3 papers
cs.IR2026
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
Zhengyang Su, Isay Katsman, Yueqi Wang +10
Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit from restricting the output space to a c…
cs.LG2025
Shedding Light on Problems with Hyperbolic Graph Learning
Isay Katsman, Anna Gilbert
Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance…
cs.LG2024
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks
Isay Katsman, Ethan Lou, Anna Gilbert
Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in…