Search Results for author: Jasjeet S. Sekhon

Found 6 papers, 4 papers with code

Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations

1 code implementation CVPR 2024 Chenyu You, Yifei Min, Weicheng Dai, Jasjeet S. Sekhon, Lawrence Staib, James S. Duncan

As a piloting study, this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation.

Contrastive Learning

Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator

1 code implementation27 Sep 2023 Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon

Deep learning research has uncovered the phenomenon of benign overfitting for overparameterized statistical models, which has drawn significant theoretical interest in recent years.

Causal Inference

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

2 code implementations5 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan

In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.

Contrastive Learning Image Segmentation +2

Hybridized Threshold Clustering for Massive Data

no code implementations5 Jul 2019 Jianmei Luo, ChandraVyas Annakula, Aruna Sai Kannamareddy, Jasjeet S. Sekhon, William Henry Hsu, Michael Higgins

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

no code implementations23 Aug 2018 Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel

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.

Causal Inference Transfer Learning

Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

6 code implementations12 Jun 2017 Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, Bin Yu

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies.

Statistics Theory Methodology Statistics Theory

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