Erik Schultheis

According to our database, Erik Schultheis authored at least 21 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?

Nasib Ullah, Erik Schultheis, Jinbin Zhang, Rohit Babbar

CoRR, 2024

Navigating Extremes: Dynamic Sparsity in Large Output Space.

Nasib Ullah, Erik Schultheis, Mike Lasby, Yani Ioannou, Rohit Babbar

CoRR, 2024

Learning label-label correlations in Extreme Multi-label Classification via Label Features.

Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar

CoRR, 2024

LLaMA-Annotate - Visualizing Token-Level Confidences for LLMs.

Erik Schultheis, St John

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track, 2024

Navigating Extremes: Dynamic Sparsity in Large Output Spaces.

Nasibullah Nasibullah, Erik Schultheis, Mike Lasby, Yani Ioannou, Rohit Babbar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features.

Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

A General Online Algorithm for Optimizing Complex Performance Metrics.

Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Consistent algorithms for multi-label classification with macro-at-k metrics.

Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Generating artificial displacement data of cracked specimen using physics-guided adversarial networks.

David Melching, Erik Schultheis, Eric Breitbarth

Mach. Learn. Sci. Technol., December, 2023

Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 500k Labels on a Single Commodity GPU.

Erik Schultheis, Rohit Babbar

CoRR, 2023

Physics-guided adversarial networks for artificial digital image correlation data generation.

David Melching, Erik Schultheis, Eric Breitbarth

CoRR, 2023

Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU.

Erik Schultheis, Rohit Babbar

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Generalized test utilities for long-tail performance in extreme multi-label classification.

Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Speeding-up one-versus-all training for extreme classification via mean-separating initialization.

Erik Schultheis, Rohit Babbar

Mach. Learn., 2022

CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification.

Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification.

Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Extreme Multicore Classification.

Erik Schultheis, Rohit Babbar

Proceedings of the Machine Learning under Resource Constraints - Volume 1: Fundamentals, 2022

2021

Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization.

Erik Schultheis, Rohit Babbar

CoRR, 2021

Unbiased Loss Functions for Multilabel Classification with Missing Labels.

Erik Schultheis, Rohit Babbar

CoRR, 2021

Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels.

Mohammadreza Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar

Proceedings of the WWW '21: The Web Conference 2021, 2021

2020

Unbiased Loss Functions for Extreme Classification With Missing Labels.

Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta, Rohit Babbar

CoRR, 2020