Internal Program Activities Breadcrumb Home Programs & Events Internal Program Activities Upcoming Past View Events by Program - All -Sublinear Algorithms View Events by Program No Internal Activities yet. Pagination Previous page Previous Page 1 Current page 2 View Events by Program - All -Sublinear AlgorithmsError-Correcting Codes: Theory and PracticeLogic and Algorithms in Database Theory and AIData Structures and Optimization for Fast AlgorithmsAnalysis and TCS: New FrontiersSummer Cluster on Quantum ComputingQuantum Research PodMeta-ComplexityExtended Reunion: SatisfiabilityData-Driven Decision ProcessesGraph Limits and Processes on Networks: From Epidemics to MisinformationSummer Cluster: AI and HumanitySummer Cluster: Interpretable Machine LearningComputational Innovation and Data-Driven BiologySummer Cluster: Deep Learning TheoryCausalityLearning and GamesGeometric Methods in Optimization and SamplingComputational Complexity of Statistical InferenceTheoretical Foundations of Computer SystemsSatisfiability: Theory, Practice, and BeyondTheory of Reinforcement LearningProbability, Geometry, and Computation in High DimensionsLattices: Algorithms, Complexity, and CryptographyThe Quantum Wave in ComputingProofs, Consensus, and Decentralizing SocietyOnline and Matching-Based Market DesignSummer Cluster: Error-Correcting Codes and High-Dimensional ExpansionFoundations of Deep LearningData Privacy: Foundations and ApplicationsGeometry of PolynomialsFoundations of Data ScienceLower Bounds in Computational ComplexityThe Brain and ComputationReal-Time Decision MakingBridging Continuous and Discrete OptimizationFoundations of Machine LearningPseudorandomnessLogical Structures in ComputationAlgorithms and UncertaintyAlgorithmic Challenges in GenomicsCounting Complexity and Phase TransitionsFine-Grained Complexity and Algorithm DesignEconomics and ComputationCryptographyInformation TheoryAlgorithmic Spectral Graph TheoryAlgorithms and Complexity in Algebraic GeometryEvolutionary Biology and the Theory of ComputingQuantum Hamiltonian ComplexityReal Analysis in Computer ScienceTheoretical Foundations of Big Data Analysis 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) 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 (UCLA) 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) 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 78 Page 79 Current page 80 Page 81 Page 82 Next page Next Last page Last