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Erik Schultheis
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2020 – today
- 2024
- [c9]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 - [c8]Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski:
A General Online Algorithm for Optimizing Complex Performance Metrics. ICML 2024 - [c7]Siddhant Kharbanda
, Devaansh Gupta
Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. KDD 2024: 1360-1371 - [c6]Erik Schultheis
LLaMA-Annotate - Visualizing Token-Level Confidences for LLMs. ECML/PKDD (8) 2024: 424-428 - [i11]Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski:
Consistent algorithms for multi-label classification with macro-at-k metrics. CoRR abs/2401.16594 (2024) - [i10]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 abs/2405.04545 (2024) - [i9]Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski:
A General Online Algorithm for Optimizing Complex Performance Metrics. CoRR abs/2406.14743 (2024) - 2023
- [j2]David Melching
Generating artificial displacement data of cracked specimen using physics-guided adversarial networks. Mach. Learn. Sci. Technol. 4(4): 45063 (2023) - [c5]Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski:
Generalized test utilities for long-tail performance in extreme multi-label classification. NeurIPS 2023 - [c4]Erik Schultheis
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU. ECML/PKDD (3) 2023: 689-704 - [i8]David Melching, Erik Schultheis, Eric Breitbarth:
Physics-guided adversarial networks for artificial digital image correlation data generation. CoRR abs/2303.15939 (2023) - [i7]Erik Schultheis, Rohit Babbar:
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 500k Labels on a Single Commodity GPU. CoRR abs/2306.03725 (2023) - [i6]Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski:
Generalized test utilities for long-tail performance in extreme multi-label classification. CoRR abs/2311.05081 (2023) - 2022
- [j1]Erik Schultheis
Speeding-up one-versus-all training for extreme classification via mean-separating initialization. Mach. Learn. 111(11): 3953-3976 (2022) - [c3]Erik Schultheis
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. KDD 2022: 1547-1557 - [c2]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 - [p1]Erik Schultheis, Rohit Babbar:
Extreme Multicore Classification. Mach. Learn. under Resour. Constraints Vol. 1 (1) 2022: 249-285 - [i5]Erik Schultheis
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. CoRR abs/2207.13186 (2022) - [i4]Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. CoRR abs/2211.00640 (2022) - 2021
- [c1]Mohammadreza Qaraei, Erik Schultheis
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels. WWW 2021: 3711-3720 - [i3]Erik Schultheis, Rohit Babbar:
Unbiased Loss Functions for Multilabel Classification with Missing Labels. CoRR abs/2109.11282 (2021) - [i2]Erik Schultheis, Rohit Babbar:
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization. CoRR abs/2109.13122 (2021) - 2020
- [i1]Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta, Rohit Babbar:
Unbiased Loss Functions for Extreme Classification With Missing Labels. CoRR abs/2007.00237 (2020)
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