Erik Schultheis

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 20

Top Topics

Single Commodity

Binary Classification

Top Venues

Publications

Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar
Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. KDD (2024)
Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski
Consistent algorithms for multi-label classification with macro-at-k metrics. ICLR (2024)
Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski
Consistent algorithms for multi-label classification with macro-at-k metrics. CoRR (2024)
Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar
Learning label-label correlations in Extreme Multi-label Classification via Label Features. CoRR (2024)
Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski
A General Online Algorithm for Optimizing Complex Performance Metrics. CoRR (2024)
Erik Schultheis, Rohit Babbar
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 500k Labels on a Single Commodity GPU. CoRR (2023)
David Melching, Erik Schultheis, Eric Breitbarth
Generating artificial displacement data of cracked specimen using physics-guided adversarial networks. Mach. Learn. Sci. Technol. 4 (4) (2023)
Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski
Generalized test utilities for long-tail performance in extreme multi-label classification. CoRR (2023)
David Melching, Erik Schultheis, Eric Breitbarth
Physics-guided adversarial networks for artificial digital image correlation data generation. CoRR (2023)
Erik Schultheis, Rohit Babbar
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU. ECML/PKDD (3) (2023)
Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski
Generalized test utilities for long-tail performance in extreme multi-label classification. NeurIPS (2023)
Erik Schultheis, Rohit Babbar
Speeding-up one-versus-all training for extreme classification via mean-separating initialization. Mach. Learn. 111 (11) (2022)
Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. NeurIPS (2022)
Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. CoRR (2022)
Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. CoRR (2022)
Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. KDD (2022)
Erik Schultheis, Rohit Babbar
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization. CoRR (2021)
Erik Schultheis, Rohit Babbar
Unbiased Loss Functions for Multilabel Classification with Missing Labels. CoRR (2021)
Mohammadreza Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels. WWW (2021)
Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta, Rohit Babbar
Unbiased Loss Functions for Extreme Classification With Missing Labels. CoRR (2020)