500 citations · 617 across the 11 of their papers we have counts for
16 papers
Game-theoretic Counterfactual Explanation for Graph Neural Networks
Chirag Chhablani, Sarthak Jain, Akshay Channesh +2
Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, maki…
Probabilistic Group Testing in Distributed Computing with Attacked Workers
Sarthak Jain, Martina Cardone, Soheil Mohajer
The problem of distributed matrix-vector product is considered, where the server distributes the task of the computation among worker nodes, out of which are compromised (b…
How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
Jinghan Yang, Sarthak Jain, Byron C. Wallace
We consider the problem of identifying a minimal subset of training data such that if the instances comprising had been removed prior to training, t…
Influence Functions for Sequence Tagging Models
Sarthak Jain, Varun Manjunatha, Byron C. Wallace +1
Many language tasks (e.g., Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling) are naturally framed as sequence tagging problems. However, there has been…
Modular Self-Supervision for Document-Level Relation Extraction
Sheng Zhang, Cliff Wong, Naoto Usuyama +3
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining…
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
Eric Lehman, Sarthak Jain, Karl Pichotta +2
Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of trai…
An Empirical Comparison of Instance Attribution Methods for NLP
Pouya Pezeshkpour, Sarthak Jain, Byron C. Wallace +1
Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging. Instance attribution methods constit…
SciREX: A Challenge Dataset for Document-Level Information Extraction
Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi +1
Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It i…
Learning to Faithfully Rationalize by Construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter +1
In many settings it is important for one to be able to understand why a model made a particular prediction. In NLP this often entails extracting snippets of an input text `responsi…
ERASER: A Benchmark to Evaluate Rationalized NLP Models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani +4
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased inte…
SUPP.AI: Finding Evidence for Supplement-Drug Interactions
Lucy Lu Wang, Oyvind Tafjord, Arman Cohan +5
Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI,…
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
Ramin Mohammadi, Sarthak Jain, Stephen Agboola +3
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension man…
An Analysis of Attention over Clinical Notes for Predictive Tasks
Sarthak Jain, Ramin Mohammadi, Byron C. Wallace
The shift to electronic medical records (EMRs) has engendered research into machine learning and natural language technologies to analyze patient records, and to predict from these…
Attention is not Explanation
Sarthak Jain, Byron C. Wallace
Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equippe…
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Sarthak Jain, Edward Banner, Jan-Willem van de Meent +2
We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and inte…
Structured Disentangled Representations
Babak Esmaeili, Hao Wu, Sarthak Jain +6
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disen…