Prateek Yadav

According to our database, Prateek Yadav authored at least 30 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A cost-efficient content distribution optimization model for fog-based content delivery networks.

Prateek Yadav, Subrat Kar

J. Cloud Comput., December, 2024

Efficient Content Distribution in Fog-Based CDN: A Joint Optimization Algorithm for Fog-Node Placement and Content Delivery.

Prateek Yadav, Subrat Kar

IEEE Internet Things J., May, 2024

INSPIRE: Incorporating Diverse Feature Preferences in Recourse.

Prateek Yadav, Peter Hase, Mohit Bansal

Trans. Mach. Learn. Res., 2024

Glider: Global and Local Instruction-Driven Expert Router.

Pingzhi Li, Prateek Yadav, Jaehong Yoon, Jie Peng, Yi-Lin Sung, Mohit Bansal, Tianlong Chen

CoRR, 2024

What Matters for Model Merging at Scale?

Prateek Yadav, Tu Vu, Jonathan Lai, Alexandra Chronopoulou, Manaal Faruqui, Mohit Bansal, Tsendsuren Munkhdalai

CoRR, 2024

A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning.

Prateek Yadav, Colin Raffel, Mohammed Muqeeth, Lucas Caccia, Haokun Liu, Tianlong Chen, Mohit Bansal, Leshem Choshen, Alessandro Sordoni

CoRR, 2024

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, David Lo, Binyuan Hui, Niklas Muennighoff, Daniel Fried, Xiaoning Du, Harm de Vries, Leandro von Werra

CoRR, 2024

Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order.

Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Minh Chien Vu, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin P. Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Junior, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Moustafa-Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, Sampo Pyysalo

CoRR, 2024

D2 Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning.

Adyasha Maharana, Prateek Yadav, Mohit Bansal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy.

Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization.

Prateek Yadav, Leshem Choshen, Colin Raffel, Mohit Bansal

CoRR, 2023

D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning.

Adyasha Maharana, Prateek Yadav, Mohit Bansal

CoRR, 2023

Resolving Interference When Merging Models.

Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, Mohit Bansal

CoRR, 2023

Self-Chained Image-Language Model for Video Localization and Question Answering.

Shoubin Yu, Jaemin Cho, Prateek Yadav, Mohit Bansal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

TIES-Merging: Resolving Interference When Merging Models.

Prateek Yadav, Derek Tam, Leshem Choshen, Colin A. Raffel, Mohit Bansal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Exploring Continual Learning for Code Generation Models.

Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023

Exclusive Supermask Subnetwork Training for Continual Learning.

Prateek Yadav, Mohit Bansal

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

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

Swarnadeep Saha, Prateek Yadav, Mohit Bansal

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions.

Prateek Yadav, Peter Hase, Mohit Bansal

CoRR, 2021

multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning.

Swarnadeep Saha, Prateek Yadav, Mohit Bansal

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning.

Swarnadeep Saha, Prateek Yadav, Lisa Bauer, Mohit Bansal

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020

Evaluating the Impact of Region Based Content Popularity of Videos on the Cost of CDN Deployment.

Prateek Yadav, Subrat Kar

Proceedings of the 2020 National Conference on Communications, 2020

NHP: Neural Hypergraph Link Prediction.

Naganand Yadati, Vikram Nitin, Madhav Nimishakavi, Prateek Yadav, Anand Louis, Partha P. Talukdar

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019

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

Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha P. Talukdar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Lovasz Convolutional Networks.

Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Pratim Talukdar

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning.

Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha P. Talukdar

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks.

Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha P. Talukdar

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018

Graph Convolutional Networks based Word Embeddings.

Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Piyush Rai, Chiranjib Bhattacharyya, Partha P. Talukdar

CoRR, 2018

HyperGCN: Hypergraph Convolutional Networks for Semi-Supervised Classification.

Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Anand Louis, Partha Pratim Talukdar

CoRR, 2018

Lovasz Convolutional Networks.

Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Arun Rajkumar, Partha Pratim Talukdar

CoRR, 2018