Applying machine learning classifiers to dynamic Android malware detection at scale. - Brandon Amos; Hamilton A. Turner; Jules White (IWCMC, 2013)

Performance Study of Spindle, A Web Analytics Query Engine Implemented in Spark. - Brandon Amos; David Tompkins (CloudCom, 2014)

Fortran 95 implementation of QNSTOP for global and stochastic optimization. - Brandon D. Amos; David R. Easterling; Layne T. Watson; Brent S. Castle; Michael W. Trosset; William I. Thacker (SpringSim (HPS), 2014)

Global parameter estimation for a eukaryotic cell cycle model in systems biology. - T. M. Andrew; Brandon D. Amos; David R. Easterling; Cihan Oguz; William T. Baumann; John J. Tyson; Layne T. Watson (SummerSim, 2014)

Early Implementation Experience with Wearable Cognitive Assistance Applications. - Zhuo Chen; Lu Jiang 004; Wenlu Hu; Kiryong Ha; Brandon Amos; Padmanabhan Pillai; Alexander G. Hauptmann; Mahadev Satyanarayanan (WearSys@MobiSys, 2015)

The Case for Offload Shaping. - Wenlu Hu; Brandon Amos; Zhuo Chen; Kiryong Ha; Wolfgang Richter 001; Padmanabhan Pillai; Benjamin Gilbert; Jan Harkes; Mahadev Satyanarayanan (HotMobile, 2015)

Bad Parts: Are Our Manufacturing Systems at Risk of Silent Cyberattacks? - Hamilton A. Turner; Jules White; Jaime A. Camelio; Christopher Williams 002; Brandon Amos; Robert Parker (IEEE Secur. Priv., 2015)

Edge Analytics in the Internet of Things. - Mahadev Satyanarayanan; Pieter Simoens; Yu Xiao 001; Padmanabhan Pillai; Zhuo Chen; Kiryong Ha; Wenlu Hu; Brandon Amos (IEEE Pervasive Comput., 2015)

Quantifying the Impact of Edge Computing on Mobile Applications. - Wenlu Hu; Ying Gao; Kiryong Ha; Junjue Wang; Brandon Amos; Zhuo Chen; Padmanabhan Pillai; Mahadev Satyanarayanan (APSys, 2016)

Collapsed Variational Inference for Sum-Product Networks. - Han Zhao 002; Tameem Adel; Geoffrey J. Gordon; Brandon Amos (ICML, 2016)

Privacy Mediators: Helping IoT Cross the Chasm. - Nigel Davies 001; Nina Taft; Mahadev Satyanarayanan; Sarah Clinch; Brandon Amos (HotMobile, 2016)

HotMobile 2016. - Brandon Amos; Ketan Bhardwaj; Kiron Lebeck (IEEE Pervasive Comput., 2016)

An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance. - 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 (SEC, 2017)

You can teach elephants to dance: agile VM handoff for edge computing. - Kiryong Ha; Yoshihisa Abe; Thomas Eiszler; Zhuo Chen; Wenlu Hu; Brandon Amos; Rohit Upadhyaya; Padmanabhan Pillai; Mahadev Satyanarayanan (SEC, 2017)

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

Input Convex Neural Networks. - Brandon Amos; Lei Xu 040; J. Zico Kolter (ICML, 2017)

A Scalable and Privacy-Aware IoT Service for Live Video Analytics. - Junjue Wang; Brandon Amos; Anupam Das 001; Padmanabhan Pillai; Norman M. Sadeh; Mahadev Satyanarayanan (MMSys, 2017)

Task-based End-to-end Model Learning in Stochastic Optimization. - Priya L. Donti; J. Zico Kolter; Brandon Amos (NIPS, 2017)

Task-based End-to-end Model Learning. - Priya L. Donti; Brandon Amos; J. Zico Kolter (CoRR, 2017)

Learning Awareness Models. - Brandon Amos; Laurent Dinh; Serkan Cabi; Thomas Rothörl; Sergio Gomez Colmenarejo; Alistair Muldal; Tom Erez; Yuval Tassa; Nando de Freitas; Misha Denil (ICLR (Poster), 2018)

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

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

Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework. - Junjue Wang; Brandon Amos; Anupam Das 001; Padmanabhan Pillai; Norman M. Sadeh; Mahadev Satyanarayanan (ACM Trans. Multim. Comput. Commun. Appl., 2018)

Differentiable Convex Optimization Layers. - Akshay Agrawal 001; Brandon Amos; Shane T. Barratt; Stephen P. Boyd; Steven Diamond; J. Zico Kolter (NeurIPS, 2019)

The Limited Multi-Label Projection Layer. - Brandon Amos; Vladlen Koltun; J. Zico Kolter (CoRR, 2019)

