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Found 6 papers, 4 papers with code
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
As a piloting study, this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation.
Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator
Deep learning research has uncovered the phenomenon of benign overfitting for over-parameterized statistical models, which has drawn significant theoretical interest in recent years.
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.
Hybridized Threshold Clustering for Massive Data
Finally, a more sophisticated clustering algorithm is applied to the reduced prototype points, thereby obtaining a clustering on all $n$ data points.
Transfer Learning for Estimating Causal Effects using Neural Networks
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature.
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies.
Statistics Theory Methodology Statistics Theory
