Fed-SVT is nearly optimal up to logarithmic factors.

Unconditional generation--the problem of modeling data distribution without relying on human-annotated labels--is a long-standing and fundamental challenge in generative models, creating a potential of learning from large-scale unlabeled data. In the literature, the generation quality of an unconditional method has been much worse than that of its conditional counterpart. This gap can be attributed to the lack of semantic information provided by labels. In this work, we show that one can close this gap by generating semantic representations in the representation space produced by a self-supervised encoder. These representations can be used to condition the image generator.

For heterophilic graphs, achieving balance based on relative gradients leads to faster training and better generalization. In contrast, homophilic graphs benefit from delaying the learning of later layers.

Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost.

In this work, we introduce a new approach for visualizing posteriors across multiple levels of granularity using tree -valued predictions. Our method predicts a tree-valued hierarchical summarization of the posterior distribution for any input measurement, in a single forward pass of a neural network.

To address these challenges, we introduce a novel adaptive framework that allows finer adjustments to the weight vector in P A using side information.

OFUGLB outperforms or is at par with prior algorithms for logistic bandits.

Intelligent agents must be generalists, capable of quickly adapting to various tasks.

In our work, we focus on smooth convex or strongly convex problems in the interpolation regime.

Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data.