linear image inverses via flows. Trans. Mach. Learn. Res. 2024 (2024)- [j1]Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. Trans. Mach. Learn. Res. 2024 (2024) - [c27]Ricky T. Q. Chen, Yaron Lipman:
Flow Matching on General Geometries. ICLR 2024 - [c26]Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen:
Generalized Schrödinger Bridge Matching. ICLR 2024 - [c25]Neta Shaul, Juan C. Pérez, Ricky T. Q. Chen, Ali K. Thabet, Albert Pumarola, Yaron Lipman:
Bespoke Solvers for Generative Flow Models. ICLR 2024 - [c24]Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio:
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. ICLR 2024 - [c23]Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen:
Variational Schrödinger Diffusion Models. ICML 2024 - [c22]Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood:
FlowMM: Generating Materials with Riemannian Flow Matching. ICML 2024 - [c21]Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali K. Thabet, Albert Pumarola, Yaron Lipman:
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models. ICML 2024 - [i38]Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky T. Q. Chen:
Reflected Schrödinger Bridge for Constrained Generative Modeling. CoRR abs/2401.03228 (2024) - [i37]Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali K. Thabet, Albert Pumarola, Yaron Lipman:
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models. CoRR abs/2403.01329 (2024) - [i36]Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen:
Variational Schrödinger Diffusion Models. CoRR abs/2405.04795 (2024) - [i35]Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos:
Neural Optimal Transport with Lagrangian Costs. CoRR abs/2406.00288 (2024) - [i34]Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood:
FlowMM: Generating Materials with Riemannian Flow Matching. CoRR abs/2406.04713 (2024) - [i33]Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman:
Discrete Flow Matching. CoRR abs/2407.15595 (2024) - [i32]Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen:
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control. CoRR abs/2409.08861 (2024) - 2023
- [c20]Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le:
Flow Matching for Generative Modeling. ICLR 2023 - [c19]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 - [c18]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: 28100-28127 - [c17]Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman:
On Kinetic Optimal Probability Paths for Generative Models. ICML 2023: 30883-30907 - [c16]Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos:
TaskMet: Task-driven Metric Learning for Model Learning. NeurIPS 2023 - [i31]Ricky T. Q. Chen, Yaron Lipman:
Riemannian Flow Matching on General Geometries. CoRR abs/2302.03660 (2023) - [i30]Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. CoRR abs/2302.05793 (2023) - [i29]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 abs/2304.14772 (2023) - [i28]Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel
, Matt Le, Yaron Lipman:
On Kinetic Optimal Probability Paths for Generative Models. CoRR abs/2306.06626 (2023) - [i27]Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen:
Generalized Schrödinger Bridge Matching. CoRR abs/2310.02233 (2023) - [i26]Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio:
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. CoRR abs/2310.02679 (2023) - [i25]Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer:
Training-free Linear Image Inversion via Flows. CoRR abs/2310.04432 (2023) - [i24]Neta Shaul, Juan C. Pérez, Ricky T. Q. Chen, Ali K. Thabet, Albert Pumarola, Yaron Lipman:
Bespoke Solvers for Generative Flow Models. CoRR abs/2310.19075 (2023) - [i23]Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen:
Guided Flows for Generative Modeling and Decision Making. CoRR abs/2311.13443 (2023) - [i22]Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen:
Stochastic Optimal Control Matching. CoRR abs/2312.02027 (2023) - [i21]Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos:
TaskMet: Task-Driven Metric Learning for Model Learning. CoRR abs/2312.05250 (2023) - 2022
- [c15]Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud:
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. AISTATS 2022: 721-738 - [c14]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: 1749-1763 - [c13]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Semi-Discrete Normalizing Flows through Differentiable Tessellation. NeurIPS 2022 - [c12]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 - [c11]Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen:
Neural Conservation Laws: A Divergence-Free Perspective. NeurIPS 2022 - [i20]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Semi-Discrete Normalizing Flows through Differentiable Tessellation. CoRR abs/2203.06832 (2022) - [i19]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 abs/2207.04711 (2022) - [i18]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 abs/2207.09442 (2022) - [i17]Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio:
Unifying Generative Models with GFlowNets. CoRR abs/2209.02606 (2022) - [i16]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 abs/2210.00999 (2022) - [i15]Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen:
Neural Conservation Laws: A Divergence-Free Perspective. CoRR abs/2210.01741 (2022) - [i14]Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le:
Flow Matching for Generative Modeling. CoRR abs/2210.02747 (2022) - [i13]Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel, Karen Ullrich:
Latent Discretization for Continuous-time Sequence Compression. CoRR abs/2212.13659 (2022) - 2021
- [c10]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Learning Neural Event Functions for Ordinary Differential Equations. ICLR 2021 - [c9]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Neural Spatio-Temporal Point Processes. ICLR 2021 - [c8]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 - [c7]Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons:
"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms. ICML 2021: 5443-5452 - [i12]Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud:
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. CoRR abs/2102.06559 (2021) - 2020
- [c6]Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud:
Scalable Gradients for Stochastic Differential Equations. AISTATS 2020: 3870-3882 - [c5]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS 2020: 60-69 - [c4]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 - [i11]Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud:
Scalable Gradients for Stochastic Differential Equations. CoRR abs/2001.01328 (2020) - [i10]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 abs/2004.00353 (2020) - [i9]Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons:
"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code. CoRR abs/2009.09457 (2020) - [i8]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Learning Neural Event Functions for Ordinary Differential Equations. CoRR abs/2011.03902 (2020) - [i7]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Neural Spatio-Temporal Point Processes. CoRR abs/2011.04583 (2020) - [i6]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR abs/2011.04803 (2020) - [i5]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 abs/2012.05942 (2020)
2010 – 2019
- 2019
- [c3]Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud:
Scalable Gradients and Variational Inference for Stochastic Differential Equations. AABI 2019: 1-28 - [c2]Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud:
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR 2019 - [c1]Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen:
Invertible Residual Networks. ICML 2019: 573-582 - [i4]Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen:
Residual Flows for Invertible Generative Modeling. CoRR abs/1906.02735 (2019) - [i3]Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud:
Latent ODEs for Irregularly-Sampled Time Series. CoRR abs/1907.03907 (2019) - [i2]Ricky T. Q. Chen, David Duvenaud:
Neural Networks with Cheap Differential Operators. CoRR abs/1912.03579 (2019) - 2018
- [i1]Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud:
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR abs/1810.01367 (2018)
Coauthor Index
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