58 citations · 96 across the 13 of their papers we have counts for
13 papers
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models
Sahil Verma, Royi Rassin, Arnav Das +6
Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models…
Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective
Sahil Verma, Gantavya Bhatt, Avi Schwarzschild +6
Despite the advanced capabilities of contemporary machine learning (ML) models, they remain vulnerable to adversarial and backdoor attacks. This vulnerability is particularly conce…
RecRec: Algorithmic Recourse for Recommender Systems
Sahil Verma, Ashudeep Singh, Varich Boonsanong +2
Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models…
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
Sahil Verma, Chirag Shah, John P. Dickerson +3
Recommender systems influence many of our interactions in the digital world -- impacting how we shop for clothes, sorting what we see when browsing YouTube or TikTok, and determini…
Pitfalls of Explainable ML: An Industry Perspective
Sahil Verma, Aditya Lahiri, John P. Dickerson +1
As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost…
Counterfactual Explanations for Machine Learning: Challenges Revisited
Sahil Verma, John Dickerson, Keegan Hines
Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form `…
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models
Sahil Verma, Keegan Hines, John P. Dickerson
Explainable machine learning (ML) has gained traction in recent years due to the increasing adoption of ML-based systems in many sectors. Algorithmic Recourses (ARs) provide "what…
Removing biased data to improve fairness and accuracy
Sahil Verma, Michael Ernst, Rene Just
Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data wil…
ShapeFlow: Dynamic Shape Interpreter for TensorFlow
Sahil Verma, Zhendong Su
We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code. Sh…
Debug-Localize-Repair: A Symbiotic Construction for Heap Manipulations
Sahil Verma, Subhajit Roy
We present Wolverine2, an integrated Debug-Localize-Repair environment for heap manipulating programs. Wolverine2 provides an interactive debugging environment: while concretely ex…
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Sahil Verma, Varich Boonsanong, Minh Hoang +3
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understa…
Facets of Fairness in Search and Recommendation
Sahil Verma, Ruoyuan Gao, Chirag Shah
Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions. Counteracting this bias and bringing a certain amount of fairness…
Benchmarking Symbolic Execution Using Constraint Problems -- Initial Results
Sahil Verma, Roland H. C. Yap
Symbolic execution is a powerful technique for bug finding and program testing. It is successful in finding bugs in real-world code. The core reasoning techniques use constraint so…