82 citations · 187 across the 21 of their papers we have counts for
24 papers
Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
Siddhartha Jain, Ameya Velingker
Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context…
Even Sparser Graph Transformers
Hamed Shirzad, Honghao Lin, Balaji Venkatachalam +3
Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scali…
A Theory for Compressibility of Graph Transformers for Transductive Learning
Hamed Shirzad, Honghao Lin, Ameya Velingker +3
Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold a…
Weisfeiler-Leman at the margin: When more expressivity matters
Billy J. Franks, Christopher Morris, Ameya Velingker +1
The Weisfeiler-Leman algorithm (-WL) is a well-studied heuristic for the graph isomorphism problem. Recently, the algorithm has played a prominent role in understanding the expr…
Locality-Aware Graph-Rewiring in GNNs
Federico Barbero, Ameya Velingker, Amin Saberi +2
Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursiv…
Fast -Approximation Algorithms for Binary Matrix Factorization
Ameya Velingker, Maximilian Vötsch, David P. Woodruff +1
We introduce efficient -approximation algorithms for the binary matrix factorization (BMF) problem, where the inputs are a matrix $\mathbf{A}\in\{0,1\}^{n\times d}…