analiza aiqbench
This research addresses a critical gap in online learning where classical change-point detection schemes fail catastrophically due to endogenous confounding in multi-shift environments. The authors introduce Anytime Tracking CUSUM (ATC), a horizon-free algorithm that selectively balances ignoring minor shifts with rapid reaction to significant ones, achieving near-minimax-optimal performance. Validated on both synthetic and real-world data, this work establishes a novel information-theoretic lower bound and offers a robust solution for dynamic learning scenarios.
The Cost of Learning Under Multiple Change Points
arXiv:2602.11406v2 Announce Type: replace-cross Abstract: We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical "high confidence" detection schemes, the multiple change point environment presents new learning-theoretic and algorithmic challenges. Specifically, we show that classical methods may exhibit catastrophic failure (high regret) due to a phenomenon we refer to as endogenous confounding. To overcome this, we propose a new class of learning algorithms dubbed Anytime Tracking CUSUM (ATC). These are horizon-free online algorithms that implement a selective detection principle, balancing the need to ignore "small" (hard-to-detect) shifts, while reacting "quickly" to significant ones. We prove that the performance of a properly tuned ATC algorithm is nearly minimax-optimal; its regret is guaranteed to closely match a novel information-theoretic lower bound on the achievable performance of any learning algorithm in the multiple change point problem. Experiments on synthetic as well as real-world data validate the aforementioned theoretical findings.
Abstract:We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical "high confidence" detection schemes, the multiple change point environment presents new learning-theoretic and algorithmic challenges. Specifically, we show that classical methods may exhibit catastrophic failure (high regret) due to a phenomenon we refer to as endogenous confounding. To overcome this, we propose a new class of learning algorithms dubbed Anytime Tracking CUSUM (ATC). These are horizon-free online algorithms that implement a selective detection principle, balancing the need to ignore "small" (hard-to-detect) shifts, while reacting "quickly" to significant ones. We prove that the performance of a properly tuned ATC algorithm is nearly minimax-optimal; its regret is guaranteed to closely match a novel information-theoretic lower bound on the achievable performance of any learning algorithm in the multiple change point problem. Experiments on synthetic as well as real-world data validate the aforementioned theoretical findings.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2602.11406 [stat.ML]
(or arXiv:2602.11406v2 [stat.ML] for this version)
Text integral sub licența deschisă a publicației · 171 cuvinte
Subiecte conexe
In-Context Multiple Instance Learning
arXiv · 6 iun. 2026
Goal-Conditioned Supervised Learning for Multi-Objective Recommendation
arXiv · 30 sept. 2026
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
arXiv · 3 aug. 2026
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
arXiv · 8 iun. 2026