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Brandon Amos
Brandon D. Amos
Person information
- affiliation: Facebook AI, New Yourk City, Ny, USA
- affiliation (PhD 2019): Carnegie Mellon University, Pittsburgh, PA, USA
- affiliation: Virginia Tech, Blacksburg, VA, USA
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2020 – today
- 2024
- [j8]Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato:
Learning to Warm-Start Fixed-Point Optimization Algorithms. J. Mach. Learn. Res. 25: 166:1-166:46 (2024) - [i40]Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, Yuandong Tian:
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs. CoRR abs/2404.16873 (2024) - [i39]Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos:
Neural Optimal Transport with Lagrangian Costs. CoRR abs/2406.00288 (2024) - [i38]Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson:
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. CoRR abs/2407.18158 (2024) - [i37]Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J. Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov:
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold. CoRR abs/2408.14608 (2024) - 2023
- [j7]Brandon Amos:
Tutorial on Amortized Optimization. Found. Trends Mach. Learn. 16(5): 592-732 (2023) - [c41]Brandon Amos:
On amortizing convex conjugates for optimal transport. ICLR 2023 - [c40]Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko:
Meta Optimal Transport. ICML 2023: 791-813 - [c39]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 - [c38]Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. ICML 2023: 42339-42362 - [c37]Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato:
End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization. L4DC 2023: 220-234 - [c36]Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos:
TaskMet: Task-driven Metric Learning for Model Learning. NeurIPS 2023 - [c35]Arman Zharmagambetov, Brandon Amos, Aaron M. Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian:
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information. NeurIPS 2023 - [i36]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) - [i35]Arman Zharmagambetov, Brandon Amos, Aaron M. Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian:
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information. CoRR abs/2307.08964 (2023) - [i34]Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato
:
Learning to Warm-Start Fixed-Point Optimization Algorithms. CoRR abs/2309.07835 (2023) - [i33]Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen:
Stochastic Optimal Control Matching. CoRR abs/2312.02027 (2023) - [i32]Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos:
TaskMet: Task-Driven Metric Learning for Model Learning. CoRR abs/2312.05250 (2023) - 2022
- [c34]Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos:
Cross-Domain Imitation Learning via Optimal Transport. ICLR 2022 - [c33]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 - [c32]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Semi-Discrete Normalizing Flows through Differentiable Tessellation. NeurIPS 2022 - [c31]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 - [c30]Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster:
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. NeurIPS 2022 - [i31]Brandon Amos:
Tutorial on amortized optimization for learning to optimize over continuous domains. CoRR abs/2202.00665 (2022) - [i30]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Semi-Discrete Normalizing Flows through Differentiable Tessellation. CoRR abs/2203.06832 (2022) - [i29]Brandon Amos, Samuel Cohen, Giulia Luise, Ievgen Redko:
Meta Optimal Transport. CoRR abs/2206.05262 (2022) - [i28]Eugene Vinitsky
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. CoRR abs/2206.09889 (2022) - [i27]Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel
Matching Normalizing Flows and Probability Paths on Manifolds. CoRR abs/2207.04711 (2022) - [i26]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) - [i25]Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. CoRR abs/2210.06518 (2022) - [i24]Brandon Amos:
On amortizing convex conjugates for optimal transport. CoRR abs/2210.12153 (2022) - 2021
- [c29]Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus:
