Erik Schultheis

Doctoral Researcher
Doctoral Researcher
T313 Dept. Computer Science
Full researcher profile
https://research.aalto.fi/...
Sähköposti
[email protected]

Palkinnot

NeurIPS 2021 Outstanding Reviewer Award

Award or honor granted for a specific work School common, SCI Oct 2021

Julkaisut

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

A General Online Algorithm for Optimizing Complex Performance Metrics

Wojciech Kotłowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczyński 2024 Proceedings of Machine Learning Research

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

Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski 2024 Advances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023

Extreme Multicore Classification

Erik Schultheis, Rohit Babbar 2023 Machine Learning under Resource Constraints

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

Erik Schultheis, Rohit Babbar 2023 Machine Learning and Knowledge Discovery in Databases

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

Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar 2022 Advances in Neural Information Processing Systems 35 (NeurIPS 2022)

Beyond Standard Performance Measures in Extreme Multi-label Classification

Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski 2022

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

Erik Schultheis, Rohit Babbar, Marek Wydmuch, Krzysztof Dembczynski 2022 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining