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ARXIV:2603.18157 · CLUSTERING ALGORITHMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18157CLUSTERING ALGORITHMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEAnish Hebbar · Rong Ge · Amit Kumar · Debmalya Panigrahi · arXiv
An online learning framework for the k-median problem that adapts to dynamically changing data sequences.
Opportunity summary
Pain An online learning framework for the k-median problem that adapts to dynamically changing data sequences.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
An online learning framework for the k-median problem that adapts to dynamically changing data sequences. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning.
The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We also experimentally evaluate our algorithm and show that its empirical performance is close to optimal, and also that it automatically adapts the solution…
Clustering Algorithms moved forward this cycle; last verified April 2026. Public score 3.0/10.
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An online learning framework for the k-median problem that adapts to dynamically changing data sequences.
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10.48550/arXiv.2603.18157An online learning framework for the k-median problem that adapts to dynamically changing data sequences.
Abstract
The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning. In this model, we are given a sequence of instances of a problem and the goal of the learning-augmented algorithm is to use prior instances to propose a solution to a future instance of the problem. The performance of the algorithm is measured by its average performance across all the instances, where the performance on a single instance is the ratio between the cost of the algorithm's solution and that of an optimal solution for that instance. We apply this framework to the classic $k$-median clustering problem, and give an efficient learning algorithm that can approximately match the average performance of the best fixed $k$-median solution in hindsight across all the instances. We also experimentally evaluate our algorithm and show that its empirical performance is close to optimal, and also that it automatically adapts the solution to a dynamically changing sequence.
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What was readable
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PROBLEM
An online learning framework for the k-median problem that adapts to dynamically changing data sequences. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning.
METHOD
The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We also experimentally evaluate our algorithm and show that its empirical performance is close to optimal, and also that it automatically adapts the solution to a dynamically changing sequence.
WHY NOW
Clustering Algorithms moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An online learning framework for the k-median problem that adapts to dynamically changing data sequences. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We also experimentally evaluate our algorithm and show that its empirical performance is close to optimal, and also that it automatically adapts the solution to a dynamically changing sequence.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Clustering Algorithms moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An online learning framework for the k-median problem that adapts to dynamically changing data sequences.
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Clustering Algorithms
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