Statistical Learning
Differentiable sorting for censored time-to-event data
This bias becomes particularly noticeable when the study's endpoint can be inferred from the
In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development
Despite diverse modeling techniques, existing models are black boxes and fail to provide insights and explanations about their representations.
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.
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Most previous work on federated learning assumes that clients possess static batches of training data. However, clients may also need to make real-time predictions on streaming data in non-stationary environments.
On the Overlooked Structure of Stochastic Gradients Zeke Xie
Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).
A Kernel Perspective on Distillation-based Collaborative Learning
To tackle this problem, we rigorously analyze a representative distillation-based algorithm in the view of kernel regression.