1 citations · 2 across the 8 of their papers we have counts for
10 papers
Falling Trees: A Model Class for Interpretable Risk Prioritization
Varun Babbar, Zachery Boner, Margo Seltzer +1
Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule lists (FRLs), which are orde…
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Yixiao Wang, Hayden McTavish, Varun Babbar +2
Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing w…
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
Zakk Heile, Hayden McTavish, Varun Babbar +2
Standard machine learning pipelines often admit many near-optimal models. These "Rashomon sets" pose a range of challenges and opportunities for uncertainty-aware, robust decision…
Near Optimal Decision Trees in a SPLIT Second
Varun Babbar, Hayden McTavish, Cynthia Rudin +1
Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is…
"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
Varun Babbar, Zhicheng Guo, Cynthia Rudin
The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-related challenges. A common issue a…
Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training
Xiaoying Zhi, Varun Babbar, Rundong Liu +4
The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutio…
Topical: Learning Repository Embeddings from Source Code using Attention
Agathe Lherondelle, Varun Babbar, Yash Satsangi +3
This paper presents Topical, a novel deep neural network for repository level embeddings. Existing methods, reliant on natural language documentation or naive aggregation technique…
On the Utility of Prediction Sets in Human-AI Teams
Varun Babbar, Umang Bhatt, Adrian Weller
Research on human-AI teams usually provides experts with a single label, which ignores the uncertainty in a model's recommendation. Conformal prediction (CP) is a well established…
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation
Antonios Georgiadis, Varun Babbar, Fran Silavong +2
Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysi…
Training a Task-Specific Image Reconstruction Loss
Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu +2
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution. The loss function should e…