Amos, Brandon

58 publications

JAIR 2026 Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning Mattia Silvestri, Senne Berden, Gaetano Signorelli, Ali Irfan Mahmutogullari, Jayanta Mandi, Brandon Amos, Tias Guns, Michele Lombardi

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 AdvPrefix: An Objective for Nuanced LLM Jailbreaks Sicheng Zhu, Brandon Amos, Yuandong Tian, Chuan Guo, Ivan Evtimov

ICML 2025 AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, Yuandong Tian

NeurIPS 2025 AlgoTune: Can Language Models Speed up General-Purpose Numerical Programs? Ori Press, Brandon Amos, Haoyu Zhao, Yikai Wu, Samuel Ainsworth, Dominik Krupke, Patrick Kidger, Touqir Sajed, Bartolomeo Stellato, Jisun Park, Nathanael Bosch, Eli Meril, Albert Steppi, Arman Zharmagambetov, Fangzhao Zhang, David Pérez-Piñeiro, Alberto Mercurio, Ni Zhan, Talor Abramovich, Kilian Lieret, Hanlin Zhang, Shirley Huang, Matthias Bethge, Ofir Press

ICLR 2025 Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles Buu Phan, Brandon Amos, Itai Gat, Marton Havasi, Matthew J. Muckley, Karen Ullrich

ICLR 2025 Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov

ICML 2025 Wasserstein Flow Matching: Generative Modeling over Families of Distributions Doron Haviv, Aram-Alexandre Pooladian, Dana Pe’Er, Brandon Amos

JMLR 2024 Learning to Warm-Start Fixed-Point Optimization Algorithms Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato

ICMLW 2024 Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov

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

NeurIPS 2024 Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson

ICMLW 2024 Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models Sanae Lotfi, Yilun Kuang, Marc Anton Finzi, Brandon Amos, Micah Goldblum, Andrew Gordon Wilson

L4DC 2023 End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato

ICMLW 2023 Koopman Constrained Policy Optimization: A Koopman Operator Theoretic Method for Differentiable Optimal Control in Robotics Matthew Retchin, Brandon Amos, Steven Brunton, Shuran Song

NeurIPS 2023 Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian

ICMLW 2023 Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information Arman Zharmagambetov, Brandon Amos, Aaron M Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian

ICMLW 2023 Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information Arman Zharmagambetov, Brandon Amos, Aaron M Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian

ICML 2023 Meta Optimal Transport Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko

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

ICLR 2023 On Amortizing Convex Conjugates for Optimal Transport Brandon Amos

ICML 2023 Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover

ICLRW 2023 Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover

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

FnTML 2023 Tutorial on Amortized Optimization Brandon Amos

ICLR 2022 Cross-Domain Imitation Learning via Optimal Transport Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos

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 Nocturne: A Scalable Driving Benchmark for Bringing Multi-Agent Learning One Step Closer to the Real World Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster

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

AISTATS 2021 Aligning Time Series on Incomparable Spaces Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth

ICML 2021 CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints Anselm Paulus, Michal Rolinek, Vit Musil, Brandon Amos, Georg Martius

NeurIPSW 2021 Cross-Domain Imitation Learning via Optimal Transport Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos

NeurIPSW 2021 Imitation Learning from Pixel Observations for Continuous Control Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, Denis Yarats

AAAI 2021 Improving Sample Efficiency in Model-Free Reinforcement Learning from Images Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus

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

ICMLW 2021 Neural Fixed-Point Acceleration for Convex Optimization Shobha Venkataraman, Brandon Amos

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

L4DC 2021 On the Model-Based Stochastic Value Gradient for Continuous Reinforcement Learning Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

ICML 2021 Riemannian Convex Potential Maps Samuel Cohen, Brandon Amos, Yaron Lipman

NeurIPS 2021 Scalable Online Planning via Reinforcement Learning Fine-Tuning Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart J. Russell, Noam Brown

NeurIPSW 2020 Fit the Right NP-Hard Problem: End-to-End Learning of Integer Programming Constraints Anselm Paulus, Michal Rolinek, Vít Musil, Brandon Amos, Georg Martius

ICLR 2020 Objective Mismatch in Model-Based Reinforcement Learning Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra

L4DC 2020 Objective Mismatch in Model-Based Reinforcement Learning Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra

ICML 2020 The Differentiable Cross-Entropy Method Brandon Amos, Denis Yarats

NeurIPS 2019 Differentiable Convex Optimization Layers Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, J. Zico Kolter

NeurIPS 2018 Depth-Limited Solving for Imperfect-Information Games Noam Brown, Tuomas Sandholm, Brandon Amos

NeurIPS 2018 Differentiable MPC for End-to-End Planning and Control Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots, J. Zico Kolter

ICLR 2018 Learning Awareness Models Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

ICML 2017 Input Convex Neural Networks Brandon Amos, Lei Xu, J. Zico Kolter

ICML 2017 OptNet: Differentiable Optimization as a Layer in Neural Networks Brandon Amos, J. Zico Kolter

NeurIPS 2017 Task-Based End-to-End Model Learning in Stochastic Optimization Priya Donti, Brandon Amos, J. Zico Kolter

ICML 2016 Collapsed Variational Inference for Sum-Product Networks Han Zhao, Tameem Adel, Geoff Gordon, Brandon Amos