Kevin Ellis, Cornell University
Programming Problem Generation for Human and Machine Learning
Daehyung Park, Korea Advanced Institute of Science and Technology
Physics-Informed Interactive Skill Learning Toward In-hand Manipulation of Deformable Objects
Damian Borth, University of St.Gallen
Hyper-Representations: Learning from Populations of Neural Networks
Shuran Song, Columbia University
Language-Informed Decision Making for GeneralizableRobot Manipulation
Jaime Fernández Fisac, Princeton University
Safe Reinforcement Learning via Adversarial Imagination
Aaditya Ramdas, Carnegie Mellon University
Distribution-free uncertainty quantification for structured data
Grant M. Rotskoff, Stanford University
Establishing theoretical foundations for robust, data-driven simulations of physical systems
Haipeng Luo, University of Southern California
Goal-Oriented Reinforcement Learning: Theory and Algorithms
Razieh Rahimi, University of Massachusetts - Amherst
Generative Rankers toward Trustworthy Search Result Diversification
Jayadev Acharya, Cornell University
Information-Constrained Statistics and Federated Analytics
Yanning Shen, University of California, Irvine
Addressing Unfairness in Learning over Social Networks
Samet Oymak, University of California, Riverside
Personalized Training Strategies for Heterogeneous Data
Roi Livni, Tel-Aviv University
Fair use of synthetic data