www.cs.utexas.edu

Graclus software

Graclus (latest: Version 1.2) is a fast graph clustering software that computes normalized cut and ratio association for a given undirected graph without any eigenvector computation. This is possible ...
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UTPC: UT Programming Contest

Every year, we send four teams (each consisting of three students) to the tri-state South Central Regional. One team from UT Austin might advance to the North America Championship and the World Finals ...
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ICRA 2022 BARN Challenge

The BARN Challenge will take place primarily on the simulated BARN dataset and also physically at the conference venue in Philadelphia. The BARN dataset comprises 300 pre-generated navigation ...
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Akanksha Jain

I do research in Computer Architecture, with a focus on memory system performance. My research has introduced novel ways to improve hardware caching and prefetching. I am the inventor of the Hawkeye ...
www.cs.utexas.edu

Kavish Muthum

Hi! I'm a student at The University of Texas at Austin studying Computer Science and Mathematics.
Department of Computer Science - University of Texas at Austin

Peter Stone Delivers Keynote at the Association for the Advancement of Artificial Intelligence (AAAI) Conference

Peter Stone, Truchard Foundation Chair in Computer Science and Chair of the Department of Computer Science, presents the keynote “From How to Learn to What to Learn in Multiagent Systems and Robotics” ...
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CS 329E: Elements of Data Visualization - Spring 2026

Sangjun Park Email: sangjun at cs dot utexas dot edu Office Hours: Monday 11:00am – 12:00pm at GDC 1.302, TA Station Desk 5 (GDC basement) Wednesday 11:00am – 12:00pm at GDC 1.302, TA Station Desk 5 ...
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Transfer Learning for Reinforcement Learning Domains: A Survey

Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. Journal of Machine Learning Research, 10(1):1633–1685, 2009.
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Deep Recurrent Q-Learning for Partially Observable MDPs

Deep Recurrent Q-Learning for Partially Observable MDPs Deep Recurrent Q-Learning for Partially Observable MDPs. Matthew Hausknecht and Peter Stone. In AAAI Fall Symposium on Sequential Decision ...
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To Teach or not to Teach? Decision Making Under Uncertainty in Ad Hoc Teams

To Teach or not to Teach? Decision Making Under Uncertainty in Ad Hoc Teams. Peter Stone and Sarit Kraus. In The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), ...
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Deep Reinforcement Learning in Parameterized Action Space

Recent work has shown that deep neural networks are capable ofapproximating both value functions and policies in reinforcementlearning domains featuring continuous state and actionspaces. However, to ...
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Reasoning about Hypothetical Agent Behaviours and their Parameters

Reasoning about Hypothetical Agent Behaviours and their Parameters. Stefano Albrecht and Peter Stone. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems ...