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Quantifying London Traffic Dynamics for Air Pollution Estimation

Open-source library to automate collection of live traffic video data and extraction of descriptive traffic statistics.
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Jupyter notebook markdown generator

Posts

Future Blog Post

less than 1 minute read

Published: January 01, 2199

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false. Read more

Blog Post number 4

less than 1 minute read

Published: August 14, 2015

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 3

less than 1 minute read

Published: August 14, 2014

Blog Post number 2

less than 1 minute read

Published: August 14, 2013

Blog Post number 1

less than 1 minute read

Published: August 14, 2012

projects

Quantifying London Traffic Dynamics for Air Pollution Estimation

Open-source library to automate collection of live traffic video data and extraction of descriptive traffic statistics.
Read more

publications

The age of secrecy and unfairness in recidivism prediction

Published in HDSR, 2020

We partially reverse-engineer the COMPAS model for recidivism prediction. Read more

Citation: Rudin, Cynthia and Wang, Caroline and Coker, Beau (2020). "The Age of Secrecy and Unfairness in Recidivism Prediction." Harvard Data Science Review.

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In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction

Published in Journal of Quantitative Criminology, 2022

We design various interpretable machine learning models to predict criminal recidivism. Read more

Citation: Caroline Wang*, Bin Han*, Bhrij Patel, Feroze Mohideen, Cynthia Rudin (2022). "In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction." Journal of Quantitative Criminology.

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DM$^2$: Distributed multi-agent reinforcement learning via distribution matching

Published in AAAI, 2023

We propose DM$^2$, an algorithm that allows a team of agents to perform cooperative tasks by independently imitating corresponding experts agents from a team of experts. Read more

Citation: Caroline Wang*, Ishan Durugkar*, Elad Liebman*, Peter Stone. "DM$^2$: Distributed Multi-Agent Reinforcement Learning via Distribution Matching." AAAI 2023.

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D-Shape: Demonstration Shaped Reinforcement Learning

Published in AAMAS, 2023

We propose D-Shape, an RL+IL algorithm that allows learning from suboptimal demonstrations while retaining the ability to find the optimal policy with respect to the task reward. Read more

Citation: Caroline Wang, Garrett Warnell, Peter Stone (2023). "D-Shape: Demonstration Shaped Reinforcement Learning." AAMAS 2023.

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Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning

Published in AAAI, 2024

We introduce Causal Bisimulation Learning (CBM), a method that learns the causal relationships in the dynamics and reward functions for each task to derive a minimal, task-specific abstraction. Read more

Citation: Zizhao Wang*, Caroline Wang*, Xuesu Xiao, Yuke Zhu, Peter Stone (2024). "Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning." AAAI 2024.

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N-Agent Ad Hoc Teamwork

Published in NeurIPS, 2024

Existing paradigms for multi-agent coordination are limited by assuming that either all agents are controlled (e.g. the typical cooperative MARL algorithm), or that only a single agent is controlled (ad hoc teamwork / zero shot coordination). We pose the N-Agent Ad Hoc Teamwork (NAHT) problem to the community, to lift these restrictions and pave the path towards more open multi-agent learning paradigms. Read more

Citation: Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, Peter Stone. "N-Agent Ad Hoc Teamwork." NeurIPS 2024.

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ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

Published in arXiv preprint arXiv:2505.23686, 2025

We formulate ad hoc teamwork as an open-ended learning process between a regret-maximizing teammate generator and an ad hoc teamwork agent. Read more

Citation: Caroline Wang, Arrasy Rahman, Jiaxun Cui, Yoonchang Sung, Peter Stone. "ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork." arXiv preprint arXiv:2505.23686.

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Discovering Differences in Strategic Behavior Between Humans and LLMs

Published in arXiv preprint arXiv:2602.10324, 2026

We employ AlphaEvolve to discover interpretable models from data, revealing that frontier LLMs can be capable of deeper strategic behavior than humans in iterated rock-paper-scissors. Read more

Citation: Caroline Wang, Daniel Kasenberg, Kim Stachenfeld, Pablo Samuel Castro (2026). "Discovering Differences in Strategic Behavior Between Humans and LLMs." arXiv preprint arXiv:2602.10324.

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JaxAHT: A JAX-Based Library for Ad Hoc Teamwork

Published in Workshop on Multi-Agent Learning and Its Opportunities in the Era of Generative AI, 2026

We introduce JaxAHT, the first open-source, JAX-based library designed to accelerate and standardize the Ad Hoc Teamwork research lifecycle using hardware acceleration. Read more

Citation: Caroline Wang, Rolando Fernandez, Jiaxun Cui, Johnny Liu, Aditya Madhan, Zhihan Wang, Lingyun Xiao, Di Yang Shi, Arrasy Rahman, Peter Stone (2026). "JaxAHT: A JAX-Based Library for Ad Hoc Teamwork." Workshop on Multi-Agent Learning and Its Opportunities in the Era of Generative AI.

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