209 citations · 560 across the 53 of their papers we have counts for
7 papers · 1 filter
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand, Justin Y. Chen, Piotr Indyk +5
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph…
Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation
Ainesh Bakshi, Piotr Indyk, Praneeth Kacham +2
Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields. The main drawback of using kernel…
Generalization Bounds for Data-Driven Numerical Linear Algebra
Peter Bartlett, Piotr Indyk, Tal Wagner
Data-driven algorithms can adapt their internal structure or parameters to inputs from unknown application-specific distributions, by learning from a training sample of inputs. Sev…
Learning-based Support Estimation in Sublinear Time
Talya Eden, Piotr Indyk, Shyam Narayanan +3
We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a ra…
Addressing Feature Suppression in Unsupervised Visual Representations
Tianhong Li, Lijie Fan, Yuan Yuan +5
Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive…
Learning-Based Low-Rank Approximations
Piotr Indyk, Ali Vakilian, Yang Yuan
We introduce a "learning-based" algorithm for the low-rank decomposition problem: given an matrix , and a parameter , compute a rank- matrix that minimiz…