Recorded Talks Filter by - Any -Computational Complexity of Statistical Interference Boot CampFoundational Research SeminarIFML + DMS Joint EventIFML Diffusion Seminar SeriesIFML SeminarJoint IFML/CCSI SymposiumMLL Public LecturePublic LecturetinyML TalksUse-Inspired Research SeminarEthics/Fairness in AI SeminarWorkshopML+ X SeminarDistinguished Speaker SeminarPrivacy in AI Seminar IFML Seminar: 05/02/25 - Efficient Algorithms for Learning with Distribution Shift Adam Klivans, professor of computer science and director of IFML and Machine Learning Lab, UT-Austin IFML Seminar: 04/25/25 - On the Role of Gaussian Covariates in Minimum Norm Interpolation Gil Kur, postdoctoral fellow at ETH Zürich IFML Seminar: 04/18/25 - Learning to Solve Imaging Inverse Problems without Ground Truth Andrew Wang, PhD student, Institute for Imaging, Data and Communications in the School of Engineering, University of Edinburgh IFML Seminar: 04/11/25 - Beyond Benchmarks: Building a Science of AI Measurement Sanmi Koyejo, assistant professor in Computer Science, Stanford University and co-founder of Virtue AI IFML Seminar: 03/28/2025 - Simple Binary Hypothesis Testing: One-shot Bayes Error Bounds and Reverse Data-processing Inequalities Varun Jog, professor of Information Theory and Statistics, Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge IFML Seminar: 03/14/2025 - Training-free Approaches for Image Inversion and Editing Using Latent Generative Models Sanjay Shakkottai, Professor in the Chandra Family Department of Electrical and Computer Engineering, UT Austin IFML Seminar: 03/13/2025 - Near-Optimal Algorithms for Semirandom Planted Clique Pravesh K. Kothari, Assistant Professor in Computer Science, Princeton IFML Seminar: 03/07/2025 - Spectral Norm of Random Matrices and Tensors March Boedihardjo, assistant professor at Michigan State University IFML Seminar: 02/28/2025 - Bridging Generative and Discriminative Learning with Contrastive Mutual Information Machine Micha Livne, Senior Research Scientist at NVIDIA IFML Seminar: 02/14/2025 - Differentiable Weightless Neural Networks (DWNs) Alan T. L. Bacellar, Ph. D student in the Electrical and Computer Engineering Department, UT Austin IFML Seminar: 02/07/2025 - Preference Optimization in Large Language Model Alignment: Personalization, Common Pitfalls and Beyond Leqi Liu, Assistant Professor in use-inspired AI, Department of Information, Risk and Operations Management - UT Austin IFML Seminar: 01/31/2025 - Testing Noise Assumptions of Learning Algorithms Arsen Vasilyan, postdoctoral fellow at the Institute for Foundations of Machine Learning IFML Seminar: 12/13/2024 - Safe and Informative Imaging via Conformal Prediction and Generative Models Phil Schniter, Professor, Electrical and Computer Engineering The Ohio State University IFML Seminar: 12/06/24 - Unscripted Grounded Visual Learning Stella Yu, Professor of Electrical Engineering and Computer Science University of Michigan IFML Seminar: 11/15/24 Online Convex Optimization with a Separation Oracle Zak Mhammedi, Research Scientist at Google Research Tutorial on Diffusion Models for Image Generation -- Sanjay Shakkottai Sanjay Shakkottai IFML Seminar: 10/4/25 - Foundation Model for Sequential Decision-Making Furong Huang, Associate Professor, University of Maryland IFML Seminar: 9/27/24 - Computationally Efficient Reinforcement Learning with Linear Bellman Completeness Noah Golowich , PhD Student, MIT IFML Seminar: 9/13/24 - On the Computational Complexity of Private High-dimensional Model Selection Saptarshi Roy, Postdoc Research Fellow, The University of Texas at Austin IFML Seminar: 9/6/24 - Perceiving Humans in 4D Georogios Pavlakos, Assistant Professor, UT Austin IFML Seminar: 8/23/24 - Clued-in to Clueless: Navigating Distribution Shifts with Varying Levels of Target Distribution Information Olawale Salaudeen, Postdoctoral Associate, MIT CSAIL IFML Seminar: Generating a Video: Reflecting on a Two-Year Odyssey Atlas Wang, Associate Professor, UT Austin AIHealthTalk : 4/10/24 - Towards Digital Twins for Cardiovascular Health: From Clinical To Remote Bobak Mortazavi, Associate Professor, Texas A&M University IFML Seminar: 4/5/24 - Robustness in the Era of LLMs: Jailbreaking Attacks and Defenses Hamed Hassani, Associate Professor, The University of Pennsylvania AIHealthTalk : 4/3/24 - The Generalist Medical AI Will See You Now Pranav Rajpurkar, Assistant Professor, Harvard University AIHealthTalk : 3/27/24 - Shaping the Creation and Adoption of Large Language Models in Healthcare Nigam Shah, Professor, Stanford University AIHealthTalk: 3/20/24 - How LLMs Might Help Scale World Class Healthcare to Everyone Vivek Natarajan, Research Scientist, Google Health IFML Seminar: 3/8/2024 - An Lyapunov Analysis of the Lion Optimizer IFML Seminar: 2/23/2024 - Recent Advances in Parallel Stochastic Convex Optimization IFML Seminar: 3/1/2024 - On Solving Inverse Problems Using Latent Diffusion-based Generative Models Sanjay Shakkottai, Professor Cockrell Family Chair in Engineering # 1, UT Austin IFML SEMINAR: 2/16/24 - Long Context Foundational Models Srinadh Bhojanapalli, Research Scientist at Google Research IFML SEMINAR: 2/2/24 - Gromov-Wasserstein Alignment: Statistical and Computational Advancements via Duality IFML SEMINAR: Jan 26, 2024 - Meta Optimization Elad Hazan, Professor, Princeton and Director and co-founder, Google AI Princeton 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) Pagination Page 1 Page 2 Next page Next Last page
