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Erik Schultheis
Erik Schultheis
PhD Candidate, Aalto University
Verified email at aalto.fi
Title
Cited by
Cited by
Year
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels
M Qaraei, E Schultheis, P Gupta, R Babbar
Proceedings of the Web Conference 2021, 3711-3720, 2021
28*2021
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
E Schultheis, M Wydmuch, R Babbar, K Dembczynski
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
212022
Channeling of branched flow in weakly scattering anisotropic media
H Degueldre, JJ Metzger, E Schultheis, R Fleischmann
Physical Review Letters 118 (2), 024301, 2017
192017
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
S Kharbanda, A Banerjee, E Schultheis, R Babbar
Advances in Neural Information Processing Systems 35, 2074-2087, 2022
172022
Speeding-up one-versus-all training for extreme classification via mean-separating initialization
E Schultheis, R Babbar
Machine Learning, 1-24, 2022
14*2022
Unbiased Loss Functions for Multilabel Classification with Missing Labels
E Schultheis, R Babbar
arXiv preprint arXiv:2109.11282, 2021
32021
Towards Memory-Efficient Training for Extremely Large Output Spaces–Learning with 670k Labels on a Single Commodity GPU
E Schultheis, R Babbar
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023
22023
Generalized test utilities for long-tail performance in extreme multi-label classification
E Schultheis, M Wydmuch, W Kotlowski, R Babbar, K Dembczynski
Advances in Neural Information Processing Systems 36, 2024
12024
Consistent algorithms for multi-label classification with macro-at- metrics
E Schultheis, W Kotłowski, M Wydmuch, R Babbar, S Borman, ...
arXiv preprint arXiv:2401.16594, 2024
2024
Gandalf: Learning label correlations in Extreme Multi-label Classification via Label Features
S Kharbanda, D Gupta, E Schultheis, A Banerjee, V Verma, CJ Hsieh, ...
2023
Gandalf: Data Augmentation is all you need for Extreme Classification
S Kharbanda, D Gupta, E Schultheis, A Banerjee, V Verma, R Babbar
2022
Beyond Standard Performance Measures in Extreme Multi-label Classification
E Schultheis, M Wydmuch, R Babbar, K Dembczyński
Workshop on Online and Adaptive Recommender Systems, 2022
2022
Unbiased Estimates for Multilabel Reductions of Extreme Classification with Missing Labels
E Schultheis, R Babbar
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