Brandon Amos
40 papers · 2016–2025 · 9 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot (9) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (9)
🧭
Keyword Pioneer
🐣
Hot Topic Early Bird
🐝
Cross-Pollinator
(9)
🤝
Dynamic Duo
(10)
👑
Triple Crown
🏆
Grand Slam
🔬
Deep Specialist
(12)
💎
Century Club
(40)
🚀
Conference Pioneer
🗃️
Keyword Collector
(144)
⚡
Prolific Year
(7)
🔥
Unstoppable
(10)
Conferences
ICML (13)
NIPS (12)
ICLR (7)
L4DC (3)
AAAI (1)
AISTATS (1)
EMNLP (1)
JMLR (1)
UAI (1)
Top co-authors
Research topics
Keywords
reinforcement learning
(5)
model-based reinforcement learning
(3)
neural network
(3)
convex optimization
(3)
optimal transport
(3)
imitation learning
(3)
differentiable programming
(3)
differentiable optimization
(2)
generalization bound
(2)
end-to-end learning
(2)
implicit differentiation
(2)
policy optimization
(2)
normalizing flow
(2)
portfolio optimization
(2)
structured prediction
(2)
generative model
(2)
continuous control
(2)
deep learning
(2)
combinatorial optimization
(2)
probabilistic model
(2)
Papers
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
ICLR 2025
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
ICML 2025
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
ICML 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
ICML 2025
Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
ICLR 2025
Neural Optimal Transport with Lagrangian Costs
UAI 2024
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
NIPS 2024
Stochastic Optimal Control Matching
NIPS 2024
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning
EMNLP 2024
Learning to Warm-Start Fixed-Point Optimization Algorithms
JMLR 2024
Multisample Flow Matching: Straightening Flows with Minibatch Couplings
ICML 2023
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
ICML 2023
End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization
L4DC 2023
TaskMet: Task-driven Metric Learning for Model Learning
NIPS 2023
On amortizing convex conjugates for optimal transport
ICLR 2023
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
NIPS 2023
Meta Optimal Transport
ICML 2023
Theseus: A Library for Differentiable Nonlinear Optimization
NIPS 2022
Cross-Domain Imitation Learning via Optimal Transport
ICLR 2022
Matching Normalizing Flows and Probability Paths on Manifolds
ICML 2022
Semi-Discrete Normalizing Flows through Differentiable Tessellation
NIPS 2022
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
NIPS 2022
On the Model-Based Stochastic Value Gradient for Continuous Reinforcement Learning
L4DC 2021
Riemannian Convex Potential Maps
ICML 2021
Neural Spatio-Temporal Point Processes
ICLR 2021
Learning Neural Event Functions for Ordinary Differential Equations
ICLR 2021
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
ICML 2021
Scalable Online Planning via Reinforcement Learning Fine-Tuning
NIPS 2021
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
AAAI 2021
Aligning Time Series on Incomparable Spaces
AISTATS 2021
The Differentiable Cross-Entropy Method
ICML 2020
Objective Mismatch in Model-based Reinforcement Learning
L4DC 2020
Differentiable Convex Optimization Layers
NIPS 2019
Differentiable MPC for End-to-end Planning and Control
NIPS 2018
Depth-Limited Solving for Imperfect-Information Games
NIPS 2018
Learning Awareness Models
ICLR 2018
Input Convex Neural Networks
ICML 2017
OptNet: Differentiable Optimization as a Layer in Neural Networks
ICML 2017
Task-based End-to-end Model Learning in Stochastic Optimization
NIPS 2017
Collapsed Variational Inference for Sum-Product Networks
ICML 2016