Assessing the Impact of Distribution Shift on Reinforcement Learning Performance

no code implementations • 5 Feb 2024 • Ted Fujimoto, Joshua Suetterlein, Samrat Chatterjee, Auroop Ganguly

We then apply these tools to single-agent and multi-agent environments to show the impact of introducing distribution shifts during test time.

reinforcement-learning Reinforcement Learning (RL) +1

Ad Hoc Teamwork in the Presence of Adversaries

no code implementations • 9 Aug 2022 • Ted Fujimoto, Samrat Chatterjee, Auroop Ganguly

Advances in ad hoc teamwork have the potential to create agents that collaborate robustly in real-world applications.

Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

no code implementations • 12 Feb 2019 • Edward Collier, Kate Duffy, Sangram Ganguly, Geri Madanguit, Subodh Kalia, Gayaka Shreekant, Ramakrishna Nemani, Andrew Michaelis, Shuang Li, Auroop Ganguly, Supratik Mukhopadhyay

Machine learning has proven to be useful in classification and segmentation of images.

BIG-bench Machine Learning General Classification +3

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

no code implementations • 27 Dec 2016 • Anuj Karpatne, Gowtham Atluri, James Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, Vipin Kumar

Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery.