Purdue University was awarded a $450,000 project grant from the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075) to advance trustworthy reinforcement learning techniques for online decision making. The three-year award beginning August 15, 2022 will support research into robustness, fairness, causality and explainability in dynamic pricing, dynamic assortment selection and two-sided market matching. The University will develop new theoretical tools,...
This $474,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop a suite of neural bandit learning algorithms that leverage recent advances in deep learning theory for efficient neural network model training with incomplete feedback. The key objectives are to: 1) Advance bandit learning methods in more complex neural network architectures and explore new deep learning...
This $299,631 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) will support Purdue University's research on theoretical foundations for integrating foundational models into reinforcement learning. The project aims to eliminate reliance on simulators and develop reinforcement learning methods that can be trained directly in real-world settings. Key research areas include incorporating...
This Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the CFDA program "Computer and Information Science and Engineering" provides $150,000 to Duke University to conduct collaborative research on developing efficient and provable algorithms for approximate sampling-based exploration strategies in sequential decision-making problems. The key objectives are to unify exploration strategies across different applications like...
This $474,000 federal Project Grant award, issued by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070), supports research to develop neural bandit learning algorithms that leverage deep learning techniques to optimize decision-making in contexts with incomplete feedback. The primary awardee, the University of California, Los Angeles (UCLA), will lead a multi-year research project to bridge the gap between deep learning...
This National Science Foundation (NSF) Office of Advanced Cyberinfrastructure grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $150,000 from October 1, 2023 to September 30, 2026 to the University of California, Los Angeles (UCLA) to conduct collaborative research on developing efficient and provably effective exploration strategies for sequential decision-making problems in artificial intelligence applications. The key products and...
This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) provides $300,000 to the Regents of the University of California at Riverside to conduct research on adapting foundation models for multimodal sequential decision-making. The project aims to develop novel techniques and methods to leverage foundation models, which are complex neural networks trained on large datasets, to improve the performance of...
This Project Grant award of $476,440 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research into developing new algorithms and approaches for unsupervised and autonomous reinforcement learning of skills by artificial intelligence (AI) models. The key objectives of the research are to: 1) create new algorithms to discover small, reusable skills that can be rapidly combined to solve complex tasks; 2) develop...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, with CFDA number 47.049, will fund research to address the challenge of reliable and interpretable reinforcement learning (RL) systems in complex, data-limited environments. The $154,999 award, effective August 1, 2025 through July 31, 2028, aims to develop theoretical foundations and methods for robust inference and decision-making in RL, including tools for contextual bandits...