arXiv AI
Query-efficient winner prediction in district-based elections
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Randomized Algorithms for Learning Partitions with Near Optimal Query Complexity in Constant Rounds
arXiv:2608. 02176v1 Announce Type: cross Abstract: We study the round complexity of learning a hidden partition $\mathcal{P}$ of an $n$-element universe using PAIR queries: PAIR($x,y$) tells us whether $x$ and $y$ belong to the same part of the partition or not.
arXiv Machine Learning
Sep 16
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
The paper introduces a derandomization framework for stochastic majority vote classifiers, converting PAC‑Bayesian guarantees into deterministic majority vote guarantees. By applying disintegrated PAC‑Bayesian theory to the space of vote weight vectors, the authors derive two families of high‑probability generalization bounds for both data‑independent and data‑dependent ensembles. These bounds naturally lead to a self‑bounding learning algorithm that optimizes deterministic majority vote performance.
arXiv AI
Sep 1
Improving Randomized Metric Distortion to 2.3282
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arXiv Machine Learning
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Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction
arXiv:2608. 06262v1 Announce Type: new Abstract: Model evaluations may fix all tests before observing any responses or select later tests using earlier responses.
arXiv Machine Learning
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Is Randomness Necessary for Adaptive Data Analysis?
arXiv:2607. 07085v1 Announce Type: cross Abstract: The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused.
arXiv Machine Learning
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On Randomized Algorithms in Online Strategic Classification
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.