• 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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Learning-Augmented Sketches for Frequency Estimation
Piotr Indyk (Massachusetts Institute of Technology)
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Adaptive Sparse Recovery with Limited Adaptivity
Eric Price (University of Texas at Austin)
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Break
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Universal Sketches
Vladimir Braverman (Johns Hopkins University)
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Lunch
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Approximating the Cost of a Metric K-Nearest Neighbor Graph in Sublinear Time
Christian Sohler (Technische Universität Dortmund and Google Switzerland)
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Testing and Learning Distributions Under Local Information Constraints
Clement Canonne (Stanford University)
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Break
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Erasures vs. Errors in Local Decoding and Property Testing
Sofya Raskhodnikova (Boston University)
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Sublinear Time Local-Access Random Generators
Ronitt Rubinfeld (Massachusetts Institute of Technology)
Reception
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Coffee and Check-In
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Importance Sampling in High Dimensions via Hashing
Moses Charikar (Stanford University)
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Break
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Labeling a Data Set Using Sublinearly Many Queries
Sanjoy Dasgupta (UC San Diego)
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Lunch
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An Optimal Space Lower Bound for Approximating MAX-CUT
Michael Kapralov (Ecole Polytechnique Federale de Lausanne)
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Sublinear Algorithms for (Delta + 1) Vertex Coloring
Sepehr Assadi (University of Pennsylvania)
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Break
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Monte Carlo Approximation Certificates for K-Means Clustering
Dustin Mixon (Ohio State University)
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Certified Sub-Linear Lower Bounds for K-Means Clustering
Soledad Villar (New York University)
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Coffee and Check-In
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High Dimensional Robust Sparse Regression
Constantine Caramanis (University of Texas at Austin)
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Online Algorithms for Low Rank Approximation
Aditya Bhaskara (University of Utah)
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Break
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Fast NN Prediction with No Statistical Tradeoff
Samory Kpotufe (Princeton University)
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Lunch
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Spectral Partitioning for Metrics (And NNs Too)
Alex Andoni (Columbia University)
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Holder Homeomorphisms and Approximate Nearest Neighbors
Ilya Razenshteyn (Microsoft Research)
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Break
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Efficient Reductions for k-Nearest Neighbor Search
Rasmus Pagh (IT University of Copenhagen)
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Coffee and Check-In
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When Hashes Met Wedges - A Distributed Algorithm for Finding High Similarity Vectors
C. Seshadhri (UC Santa Cruz)
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Efficient Structured Matrix Recovery and Nearly Linear Time Algorithms for Solving Inverse Symmetric M-Matrices
Aaron Sidford (Stanford University)
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Break
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Scalable Spatial Scan Statistics with Coresets
Jeff Phillips (University of Utah)
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Lunch at the Simons Institute
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Panel: Can Theory and Machine Learning Meet?
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Break
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Rump Session (Open Problems + Short Talks)