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Published in Journal 1, 2009
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Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Published in Journal 1, 2010
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Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Published in Journal 1, 2015
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Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in European Conference on Artificial Intelligence (ECAI), 2025
This paper presents iHOMER, a novel framework for online multi-label learning in dynamic environments. iHOMER is the first incremental, drift-aware algorithm that partitions the label space into correlated clusters without requiring predefined hierarchies. It adapts to concept drift over time, balances scalability and predictive performance, and employs statistical tests to guide both splitting and merging of label clusters.
Published in Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop (ICLR 2026), 2026
Accepted in ICLR 2026 — Best Poster Award CAO Workshop
Published in arXiv, 2026
SR²AM equips a single LLM with internal “System I” (reactive), “System II” (simulative planning via a learned world model), and “System III” (self-regulation over planning depth and action vs. simulation). A configurator determines when and how far to simulate, enabling 30B models to rival 685B, 1T models at a fraction of the token cost. Thinking longer doesn’t always mean thinking smarter, SR²AM lets the LLM know when to simulate ahead, when to act directly, and when to balance both for optimal performance.
Project website: https://sailing-lab.github.io/sr2am-self-regulated-planning/
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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