Statistical Learning
0d288dbe7a757672151aa5fb5423ef2e-Paper-Conference.pdf
Exp. Psychology, Oxford Brain Mind Institute, EPFL Jan P .
On f-Divergence Principled Domain Adaptation: An Improved Framework
Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning. In this work, we improve the theoretical foundations of UDA proposed in Acuna et al. (2021) by refining their
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
We give exact expressions for the risk and learning rate curves in terms of a deterministic solution to a system of ODEs.
Cell ontology guided transcriptome foundation model
Cells are basic units of all living organisms.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Intent learning, which aims to learn users' intents for user understanding and
Trade-Offs of Diagonal Fisher Information Matrix Estimators
The Fisher information matrix can be used to characterize the local geometry of the parameter space of neural networks.