Search Results for author: Prateek Yadav

Found 11 papers, 9 papers with code

Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning

1 code implementation ACL 2022 Swarnadeep Saha, Prateek Yadav, Mohit Bansal

In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs.

Contrastive Learning Graph Generation +1

Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

1 code implementation1 Nov 2021 Prateek Yadav, Peter Hase, Mohit Bansal

Current approaches try to optimize for the cost incurred by users when adopting a recourse, but they assume that all users share the same cost function.

Fairness

multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning

1 code implementation NAACL 2021 Swarnadeep Saha, Prateek Yadav, Mohit Bansal

In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph.

Multi-Label Classification

ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning

1 code implementation EMNLP 2021 Swarnadeep Saha, Prateek Yadav, Lisa Bauer, Mohit Bansal

Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context.

Graph Generation Multiple-choice +1

HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

1 code implementation NeurIPS 2019 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

1 code implementation24 Jan 2019 Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar

Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph.

HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

1 code implementation7 Sep 2018 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.

Lovasz Convolutional Networks

1 code implementation29 May 2018 Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar

We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.

Multi-class Classification

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