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. AAAI 2021: 10674-10681 - [c28]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. AISTATS 2021: 1036-1044 - [c27]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Learning Neural Event Functions for Ordinary Differential Equations. ICLR 2021 - [c26]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Neural Spatio-Temporal Point Processes. ICLR 2021 - [c25]Samuel Cohen, Brandon Amos, Yaron Lipman:
Riemannian Convex Potential Maps. ICML 2021: 2028-2038 - [c24]Anselm Paulus, Michal Rolínek, Vít Musil, Brandon Amos, Georg Martius:
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints. ICML 2021: 8443-8453 - [c23]Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson:
On the model-based stochastic value gradient for continuous reinforcement learning. L4DC 2021: 6-20 - [c22]Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart J. Russell, Noam Brown:
Scalable Online Planning via Reinforcement Learning Fine-Tuning. NeurIPS 2021: 16951-16963 - [i23]Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra:
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. CoRR abs/2104.10159 (2021) - [i22]Anselm Paulus, Michal Rolínek, Vít Musil, Brandon Amos, Georg Martius:
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints. CoRR abs/2105.02343 (2021) - [i21]Samuel Cohen, Brandon Amos, Yaron Lipman:
Riemannian Convex Potential Maps. CoRR abs/2106.10272 (2021) - [i20]Shobha Venkataraman, Brandon Amos:
Neural Fixed-Point Acceleration for Convex Optimization. CoRR abs/2107.10254 (2021) - [i19]Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, Noam Brown:
Scalable Online Planning via Reinforcement Learning Fine-Tuning. CoRR abs/2109.15316 (2021) - [i18]Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos:
Cross-Domain Imitation Learning via Optimal Transport. CoRR abs/2110.03684 (2021) - [i17]Jack Richter-Powell, Jonathan Lorraine, Brandon Amos:
Input Convex Gradient Networks. CoRR abs/2111.12187 (2021) - 2020
- [j6]Brandon D. Amos, David R. Easterling, Layne T. Watson, William I. Thacker, Brent S. Castle, Michael W. Trosset:
Algorithm 1007: QNSTOP - Quasi-Newton Algorithm for Stochastic Optimization. ACM Trans. Math. Softw. 46(2): 17:1-17:20 (2020) - [c21]Brandon Amos, Denis Yarats:
The Differentiable Cross-Entropy Method. ICML 2020: 291-302 - [c20]Nathan O. Lambert, Brandon Amos, Omry Yadan, Roberto Calandra:
Objective Mismatch in Model-based Reinforcement Learning. L4DC 2020: 761-770 - [i16]Nathan O. Lambert, Brandon Amos, Omry Yadan, Roberto Calandra:
Objective Mismatch in Model-based Reinforcement Learning. CoRR abs/2002.04523 (2020) - [i15]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. CoRR abs/2006.12648 (2020) - [i14]Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson:
On the model-based stochastic value gradient for continuous reinforcement learning. CoRR abs/2008.12775 (2020) - [i13]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Learning Neural Event Functions for Ordinary Differential Equations. CoRR abs/2011.03902 (2020) - [i12]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Neural Spatio-Temporal Point Processes. CoRR abs/2011.04583 (2020)
2010 – 2019
- 2019
- [j5]Minghan Chen
Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model of the Budding Yeast Cell Cycle. IEEE ACM Trans. Comput. Biol. Bioinform. 16(1): 301-311 (2019) - [c19]Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter:
Differentiable Convex Optimization Layers. NeurIPS 2019: 9558-9570 - [i11]Brandon Amos, Vladlen Koltun, J. Zico Kolter:
The Limited Multi-Label Projection Layer. CoRR abs/1906.08707 (2019) - [i10]Brandon Amos, Denis Yarats:
The Differentiable Cross-Entropy Method. CoRR abs/1909.12830 (2019) - [i9]Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala:
Generalized Inner Loop Meta-Learning. CoRR abs/1910.01727 (2019) - [i8]Denis Yarats, Amy Zhang
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. CoRR abs/1910.01741 (2019) - [i7]Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter:
Differentiable Convex Optimization Layers. CoRR abs/1910.12430 (2019) - 2018
- [j4]Junjue Wang, Brandon Amos, Anupam Das, Padmanabhan Pillai, Norman M. Sadeh, Mahadev Satyanarayanan:
Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework. ACM Trans. Multim. Comput. Commun. Appl. 14(3s): 64:1-64:24 (2018) - [c18]Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil:
Learning Awareness Models. ICLR (Poster) 2018 - [c17]Noam Brown, Tuomas Sandholm, Brandon Amos:
Depth-Limited Solving for Imperfect-Information Games. NeurIPS 2018: 7674-7685 - [c16]Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter:
Differentiable MPC for End-to-end Planning and Control. NeurIPS 2018: 8299-8310 - [i6]Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil:
Learning Awareness Models. CoRR abs/1804.06318 (2018) - [i5]Noam Brown, Tuomas Sandholm, Brandon Amos:
Depth-Limited Solving for Imperfect-Information Games. CoRR abs/1805.08195 (2018) - [i4]Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter:
Differentiable MPC for End-to-end Planning and Control. CoRR abs/1810.13400 (2018) - 2017
- [c15]Kiryong Ha, Yoshihisa Abe, Thomas Eiszler
You can teach elephants to dance: agile VM handoff for edge computing. SEC 2017: 12:1-12:14 - [c14]Zhuo Chen, Wenlu Hu, Junjue Wang, Siyan Zhao, Brandon Amos, Guanhang Wu, Kiryong Ha, Khalid Elgazzar, Padmanabhan Pillai, Roberta L. Klatzky, Daniel P. Siewiorek, Mahadev Satyanarayanan:
An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance. SEC 2017: 14:1-14:14 - [c13]Brandon Amos, J. Zico Kolter:
OptNet: Differentiable Optimization as a Layer in Neural Networks. ICML 2017: 136-145 - [c12]Brandon Amos, Lei Xu, J. Zico Kolter:
Input Convex Neural Networks. ICML 2017: 146-155 - [c11]Junjue Wang, Brandon Amos, Anupam Das, Padmanabhan Pillai, Norman M. Sadeh, Mahadev Satyanarayanan:
A Scalable and Privacy-Aware IoT Service for Live Video Analytics. MMSys 2017: 38-49 - [c10]Priya L. Donti, J. Zico Kolter, Brandon Amos:
Task-based End-to-end Model Learning in Stochastic Optimization. NIPS 2017: 5484-5494 - [i3]Brandon Amos, J. Zico Kolter:
OptNet: Differentiable Optimization as a Layer in Neural Networks. CoRR abs/1703.00443 (2017) - [i2]Priya L. Donti, Brandon Amos, J. Zico Kolter:
Task-based End-to-end Model Learning. CoRR abs/1703.04529 (2017) - 2016
- [j3]Brandon Amos, Ketan Bhardwaj, Kiron Lebeck:
HotMobile 2016. IEEE Pervasive Comput. 15(2): 79-81 (2016) - [c9]Wenlu Hu, Ying Gao, Kiryong Ha, Junjue Wang, Brandon Amos, Zhuo Chen, Padmanabhan Pillai, Mahadev Satyanarayanan:
Quantifying the Impact of Edge Computing on Mobile Applications. APSys 2016: 5:1-5:8 - [c8]Han Zhao, Tameem Adel, Geoffrey J. Gordon, Brandon Amos:
Collapsed Variational Inference for Sum-Product Networks. ICML 2016: 1310-1318 - [c7]Nigel Davies
Privacy Mediators: Helping IoT Cross the Chasm. HotMobile 2016: 39-44 - [i1]Brandon Amos, Lei Xu, J. Zico Kolter:
Input Convex Neural Networks. CoRR abs/1609.07152 (2016) - 2015
- [j2]Hamilton A. Turner, Jules White, Jaime A. Camelio, Christopher Williams
Bad Parts: Are Our Manufacturing Systems at Risk of Silent Cyberattacks? IEEE Secur. Priv. 13(3): 40-47 (2015) - [j1]Mahadev Satyanarayanan, Pieter Simoens
Edge Analytics in the Internet of Things. IEEE Pervasive Comput. 14(2): 24-31 (2015) - [c6]Zhuo Chen, Lu Jiang, Wenlu Hu, Kiryong Ha, Brandon Amos, Padmanabhan Pillai, Alexander G. Hauptmann, Mahadev Satyanarayanan:
Early Implementation Experience with Wearable Cognitive Assistance Applications. WearSys@MobiSys 2015: 33-38 - [c5]Wenlu Hu, Brandon Amos
The Case for Offload Shaping. HotMobile 2015: 51-56 - 2014
- [c4]Brandon Amos
Performance Study of Spindle, A Web Analytics Query Engine Implemented in Spark. CloudCom 2014: 505-510 - [c3]Brandon D. Amos, David R. Easterling, Layne T. Watson, Brent S. Castle, Michael W. Trosset, William I. Thacker:
Fortran 95 implementation of QNSTOP for global and stochastic optimization. SpringSim (HPS) 2014: 15 - [c2]T. M. Andrew, Brandon D. Amos, David R. Easterling, Cihan Oguz, William T. Baumann, John J. Tyson, Layne T. Watson:
Global parameter estimation for a eukaryotic cell cycle model in systems biology. SummerSim 2014: 45 - 2013
- [c1]Brandon Amos
Applying machine learning classifiers to dynamic Android malware detection at scale. IWCMC 2013: 1666-1671
Coauthor Index
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last updated on 2024-10-04 20:02 CEST by the dblp team
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