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Noise-Adaptive Thompson Sampling for Linear Contextual Bandits
Linear contextual bandits represent a fundamental class of models with numerous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance, ensuring provable guarantees for both worst-case constant-variance noise and deterministic reward scenarios.
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- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
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- North America > United States > Minnesota > Hennepin County > Minneapolis (0.14)
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John Ellipsoids via Lazy Updates
The John ellipsoid problem has far-reaching applications in numerous fields of computer science.
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Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
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Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
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- Europe > Italy > Lazio > Rome (0.04)
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