About Me
PhD in Computer Science at UMass Amherst.
I am a 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 representation learning methods, 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
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Interactive Correlation Clustering with Existential Cluster Constraints
Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum
ICML 2022.
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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] [bibtex]
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Event & Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum
CRAC Workshop at EMNLP 2021. 🏆 Best Paper Award.
[talk] [slides] [bibtex] -
SubSumE : A Dataset for Subjective Summary Extraction from Wikipedia Documents
Nishant Yadav*, Matteo Brucato*, Anna Fariha*, Oscar Youngquist, Julian Killingback, Alexandra Meliou, Peter J. Haas
NewSum Workshop at EMNLP 2021.
[data] [talk] [bibtex] -
Clustering-based Inference for Zero-Shot Biomedical Entity Linking
Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum
NAACL 2021.
[arXiv] [bibtex] -
Stochastic Package Queries in Probabilistic Databases
Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou
SIGMOD 2020.
[arXiv] [talk] [bibtex] -
Supervised Hierarchical Clustering using Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum
International Conference of Machine Learning (ICML) 2019
[supplementary] [arXiv] [code] [bibtex] -
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 won best paper award at Computational Models of Reference, Anaphora and Coreference Workshop, EMNLP 2021.
- (Sep'21) Dataset paper on subjective document summarization accepted in New Frontiers of Summarization 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