Internal Program Activities Breadcrumb Home Programs & Events Internal Activities Upcoming Past View Events by Program - All - View Events by Program No Internal Activities yet. View Events by Program - All -Theory of Reinforcement LearningProbability, Geometry, and Computation in High DimensionsSummer Cluster: Interpretable Machine Learning View Events by Program Fellows Talk - Cindy Rush and Erik Waingarten Fellows Talk - Cindy Rush and Erik Waingarten Probability, Geometry, and Computation in High Dimensions Cindy Rush (Columbia University), Erik Waingarten (University of Pennsylvania) Nodal Domains of G(n,p) Graphs Nodal Domains of G(n,p) Graphs Probability, Geometry, and Computation in High Dimensions Mark Rudelson (University of Michigan) Evaluating AI Decision Support Tools as Embedded Systems Evaluating AI Decision Support Tools as Embedded Systems Summer Cluster: Interpretable Machine Learning Sina Fazelpour (Carnegie Mellon University) Fellows Talk - Galyna Livshyts and Lin Yang Fellows Talk - Galyna Livshyts and Lin Yang Probability, Geometry, and Computation in High Dimensions Galyna Livshyts (Georgia Institute of Technology), Lin Yang (UMass, Amherst) Polynomial Time Trace Reconstruction in the Smoothed Complexity Model Polynomial Time Trace Reconstruction in the Smoothed Complexity Model Probability, Geometry, and Computation in High Dimensions Anindya De (University of Pennsylvania) The Interpolation Phase Transition in Neural Networks: Memorization and Generalization Under Lazy Training The Interpolation Phase Transition in Neural Networks: Memorization and Generalization Under Lazy Training Probability, Geometry, and Computation in High Dimensions Andrea Montanari (Stanford University) Fellows Talk - The Kikuchi Hierarchy & Tensor PCA Fellows Talk - The Kikuchi Hierarchy & Tensor PCA Probability, Geometry, and Computation in High Dimensions Ahmed El Alaoui, Stanford University Explainable k-Means and k-Medians Clustering Explainable k-Means and k-Medians Clustering Summer Cluster: Interpretable Machine Learning Michal Moshkovitz Why Causal Interpretations Matter for Algorithmic Bias Mitigation: A Legal Perspective Why Causal Interpretations Matter for Algorithmic Bias Mitigation: A Legal Perspective Summer Cluster: Interpretable Machine Learning Alice Xiang (Partnership on AI) A Critical Look At Some Common Trends In IML Research A Critical Look At Some Common Trends In IML Research Summer Cluster: Interpretable Machine Learning Shai Ben-David (University of Waterloo) Pagination First page First Previous page Previous Page 84 Page 85 Current page 86 Page 87 Page 88 Next page Next Last page Last
