Alexander Trott

According to our database, Alexander Trott authored at least 18 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms.

Aditi Jha, Sam Havens, Jeremy Dohmann, Alex Trott, Jacob Portes

CoRR, 2023

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.

Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David C. Parkes

Proceedings of the ACM Web Conference 2023, 2023

MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining.

Jacob Portes, Alexander Trott, Sam Havens, Daniel King, Abhinav Venigalla, Moin Nadeem, Nikhil Sardana, Daya Khudia, Jonathan Frankle

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning.

Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Learning to Play General-Sum Games against Multiple Boundedly Rational Agents.

Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning.

Tong Mu, Stephan Zheng, Alexander R. Trott

Trans. Mach. Learn. Res., 2022

Using Reinforcement Learning to Study Platform Economies under Market Shocks.

Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David C. Parkes

CoRR, 2022

Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal.

Tong Mu, Stephan Zheng, Alexander Trott

CoRR, 2022

Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning.

Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng

CoRR, 2022

2021

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist.

Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng

CoRR, 2021

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning.

Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher

CoRR, 2021

ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations.

Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng

CoRR, 2021

2020

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies.

Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher

CoRR, 2020

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills.

Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giró-i-Nieto, Jordi Torres

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning World Graphs to Accelerate Hierarchical Reinforcement Learning.

Wenling Shang, Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher

CoRR, 2019

Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards.

Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Competitive experience replay.

Hao Liu, Alexander Trott, Richard Socher, Caiming Xiong

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Interpretable Counting for Visual Question Answering.

Alexander Trott, Caiming Xiong, Richard Socher

Proceedings of the 6th International Conference on Learning Representations, 2018