• Click on the titles of individual talks for abstract, slides and archived video.

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Coffee and Check-In
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Opening Remarks
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Robust List Decoding of Spherical Gaussians
Daniel Kane (UC San Diego)
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Mixture Models, Robustness, and Sum of Squares Proofs
Jerry Li (Microsoft Research)
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Break
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Computational-Statistical Tradeoffs in Robust High-Dimensional Estimation
Ilias Diakonikolas (University of Wisconsin-Madison)
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Lunch
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Efficient Algorithms for Outlier-Robust Regression
Adam Klivans (University of Texas, Austin)
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Detecting Weak Signals via Hypothesis Testing
Ravi Kannan (Microsoft Research India)
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Break
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On Using Graph Distances to Estimate Euclidean and Related Distances
Ery Arias-Castro (UC San Diego)
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The Importance of Better Models in Stochastic Optimization - Robustness, Fast Convergence, and Optimality
John Duchi (Stanford University)
Reception
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Coffee and Check-In
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Invariance, Causality and Novel Robustness
Peter Bühlmann (ETH Zürich)
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Generalization Bounds for Uniformly Stable Algorithms
Vitaly Feldman (Apple)
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Break
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Orthogonal Machine Learning: Power and Limitations
Lester Mackey (Microsoft Research)
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Lunch
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Some Problems of Robust High-dimensional Statistics
Gilad Lerman (University of Minnesota)
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TAP Free Energy, Spin Glasses, and Variational Inference
Zhou Fan (Yale University)
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Break
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A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression
Pragya Sur (Stanford University)
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Robustness in High-Dimensional Inference Tasks
Jelena Bradic (UC San Diego)
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Coffee and Check-In
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Controlling FDR While Highlighting Selected Discoveries
Chiara Sabatti (Stanford University)
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The Robustness of Controlling the False Discovery Rate
Weijie Su (University of Pennsylvania)
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Break
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A Flexible Framework for Hypothesis Testing in High-Dimensions
Adel Javanmard (University of Southern California)
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Lunch
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Lightning Talks
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Coffee and Check-In
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Robust Sparse Covariance Estimation by Thresholding Tyler's M-estimator
Boaz Nadler (Weizmann Institute)
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Sub-Gaussian Mean Estimation in Polynomial Time
Samuel Hopkins (UC Berkeley)
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Break
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Robust Estimation and Generative Adversarial Nets
Chao Gao (University of Chicago)
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Lunch
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Can Non-Convex Optimization be Robust?
Rong Ge (Duke University)
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Semi-Random Models and Robustness in Unsupervised Learning
Aravindan Vijayaraghavan (Northwestern University)
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Break
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Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
Laura Balzano (University of Michigan)
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How Many Directions Determine a Shape: A VC Theory for Modulii Spaces of Shapes
Sayan Mukherjee (Duke University)
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Coffee and Check-In
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Scale Calibration for High-Dimensional Robust Regression
Po-Ling Loh (University of Wisconsin, Madison)
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k-means and k-medians under Dimension Reduction
Yury Makarychev (Toyota Technological Institute at Chicago)
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Break
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Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation
Moses Charikar (Stanford University)
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Realizing Robustness
Gautam Kamath (Massachusetts Institute of Technology)