activity
20132023
most citedExplaining Machine Learning Classifiers through Diverse Counterfactual Explanations

1.2k citations · 1.9k across the 21 of their papers we have counts for

collaborators

22 papers

cond-mat.mtrl-sci2023★ 19 cited

Combinatorial Reactive Sputtering with Auger Parameter Analysis Enables Synthesis of Wurtzite Zn2TaN3

Siarhei Zhuk, Alexander Wieczorek, Amit Sharma +4

The discovery of new functional materials is one of the key challenges in materials science. Combinatorial high-throughput approaches using reactive sputtering are commonly employe…

cs.IR2022

Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems

Parikshit Bansal, Yashoteja Prabhu, Emre Kiciman +1

Given a user's input text, text-matching recommender systems output relevant items by comparing the input text to available items' description, such as product-to-product recommend…

cs.LG2020★ 78 cited

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma

In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label. We show that this objective…

cs.SI2020★ 19 cited

Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums

Taisa Kushner, Amit Sharma

Recent years have seen a rise in social media platforms that provide peer-to-peer support to individuals suffering from mental distress. Studies on the impact of these platforms ha…

cs.SI2020★ 6 cited

Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms

Ashish Sharma, Monojit Choudhury, Tim Althoff +1

Mental illness is a global health problem, but access to mental healthcare resources remain poor worldwide. Online peer-to-peer support platforms attempt to alleviate this fundamen…

cs.LG2019★ 102 cited

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Divyat Mahajan, Chenhao Tan, Amit Sharma

To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to…