About Me
MS/PhD in Computer Science at UMass Amherst.
I am an MS/PhD student in Computer Science advised by Prof. Andrew McCallum. I am a member of Information Extraction and Synthesis Laboratory. I am broadly interested in natural language processing and machine learning. A bit more specifically, I am interested in methods for learning representation of entities, in structured prediction models, and clustering.
I received my bachelors in Computer Science from IIT Delhi in 2017 where I worked with Prof. Amitabha Bagchi and Prof. Subodh Vishnu Sharma. I was a research intern at Big Data Experience Labs, Adobe, Bangalore in Summer 2016 where I worked with Sumit Shekhar.
Publications
- Event & Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum
To appear in Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC) at EMNLP 2021. - Session-Aware Query Auto-completion using Extreme Multi-Label Ranking
Nishant Yadav, Rajat Sen, Daniel N. Hill, Arya Mazumdar, Inderjit S. Dhillon
KDD 2021. [arXiv] [ code] [ talk] [ blog ] - Clustering-based Inference for Zero-Shot Biomedical Entity Linking
Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum
NAACL 2021. [arXiv] - Stochastic Package Queries in Probabilistic Databases
Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou
SIGMOD 2020. [arXiv] - Supervised Hierarchical Clustering using Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum
International Conference of Machine Learning (ICML) 2019 [Supp] [arXiv] [Code] - Dynamic Partition Bloom Filters: A Bounded False Positive Solution For Dynamic Set Membership (Extended Abstract)
Sidharth Negi, Ameya Dubey, Amitabha Bagchi, Manish Yadav, Nishant Yadav, Jeetu Raj
arXiv preprint 2019 [Link] - VoCoG: An Intelligent, Non-Intrusive Assistant for Voice-based Collaborative Group-Viewing
Sumit Shekhar, Nishant Yadav, Anindya Shankar Bhandari, Aditya Siddhant
arXiv preprint 2018 [Link]
Updates
- (Sep'21) Paper on joint clustering for event and entity coreference accepted in Computational Models of Reference, Anaphora and Coreference Workshop at EMNLP 2021.
- (Summer'21) Interned with team MIDAS, part of Amazon Search, where I worked with Dan Hill, and Inderjit Dhillon on improving query-autocompletion models using extreme multi-label ranking (XMR) and on generative XMR models.
- (May'21) Paper titled "Session-Aware Query Auto-completion using Extreme Multi-Label Ranking" accepted in KDD 2021.
- (April'21) Paper on Zero-Shot Biomedical Entity Linking accepted at NAACL 2021.
- (Summer'20) Interned with team MIDAS, part of Amazon Search, where I worked with Rajat Sen , Dan Hill, Arya Mazumdar, and Inderjit Dhillon.
- (Jan'20) Paper titled "Stochastic Package Queries in Probabilistic Databases" accepted in SIGMOD 2020.
- (Dec'19) Passed PhD Portfolio exam!
- (Jun'19) Implementation of Exponential linkage (proposed in our ICML'19 paper) now available as part of Higra, a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods. Thanks to the developers of Higra for adding implemenation of exponential linkage to Higra!
- (Jun'19) Presented my paper titled "Supervised Hierarchical Clustering with Exponential Linkage" at ICML 2019