Statistical Learning
Selective Sampling and Imitation Learning via Online Regression
We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback.
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the label-noise problem from a generative-model perspective, i.e., labels are generated by gradually refining an initial random guess.
Appendix: On the Overlooked Structure of Stochastic Gradients
Avila is a non-image dataset. A.3 Image classification on MNIST We perform the common per-pixel zero-mean unit-variance normalization as data preprocessing for MNIST. Pretraining Hyperparameter Settings: We train neural networks for 50 epochs on MNIST for obtaining pretrained models. The batch size is set to 1 and no weight decay is used, unless we specify them otherwise. As for other optimizer hyperparameters, we apply the default settings directly.