1 code implementation • ICLR 2018 • Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, Sunita Sarawagi
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains.
Ranked #83 on Domain Generalization on PACS
1 code implementation • ACL 2019 • Vihari Piratla, Sunita Sarawagi, Soumen Chakrabarti
Given a small corpus $\mathcal D_T$ pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite of the limited size of $\mathcal D_T$.
1 code implementation • IJCNLP 2019 • Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, Vihari Piratla
We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC).
Ranked #13 on Grammatical Error Correction on CoNLL-2014 Shared Task
Grammatical Error Correction Optical Character Recognition (OCR)
2 code implementations • ICML 2020 • Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
The domain specific components are discarded after training and only the common component is retained.
Ranked #1 on Domain Generalization on LipitK
no code implementations • 9 Jul 2020 • Vihari Piratla, Shiv Shankar
It is believed that by processing augmented inputs in tandem with the original ones, the model learns a more robust set of features which are shared between the original and augmented counterparts.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Sahil Shah, Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi
Each client uses an unsupervised, corpus-based sketch to register to the service.
no code implementations • 7 Feb 2021 • Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat, Varun Bhatt, Harshith Goka, Archit Gupta, Kalpesh Krishna, Vihari Piratla
In this paper, we investigate the role of the parameter $d$ in RL; $d$ is called the "frame-skip" parameter, since states in the Atari domain are images.
1 code implementation • NeurIPS 2021 • Vihari Piratla, Soumen Chakrabarty, Sunita Sarawagi
Our goal is to evaluate the accuracy of a black-box classification model, not as a single aggregate on a given test data distribution, but as a surface over a large number of combinations of attributes characterizing multiple test data distributions.
1 code implementation • NeurIPS 2021 • Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, Sunita Sarawagi
In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions.
1 code implementation • ICLR 2022 • Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
We consider the problem of training a classification model with group annotated training data.
1 code implementation • 2 Nov 2022 • Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller
We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration.
no code implementations • 21 Nov 2022 • Shiv Shankar, Vihari Piratla
Most deep learning research has focused on developing new model and training procedures.
no code implementations • 5 Mar 2023 • Vihari Piratla
While we improve robustness over standard training methods for certain problem settings, performance of ML systems can still vary drastically with domain shifts.
1 code implementation • NeurIPS 2023 • Juyeon Heo, Vihari Piratla, Matthew Wicker, Adrian Weller
Machine learning from explanations (MLX) is an approach to learning that uses human-provided explanations of relevant or irrelevant features for each input to ensure that model predictions are right for the right reasons.
1 code implementation • 13 Dec 2023 • Vihari Piratla, Juyeon Heo, Katherine M. Collins, Sukriti Singh, Adrian Weller
We believe the improved quality of uncertainty-aware concept explanations make them a strong candidate for more reliable model interpretation.