Recorded Talks Filter by - Any -Computational Complexity of Statistical Interference Boot CampFoundational Research SeminarIFML SeminarJoint IFML/CCSI SymposiumMLL Public LecturePublic LecturetinyML TalksUse-Inspired Research SeminarEthics/Fairness in AI SeminarWorkshopML+ X SeminarDistinguished Speaker SeminarPrivacy in AI Seminar 2023 Machine Learning Lab Public Lecture with Scott Aaronson Scott Aaronson, theoretical computer scientist and David J. Bruton Jr. Centennial Professor of Computer Science, UT Austin Machine Learning Lab 2022 Public Lecture with Alan Bovik Alan Bovik, Director, Laboratory for Image & Video Engineering, Machine Learning Lab, UT Austin The Power of Adaptivity in Representation Learning: From Meta-Learning to Federated Learning Aryan Mokhtari, Assistant Professor, Electrical and Computer Engineering, UT Austin Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning Dr. Peter Stone, David Bruton, Jr. Centennial Professor, Associate Chair of Computer Science, Director of Texas Robotics Deployable Robots that Learn Xuesu Xiao, George Mason University (Fall 2022) Multi-Modal Deep Learning of Electrocardiograms for Precision Cardiovascular Health Benjamin Glicksberg, Assistant Professor, Icahn School of Medicine at Mount Sinai Clustering Mixtures with Almost Optimal Separation in Polynomial Time Allen Liu, graduate student in EECS at MIT. IFML Public Lecture: AI for Accurate and Fair Imaging Alex Dimakis, IFML and MLL co-director Optimal Control for Electroceutical Therapies Joshua Chang, M.D., Ph.D., Assistant Professor, Department of Neurology, The University of Texas at Austin Function Space View of Bounded Weight Norm Networks Suriya Gunasekar (Microsoft Research) The Planted Matching Problem: Sharp Threshold and Infinite-Order Phase Transition Dana Yang (Simons Institute) Scalable and Reliable Inference for Probabilistic Modeling Ruqi Zhang (UT Austin) Learning and Optimization With Adaptive Smoothed Adversaries Nika Haghtalab (UC Berkeley) The Surprising Power of the Lenstra-Lenstra-Lovasz Algorithm for Noiseless Inference Ilias Zadik (MIT) Yes, Generative Models are the New Sparsity Alex Dimakis, UT Austin On the Power of Differentiable Learning Nathan Srebro (Toyota Technological Institute at Chicago) A Data-Centric View On Robustness Ludwig Schmidt (University of Washington) Evaluating AI-based MR image reconstruction models: lessons from the fastMRI project Matthew Muckley Closing the Virtuous Cycle of AI for IC and IC for AI David Pan Mapping timescales of cortical language processing Alex Huth Bootstrapping the Error of Oja's Algorithm Purnamrita Sarkar The Role of Explicit Regularization in Overparameterized Neural Networks Shiyu Liang Mind the Gap: From Predictions to ML-Informed Decisions Maria De-Arteaga Facing an Adult Problem: New Data Sources for Fair Machine Learning Moritz Hardt Disparate Predictions: A Complicated Landscape Jamie Morgenstern CityLearn: Demand Response using Multi-Agent Reinforcement Learning Zoltan Nagy Data Analytics and ML for Subsurface Engineering and Geoscience Michael Pyrcz Computational MRI w Deep Learning Jon Tamir ApBio: Teaching Machines Biochemistry Andrew Ellington Harnessing Machine Learning to Study the Life Cycle of Stars Stella Offner N Body Hessians George Biros Remote Sensing and AI Applications in Paleoanthropology Denné Reed In Search of New Algorithms Part II: Graphical Models Adam Klivans In Search of New Algorithms Part I: Neural Networks Adam Klivans The lottery ticket hypothesis for gigantic pre-trained models Atlas Wang AI and Society Ken Fleischmann Applications of AI Raymond Mooney What Is Artificial Intelligence and Machine Learning? Joydeep Biswas