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Logarithmic High-Probability Regret for Strongly Convex Online Convex Optimization with Two-Point Bandit Feedback

Logarithmic High-Probability Regret for Strongly Convex Online Convex Optimization with Two-Point Bandit Feedback

Add by Lucas Martin

RoboStream Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics

RoboStream Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics

Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos

Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos

Stable Inversion of Discrete-Time Linear Periodically Time-Varying Systems via Cyclic Reformulation

Stable Inversion of Discrete-Time Linear Periodically Time-Varying Systems via Cyclic Reformulation

ഇന്ത്യയിലെ തീരദേശങ്ങൾ, ദ്വീപുകൾ, മരുഭൂമികൾ

ഇന്ത്യയിലെ തീരദേശങ്ങൾ, ദ്വീപുകൾ, മരുഭൂമികൾ

Tucker Tensor Train Taylor Series

Tucker Tensor Train Taylor Series

Science and Technology Hot Topics

Science and Technology Hot Topics

Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation

Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation

Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators

From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators

Adaptive Encoding Strategy for Quantum Annealing in Mixed-Variable Engineering Optimization

Adaptive Encoding Strategy for Quantum Annealing in Mixed-Variable Engineering Optimization

LUMOS Latent Universal Medical Priors for Segmentation

LUMOS Latent Universal Medical Priors for Segmentation

An Approximation Notion between P and FPTAS

An Approximation Notion between P and FPTAS

Any to Full: Prompting Depth Anything for Depth Completion in One Stage

Any to Full: Prompting Depth Anything for Depth Completion in One Stage

Learning Lineage-guided Geodesics with Finsler Geometry

Learning Lineage-guided Geodesics with Finsler Geometry