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 $600,000 National Science Foundation project grant supports research at Stony Brook University to develop a suite of novel distributed reinforcement learning algorithms. The grant is funded through the NSF's Computer and Information Science and Engineering program. Specifically, the three-year award will fund research to establish theoretical foundations for designing, analyzing, and applying fully distributed reinforcement learning algorithms over large-scale networks without global...
This $398,856 National Science Foundation project grant supports research at Northwestern University to improve reinforcement learning algorithms. Specifically, the grant funds the development of sample-efficient and computationally-efficient algorithms for both online and offline reinforcement learning with function approximation. The researchers aim to incorporate optimistic exploration and pessimistic exploitation techniques using faithful uncertainty quantification for neural networks....
The National Science Foundation awarded a $245,043 Project Grant to Carnegie Mellon University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The five-year award will support research towards theoretical foundations of neural network-based representation learning. Specifically, the awardee will build a comprehensive theory for new neural network representation learning techniques. This includes characterizing statistical properties of...
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 $600,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will support research at Stanford University toward developing a mathematical foundation for deep reinforcement learning. Over four years, the grant will fund three research thrusts investigating the types of guarantees achievable by reinforcement learning policies under different problem structures and increasing neural network complexity. The researchers will also...
The National Science Foundation (NSF) awarded a $299,998 Project Grant to Oklahoma State University under the NSF Directorate for Engineering (CFDA 47.041) program. This grant supports fundamental research to enhance the efficiency, robustness, and privacy of decentralized machine learning algorithms for processing distributed datasets. The project aims to develop a theoretical framework for efficient and private decentralized Bayesian learning methods that can produce accurate and reliable...
This $400,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to The Ohio State University. The grant supports collaborative research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives are to: 1) Design "white-box" deep neural networks optimized for information gain and representation...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will develop a cross-level methodology to enable state-of-the-art reinforcement learning techniques to run efficiently on resource-constrained edge devices. The proposed $204,205 project, spanning October 2024 to September 2027, will integrate innovative approximate computing circuit techniques and flash device-based computing to significantly...
The National Science Foundation (NSF) awarded a $237,028 Project Grant to New York University (NYU) under the Computer and Information Science and Engineering program (CFDA 47.070) to develop statistical and algorithmic foundations for robust policy learning in uncertain environments. The goal is to create provably efficient techniques for learning optimization policies that can be deployed in practical settings where the training and operational environments differ, such as when using digital...