Prateek Yadav

Interpretability, Robustness, NLP, Structured Prediction, Representation learning.

prateek@cs.unc.edu

Hey! I am a PhD student at the MURGe-Lab at the University of North Carolina - Chapel Hill, where I work with Prof. Mohit Bansal. My research goal is to make deep learning models and their evaluation procedures more scalable, robust, interpretable and reusable. I like to understand and exploit structures and invariances in the data to develop efficient models with limited supervision. Previously, I have worked on a diverse set of topics, (1) deep learning methods for Graph and Hypergraph structured data and their application to NLP, (2) estimated and controlled for uncertanity in the learned representations from these methods, (3) worked on Bayesian modeling of temporal data and (4) model based Reasoning in NLP.

Over the past few years, I have been fortunate to work with Prof. Partha Talukdar at MALL-Lab at Indian Institute of Science (IISc) Bangalore, with Dr. Prateek Jain at Microsoft Research India and with Prof. Arun Rajkumar at Indian Institute of Technology, Madras. I also worked for a year with some amazing people at LinkedIn AI Bangalore. Before all this, I completed my undergraduate degree in pure mathematics in 2018 from IISc Bangalore where I was supervised by Prof. Partha Talukdar.

Check out my publication page to know more about my current research. I am also actively involved in improving the way science is taught to students, through simple experiments and conducting workshops via Notebook Drive.

Selected Publications

  1. Pre-print
    Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
    Yadav, Prateek, Hase, Peter, and Bansal, Mohit
    2021
  2. EMNLP [ORAL]
    ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
    Saha, Swarnadeep, Yadav, Prateek, Bauer, Lisa, and Bansal, Mohit
    In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2021
  3. NAACL [ORAL]
    multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
    Saha, Swarnadeep, Yadav, Prateek, and Bansal, Mohit
    In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2021
  4. NeurIPS
    HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
    Yadati, Naganand, Nimishakavi, Madhav, Yadav, Prateek, Nitin, Vikram, Louis, Anand, and Talukdar, Partha
    In Advances in Neural Information Processing Systems 2019
  5. AISTATS
    Lovasz Convolutional Networks
    Yadav, Prateek, Nimishakavi, Madhav, Yadati, Naganand, Vashishth, Shikhar, Rajkumar, Arun, and Talukdar, Partha
    In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics 2019
  6. ACL
    Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
    Vashishth, Shikhar, Yadav, Prateek*, Bhandari, Manik*, Rai, Piyush, Bhattacharyya, Chiranjib, and Talukdar, Partha
    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019