21 citations · 46 across the 8 of their papers we have counts for
9 papers
Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam +2
Covariate shift in the test data is a common practical phenomena that can significantly downgrade both the accuracy and the fairness performance of the model. Ensuring fairness acr…
Universality and Limitations of Prompt Tuning
Yihan Wang, Jatin Chauhan, Wei Wang +1
Despite the demonstrated empirical efficacy of prompt tuning to adapt a pretrained language model for a new task, the theoretical underpinnings of the difference between "tuning pa…
Multi-Variate Time Series Forecasting on Variable Subsets
Jatin Chauhan, Aravindan Raghuveer, Rishi Saket +2
We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is…
BERTops: Studying BERT Representations under a Topological Lens
Jatin Chauhan, Manohar Kaul
Proposing scoring functions to effectively understand, analyze and learn various properties of high dimensional hidden representations of large-scale transformer models like BERT c…
A Probabilistic Framework for Knowledge Graph Data Augmentation
Jatin Chauhan, Priyanshu Gupta, Pasquale Minervini
We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the…
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models
Jatin Chauhan, Karan Bhukar, Manohar Kaul
Despite significant improvements in natural language understanding models with the advent of models like BERT and XLNet, these neural-network based classifiers are vulnerable to bl…
Learning Representations using Spectral-Biased Random Walks on Graphs
Charu Sharma, Jatin Chauhan, Manohar Kaul
Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex…
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
Jatin Chauhan, Deepak Nathani, Manohar Kaul
We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. Despite several…
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma +1
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of re…