activity
20172026
most citedDECAF: Deep Extreme Classification with Label Features

39 citations · 99 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

ExtractBench: A Benchmark and Evaluation Methodology for Complex Structured Extraction

Nick Ferguson, Josh Pennington, Narek Beghian +4

Unstructured documents like PDFs contain valuable structured information, but downstream systems require this data in reliable, standardized formats. LLMs are increasingly deployed…

cs.LG20226 cited

OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries

Shikhar Jaiswal, Ravishankar Krishnaswamy, Ankit Garg +2

State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accurac…

cs.CL202139 cited

DECAF: Deep Extreme Classification with Label Features

Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal +4

Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as…

cs.CL202124 cited

ECLARE: Extreme Classification with Label Graph Correlations

Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal +3

Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core uti…

cs.PL201730 cited

Lexicographic Ranking Supermartingales: An Efficient Approach to Termination of Probabilistic Programs

Sheshansh Agrawal, Krishnendu Chatterjee, Petr Novotný

Probabilistic programs extend classical imperative programs with real-valued random variables and random branching. The most basic liveness property for such programs is the termin…