Search Results for author:
Found 9 papers, 5 papers with code
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
Machine learning methods often assume that the test data have the same distribution as the training data.
Multi-dimensional domain generalization with low-rank structures
In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data.
Transferred Q-learning
We consider $Q$-learning with knowledge transfer, using samples from a target reinforcement learning (RL) task as well as source samples from different but related RL tasks.
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach
With only a small number of communications across participating sites, the proposed method can achieve performance comparable to the pooled analysis where individual-level data are directly pooled together.
Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.
Dynamically Corrected Nonadiabatic Holonomic Quantum Gates
The key for realizing fault-tolerant quantum computation lies in maintaining the coherence of all qubits so that high-fidelity and robust quantum manipulations on them can be achieved.
Quantum Physics
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies.
Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality
This paper considers the estimation and prediction of a high-dimensional linear regression in the setting of transfer learning, using samples from the target model as well as auxiliary samples from different but possibly related regression models.
