About Me
PhD in Computer Science at UMass Amherst.
I am a PhD student in Computer Science advised by Prof. Andrew McCallum. and 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, structured prediction models, clustering, nearest neighbor search and information retrieval.
I received my bachelors in Computer Science from IIT Delhi where I worked with Prof. Amitabha Bagchi and Prof. Subodh Vishnu Sharma. In the past, I was a research intern at Amazon Search, Berkley and Adobe Big Data Experience Labs, Bangalore.
Publications
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Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders
Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum
Under submission -
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition
Nishant Yadav, Nicholas Monath, Manzil Zaheer, Andrew McCallum
Findings of EMNLP 2023
[arXiv]
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Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction
Prakamya Mishra, Lincy Pattanaik, Arunima Sundar, Nishant Yadav, Mayank Kulkarni
Under submission
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Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum
EMNLP 2022.
[arXiv] [code] [pre-trained models]
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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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Robustness of Explanation Methods for NLP Models
Shriya Atmakuri, Tejas Chheda, Dinesh Kandula, Nishant Yadav, Taesung Lee, Hessel Tuinhof
Workshop on Trustworthy Artificial Intelligence at ECML/PKDD 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
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]