Generalized Inner Loop Meta-Learning. - Edward Grefenstette; Brandon Amos; Denis Yarats; Phu Mon Htut; Artem Molchanov; Franziska Meier; Douwe Kiela; Kyunghyun Cho; Soumith Chintala (CoRR, 2019)

Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model of the Budding Yeast Cell Cycle. - Minghan Chen; Brandon D. Amos; Layne T. Watson; John J. Tyson; Young Cao; Clifford A. Shaffer; Michael W. Trosset; Cihan Oguz; Gisella Kakoti (IEEE ACM Trans. Comput. Biol. Bioinform., 2019)

The Differentiable Cross-Entropy Method. - Brandon Amos; Denis Yarats (ICML, 2020)

Objective Mismatch in Model-based Reinforcement Learning. - Nathan O. Lambert; Brandon Amos; Omry Yadan; Roberto Calandra (L4DC, 2020)

Algorithm 1007: QNSTOP - Quasi-Newton Algorithm for Stochastic Optimization. - Brandon D. Amos; David R. Easterling; Layne T. Watson; William I. Thacker; Brent S. Castle; Michael W. Trosset (ACM Trans. Math. Softw., 2020)

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. - Denis Yarats; Amy Zhang 001; Ilya Kostrikov; Brandon Amos; Joelle Pineau; Rob Fergus (AAAI, 2021)

Aligning Time Series on Incomparable Spaces. - Samuel Cohen; Giulia Luise; Alexander Terenin; Brandon Amos; Marc Peter Deisenroth (AISTATS, 2021)

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)

Riemannian Convex Potential Maps. - Samuel Cohen; Brandon Amos; Yaron Lipman (ICML, 2021)

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints. - Anselm Paulus; Michal Rolínek; Vít Musil; Brandon Amos; Georg Martius (ICML, 2021)

On the model-based stochastic value gradient for continuous reinforcement learning. - Brandon Amos; Samuel Stanton; Denis Yarats; Andrew Gordon Wilson (L4DC, 2021)

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

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. - Luis Pineda; Brandon Amos; Amy Zhang 001; Nathan O. Lambert; Roberto Calandra (CoRR, 2021)

Neural Fixed-Point Acceleration for Convex Optimization. - Shobha Venkataraman; Brandon Amos (CoRR, 2021)

Input Convex Gradient Networks. - Jack Richter-Powell; Jonathan Lorraine; Brandon Amos (CoRR, 2021)

Cross-Domain Imitation Learning via Optimal Transport. - Arnaud Fickinger; Samuel Cohen; Stuart Russell 001; Brandon Amos (ICLR, 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)

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 N. Foerster (NeurIPS, 2022)

Tutorial on amortized optimization for learning to optimize over continuous domains. - Brandon Amos (CoRR, 2022)

On amortizing convex conjugates for optimal transport. - Brandon Amos (ICLR, 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 (ICML, 2023)

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

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

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

Tutorial on Amortized Optimization. - Brandon Amos (Found. Trends Mach. Learn., 2023)

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; Marc Finzi; Brandon Amos; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2024)

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

AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs. - Anselm Paulus; Arman Zharmagambetov; Chuan Guo 001; Brandon Amos; Yuandong Tian (CoRR, 2024)

To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning. - Da JU; Song Jiang; Andrew Cohen; Aaron Foss; Sasha Mitts; Arman Zharmagambetov; Brandon Amos; Xian Li 003; Justine T. Kao; Maryam Fazel-Zarandi; Yuandong Tian (CoRR, 2024)

Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback. - Qinqing Zheng; Mikael Henaff; Amy Zhang 001; Aditya Grover; Brandon Amos (CoRR, 2024)

Wasserstein Flow Matching: Generative modeling over families of distributions. - Doron Haviv; Aram-Alexandre Pooladian; Dana Pe'er; Brandon Amos (CoRR, 2024)

AdvPrefix: An Objective for Nuanced LLM Jailbreaks. - Sicheng Zhu; Brandon Amos; Yuandong Tian; Chuan Guo 001; Ivan Evtimov (CoRR, 2024)

Learning to Warm-Start Fixed-Point Optimization Algorithms. - Rajiv Sambharya; Georgina Hall; Brandon Amos; Bartolomeo Stellato (J. Mach. Learn. Res., 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 001; Kirill Neklyudov (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)

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)

Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset. - Lily Hong Zhang; Smitha Milli; Karen Jusko; Jonathan Smith; Brandon Amos; Wassim Bouaziz; Manon Revel; Jack Kussman; Lisa Titus; Bhaktipriya Radharapu; Jane Yu; Vidya Sarma; et al. (CoRR, 2025)

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs? - Ori Press; Brandon Amos; Haoyu Zhao; Yikai Wu 001; Samuel K. Ainsworth; Dominik Krupke; Patrick Kidger; Touqir Sajed; Bartolomeo Stellato; Jisun Park 003; Nathanael Bosch; Eli Meril; et al. (CoRR, 2025)