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» Piotr Indyk
Piotr Indyk
Distance-Sensitive Hashing∗
Arxiv:2102.08942V1 [Cs.DB]
SIGMOD Flyer
Lower Bounds on Lattice Sieving and Information Set Decoding
Constraint Clustering and Parity Games
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
Model Checking Large Design Spaces: Theory, Tools, and Experiments
Scalable Nearest Neighbor Search for Optimal Transport∗
SETH-Based Lower Bounds for Subset Sum and Bicriteria Path∗
Curriculum Vitae
Approximate Nearest Neighbor Search in High Dimensions
Fiat-Shamir Via List-Recoverable Codes (Or: Parallel Repetition of GMW Is Not Zero-Knowledge)
Rajeev Motwani
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
A Constant Factor Approximation Algorithm for Fault-Tolerant K- Median
Submission Data for 2020-2021 CORE Conference Ranking Process International Colloquium on Automata Languages and Programming
Compositional Upper-Bounding of Diameters of Factored Digraphs
Contents U U U
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The Power of Two Min-Hashes in Similarity Search Among
Curriculum Vitae
SETH-Based Lower Bounds for Subset Sum and Bicriteria Path
Approximate Nearest Neighbor Search in High Dimensions
References for the “Algorithmic High Dimensional Geometry” Lectures at the Big Data Boot Camp, Simons Institute, Berkeley
Model Counting Meets F0 Estimation
Distributed PCP Theorems for Hardness of Approximation in P
People Like Us: Mining Scholarly Data for Comparable Researchers
Alexandr Andoni Title: Associate Professor, Dept
Approximate Nearest Neighbor Algorithms for Hausdorff Metrics Via Embeddings
Efficient and Private Distance Approximation in The
SODA14 – Accepted Papers
In Defense of Minhash Over Simhash
On Closest Pair in Euclidean Metric: Monochromatic Is As Hard As Bichromatic
Association for Computing Machinery 2 Penn Plaza, Suite 701, New York
Sublinear-Time Sparse Recovery, and Its Power in the Design of Exact Algorithms
Optimal Set Similarity Data-Structures Without False Negatives IT University of Copenhagen
A Sketch-Based Sampling Algorithm on Sparse Data