Schultheis, Erik

12 publications

ICLRW 2025 Compressed Sparse Tiles for Memory-Efficient Unstructured and Semi-Structured Sparsity Mike Lasby, Max Zimmer, Sebastian Pokutta, Erik Schultheis

ICML 2025 ELMO : Efficiency via Low-Precision and Peak Memory Optimization in Large Output Spaces Jinbin Zhang, Nasib Ullah, Erik Schultheis, Rohit Babbar

NeurIPS 2025 Hogwild! Inference: Parallel LLM Generation via Concurrent Attention Gleb Rodionov, Roman Garipov, Alina Shutova, George Yakushev, Erik Schultheis, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, Dan Alistarh

TMLR 2025 Unbiased Loss Functions for Multilabel Classification with Missing Labels Erik Schultheis, Rohit Babbar

ICML 2024 A General Online Algorithm for Optimizing Complex Performance Metrics Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski

ICLR 2024 Consistent Algorithms for Multi-Label Classification with Macro-at-$k$ Metrics Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski

ECML-PKDD 2024 Llama-Annotate - Visualizing Token-Level Confidences for LLMs Erik Schultheis, St John

NeurIPS 2024 Navigating Extremes: Dynamic Sparsity in Large Output Spaces Nasib Ullah, Erik Schultheis, Mike Lasby, Yani Ioannou, Rohit Babbar

NeurIPS 2023 Generalized Test Utilities for Long-Tail Performance in Extreme Multi-Label Classification Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof J. Dembczynski

ECML-PKDD 2023 Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU Erik Schultheis, Rohit Babbar

NeurIPS 2022 CascadeXML: Rethinking Transformers for End-to-End Multi-Resolution Training in Extreme Multi-Label Classification Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar

MLJ 2022 Speeding-up One-Versus-All Training for Extreme Classification via Mean-Separating Initialization Erik Schultheis, Rohit Babbar