Search Results for author: Nishant Yadav

Found 11 papers, 3 papers with code

Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

no code implementations CRAC (ACL) 2021 Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum

Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.

SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents

no code implementations EMNLP (newsum) 2021 Nishant Yadav, Matteo Brucato, Anna Fariha, Oscar Youngquist, Julian Killingback, Alexandra Meliou, Peter Haas

Several datasets exist for summarization with objective intents where, for each document and intent (e. g., “weather”), a single summary suffices for all users.

Extractive Summarization

CDA: Contrastive-adversarial Domain Adaptation

no code implementations10 Jan 2023 Nishant Yadav, Mahbubul Alam, Ahmed Farahat, Dipanjan Ghosh, Chetan Gupta, Auroop R. Ganguly

Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains.

Contrastive Learning Domain Adaptation

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization

1 code implementation23 Oct 2022 Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum

When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods.

Retrieval

Robustness of Explanation Methods for NLP Models

no code implementations24 Jun 2022 Shriya Atmakuri, Tejas Chheda, Dinesh Kandula, Nishant Yadav, Taesung Lee, Hessel Tuinhof

Explanation methods have emerged as an important tool to highlight the features responsible for the predictions of neural networks.

Adversarial Attack Adversarial Robustness

Stochastic Package Queries in Probabilistic Databases

no code implementations11 Mar 2021 Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou

We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides.

Decision Making Decision Making Under Uncertainty +1 Databases

Session-Aware Query Auto-completion using Extreme Multi-label Ranking

1 code implementation9 Dec 2020 Nishant Yadav, Rajat Sen, Daniel N. Hill, Arya Mazumdar, Inderjit S. Dhillon

Previous queries in the user session can provide useful context for the user's intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user's prefix.

Clustering-based Inference for Biomedical Entity Linking

no code implementations NAACL 2021 Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum

In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.

Entity Linking

Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling

no code implementations12 Aug 2020 Nishant Yadav, Sai Ravela, Auroop R. Ganguly

In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such as cloud physics, which in turn have witnessed limited improvements over the last several decades.

BIG-bench Machine Learning Gaussian Processes

Supervised Hierarchical Clustering with Exponential Linkage

1 code implementation19 Jun 2019 Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum

Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function.

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