Hello, I am an Associate Professor (Senior Lecturer) at the Computer Science department of the University of Bath, UK. With my group, we are working on exciting problems on sparse and memory/energy efficient neural networks for Large Language Models and large output spaces. I am also looking for motivated students to join my group at the University of Bath, UK. Please reach out at rb2608--AT--bath.ac.uk, if you are interested in joining!
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Selected Publications (Google scholar)
ELMO: Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces (code)
Jinbin Zhang, Nasib Ullah, Erik Schultheis, and Rohit Babbar
42nd International Conference on Machine Learning, ICML 2025, Vancouver, CanadaUniDEC: Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification (pdf)
Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Cho-Jui Hsieh, and Rohit Babbar
The Web Conference, WebConf 2025, Sydney, AustraliaLabels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers? ( pdf , code )
Nasib Ullah, Erik Schultheis, Jinbin Zhang and Rohit Babbar
31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025, Toronto, CanadaLarge Language Model as a Teacher for Zero-shot Tagging at Extreme Scales ( pdf , code )
Jinbin Zhang, Nasib Ullah, and Rohit Babbar
31st International Conference on Computational Linguistics, COLING, 2025, Abu-Dhabi, UAENavigating Extremes : Dynamic Sparsity in Large Output Spaces ( pdf , code )
Nasib Ullah, Erik Schultheis, Mike Lasby, Yani Ioannou and Rohit Babbar
38th Conference on Neural Information Processing Systems, NeurIPS, 2024, Vancouver, CanadaGandalf : Learning Label-label correlations in Extreme Multi-label Classification via Label Features (pdf )
Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, and Rohit Babbar
30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, SpainA General Online Algorithm for Optimizing Complex Performance Metrics
Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, and Krzysztof Dembczyński
41st International Conference on Machine Learning, ICML 2024, Vienna, AustriaConsistent Algorithms for Multi-label Classification with Macro@k Metrics ( pdf , code )
Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Strom Borman, and Krzysztof Dembczyński
12th International Conference on Learning Representations, ICLR 2024, Vienna, AustriaGeneralized Test Utilities for Long-tail Performance in Extreme Multi-label Classification ( pdf, code )
Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, and Krzysztof Dembczyński
37th Conference on Neural Information Processing Systems, NeurIPS, 2023, New Orleans, USATowards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU
Erik Schultheis, Rohit Babbar
ECML-PKDD 2023, (pre-print, code ), Torino, ItalyInceptionXML : A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification (pdf , code )
Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta, Akash Palrecha, and Rohit Babbar
46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, TaiwanCascadeXML : Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-Label Classification (pdf , code),
Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis and Rohit Babbar
36th Conference on Neural Information Processing Systems, NeurIPS, 2022, New Orleans, USAOn Missing Labels, Long tails and propensities in Extreme Multi-label Classification (pdf , code)
Erik Schultheis, Marek Wydmuch, Rohit Babbar, and Krzysztof Dembczyński
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, USAAdversarial Examples for Extreme Multi-label Classification, (pdf, code)
Mohamaadreza Qaraei, Rohit Babbar, Machine Learning Journal 2022Speeding-up One-vs-All Training for Extreme Classification via Mean-separating Initialization, (pdf, Code)
Erik Schultheis, and Rohit Babbar
ECML & Machine Learning Journal Track, ECML-MLJ, 2022Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels (pdf, video, code)
Mohammadreza Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar
30th ACM International World Wide Web Conference, The WebConf (WWW) 2021
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