2 citations · 2 across the 6 of their papers we have counts for
6 papers
Compact Geometric Representations of Hierarchies
Prashant Gokhale, Piotr Indyk, Yuhao Liu +3
Computing geometric representations of data is a cornerstone of modern machine learning, typically achieved by training dual encoders which map queries and documents into a shared…
Optimal Algorithms for Augmented Testing of Discrete Distributions
Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld +1
We consider the problem of hypothesis testing for discrete distributions. In the standard model, where we have sample access to an underlying distribution , extensive research h…
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval
Haike Xu, Zongyu Lin, Yizhou Sun +2
Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applica…
A Bi-metric Framework for Fast Similarity Search
Haike Xu, Sandeep Silwal, Piotr Indyk
We propose a new "bi-metric" framework for designing nearest neighbor data structures. Our framework assumes two dissimilarity functions: a ground-truth metric that is accurate but…
Dimension-Accuracy Tradeoffs in Contrastive Embeddings for Triplets, Terminals & Top-k Nearest Neighbors
Vaggos Chatziafratis, Piotr Indyk
Metric embeddings traditionally study how to map items to a target metric space such that distance lengths are not heavily distorted; but what if we only care to preserve the r…
Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations
Piotr Indyk, Haike Xu
Graph-based approaches to nearest neighbor search are popular and powerful tools for handling large datasets in practice, but they have limited theoretical guarantees. We study the…