Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given $k$ data sources, the goal is to output a classifier for each source by exploiting shared structure to reduce sample complexity. We focus on the bounded label noise setting to determine whether the fast $1/ε$ rates achievable in single-task learning extend to this regime with minimal dependence on $k$. Surprisingly, we show that this is not the case. We demonstrate […]

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arXiv:2601.11701v1 Announce Type: cross Abstract: Algorithmic stability is a central concept in statistics and learning theory that measures how sensitive an algorithm’s output is to small changes in the training data. Stability plays a crucial role in understanding generalization, robustness, and replicability, and a variety of stability notions have been proposed in different learning settings. However, while stability entails desirable properties, it is typically not sufficient on its own for statistical learning — and indeed, it may be […]

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arXiv:2604.18667v1 Announce Type: new Abstract: We present linear-space data structures for several frequency queries on trees, namely: path mode, path least frequent element, and path $alpha$-minority queries. We present the first linear-space data structures, requiring $O(n sqrt{nw})$ preprocessing time, that can answer path mode and path least frequent element queries in $O(sqrt{n/w})$ time. This improves upon the best previously known bound of $O(loglog n sqrt{n/w})$ achieved by Durocher et al. in 2016. For the path $alpha$-minority problem, where […]

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Table of Links Abstract and 1. Introduction Related Works 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Methodology 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2 5.1 Preprocessing Phase 5.2 RL Training and Application Phase Experiments 6.1 Experimental Setting 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.4 Key Insights Conclusion and Future Work, and References 5.1 Preprocessing Phase The preprocessing phase is […]

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arXiv:2505.17133v2 Announce Type: replace Abstract: Probabilities of causation play a central role in modern decision making. Tian and Pearl first introduced formal definitions and derived tight bounds for three binary probabilities of causation, such as the probability of necessity and sufficiency (PNS). However, estimating these probabilities requires both experimental and observational distributions specific to each subpopulation, which are often unreliable or impractical to obtain from limited population-level data. To solve this problem, we propose two machine learning models: […]

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