A loss-driven Bayesian active learning framework derives unique acquisition objectives from arbitrary losses, with analytic solutions available when the loss is a weighted Bregman divergence.
[Yes] (b) All the training details (e.g., data splits, hy- perparameters, how they were chosen)
4 Pith papers cite this work. Polarity classification is still indexing.
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Machine unlearning often creates an illusion of forgetting via feature-classifier misalignment, with hidden representations remaining discriminative; CMF-based methods enforce alignment for better representation-level unlearning.
ARB dynamically prioritizes replay buffer samples by on-policyness to balance stability and performance in offline-to-online RL.
AdaScale-TuRBO scales Gaussian process lengthscales with problem dimension D and trust region side length L to preserve kernel geometry and improve performance over standard TuRBO in high-dimensional settings.
citing papers explorer
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Loss-Driven Bayesian Active Learning
A loss-driven Bayesian active learning framework derives unique acquisition objectives from arbitrary losses, with analytic solutions available when the loss is a weighted Bregman divergence.
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An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
Machine unlearning often creates an illusion of forgetting via feature-classifier misalignment, with hidden representations remaining discriminative; CMF-based methods enforce alignment for better representation-level unlearning.
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Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
ARB dynamically prioritizes replay buffer samples by on-policyness to balance stability and performance in offline-to-online RL.
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Rethinking Trust Region Bayesian Optimization in High Dimensions
AdaScale-TuRBO scales Gaussian process lengthscales with problem dimension D and trust region side length L to preserve kernel geometry and improve performance over standard TuRBO in high-dimensional settings.