Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels.

Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

While many techniques for detecting these attacks have been proposed, theyareeasily bypassed when theadversary hasfullknowledge of the detection mechanism and adapts the attack strategy accordingly. In this paper,we adopt anovel perspectiveand regard the omnipresence of adversarial perturbations asastrength rather thanaweakness.

Neural Information Processing Systems http://nips.cc/

Among these, diffusion classifiers, utilizing powerful diffusion models, have demonstrated superior empirical robustness.

Neural Information Processing Systems http://nips.cc/

We investigate algorithmic principles that enable efficient data deletion in ML.