Picture for Prasad Patil

Prasad Patil

Multi-study R-learner for Heterogeneous Treatment Effect Estimation

Add code
Jun 16, 2023
Viaarxiv icon

Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling

Add code
Jul 13, 2022
Figure 1 for Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Figure 2 for Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Figure 3 for Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Figure 4 for Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Viaarxiv icon

Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

Add code
Jun 20, 2020
Figure 1 for Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
Figure 2 for Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
Figure 3 for Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
Figure 4 for Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
Viaarxiv icon

Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects

Add code
May 17, 2019
Figure 1 for Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects
Figure 2 for Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects
Figure 3 for Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects
Figure 4 for Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects
Viaarxiv icon