Prateek Yadav

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Mohit BansalParker Distinguished Professor, Computer Science, UNC Chapel HillVerified email at cs.unc.edu
Partha TalukdarGoogle Research and IISc BangaloreVerified email at iisc.ac.in
Colin RaffelUniversity of Toronto, Vector Institute and Hugging FaceVerified email at cs.toronto.edu
Leshem ChoshenMIT, IBM AI researchVerified email at mail.huji.ac.il
Derek TamUniversity of TorontoVerified email at cs.toronto.edu
Shikhar VashishthResearch Scientist, Google DeepMindVerified email at google.com
Swarnadeep SahaResearch Scientist, FAIR at MetaVerified email at meta.com
Piyush RaiIIT KanpurVerified email at cse.iitk.ac.in
Chiranjib BhattacharyyaProfessor, Indian Institute of ScienceVerified email at csa.iisc.ernet.in
Tianlong ChenAssistant Professor, CS@UNC Chapel Hill; PostDoc, CSAIL@MIT+BMI@Harvard; Ph.D., ECE@UT AustinVerified email at cs.unc.edu
Jaemin ChoPhD Student at UNC Chapel HillVerified email at cs.unc.edu
Shoubin YuPhD Student at UNC Chapel HillVerified email at cs.unc.edu
Yi-Lin SungUNC Chapel HillVerified email at cs.unc.edu
Peter HaseResident, AnthropicVerified email at anthropic.com
Adyasha MaharanaUniversity of North Carolina, Chapel HillVerified email at cs.unc.edu
Arun rajkumarIndian institute of technology, MadrasVerified email at cse.iitm.ac.in
Murali Krishna RamanathanAmazon Web Services (AWS)Verified email at amazon.com
Ramesh Maruthi NallapatiPrincipal Applied Scientist, Amazon AWS AIVerified email at amazon.com
Bing XiangHead of AI Research, Goldman SachsVerified email at gs.com
Dejiao ZhangSenior Staff Research Scientist, Figma (Ex-AWS)Verified email at umich.edu

Prateek Yadav

PhD, University of North Carolina Chapel Hill

Verified email at cs.unc.edu - Homepage

Continual Learning MoE Model Merging Modular Network Efficient AI


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Hypergcn: A new method for training graph convolutional networks on hypergraphs N Yadati, M Nimishakavi, P Yadav, V Nitin, A Louis, P Talukdar NeurIPS 2019, 1511-1522, 2019	595	2019
TIES-merging: Resolving interference when merging models P Yadav, D Tam, L Choshen, C Raffel, M Bansal NeurIPS, 2024	327*	2024
Self-Chained Image-Language Model for Video Localization and Question Answering S Yu, J Cho, P Yadav, M Bansal NeurIPS 2023, 2023	159	2023
Graph convolutional networks based word embeddings P Yadav, S Vashishth, M Bhandari, P Rai, C Bhattacharyya, P Talukdar ACL 2019, 2018	138*	2018
NHP: Neural hypergraph link prediction N Yadati, V Nitin, M Nimishakavi, P Yadav, A Louis, P Talukdar CIKM 2020, 1705-1714, 2020	126*	2020
Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions TY Zhuo, MC Vu, J Chim, H Hu, W Yu, R Widyasari, INB Yusuf, H Zhan, ... arXiv preprint arXiv:2406.15877, 2024	80	2024
ExplaGraphs: An explanation graph generation task for structured commonsense reasoning S Saha, P Yadav, L Bauer, M Bansal EMNLP 2021, 2021	59	2021
Confidence-based graph convolutional networks for semi-supervised learning S Vashishth, P Yadav, M Bhandari, P Talukdar AISTATS 2019, 1792-1801, 2019	52	2019
Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning A Maharana, P Yadav, M Bansal The Twelfth International Conference on Learning Representations, 0	32*
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy P Li, Z Zhang, P Yadav, YL Sung, Y Cheng, M Bansal, T Chen ICLR 2024 (Spotlight), 2023	30	2023
Exploring continual learning for code generation models P Yadav, Q Sun, H Ding, X Li, D Zhang, M Tan, X Ma, P Bhatia, ... ACL 2023, 2023	29	2023
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning MB Swarnadeep Saha, Prateek Yadav NAACL 2021, 2021	29*	2021
INSPIRE: A Framework for Integrating Individual User Preferences in Recourse P Yadav, P Hase, M Bansal TMLR, 2022	20*	2022
A survey on model moerging: Recycling and routing among specialized experts for collaborative learning P Yadav, C Raffel, M Muqeeth, L Caccia, H Liu, T Chen, M Bansal, ... arXiv preprint arXiv:2408.07057, 2024	18	2024
Lovasz convolutional networks P Yadav, M Nimishakavi, N Yadati, S Vashishth, A Rajkumar, P Talukdar AISTATS 2019, 1978-1987, 2019	16	2019
Compeft: Compression for communicating parameter efficient updates via sparsification and quantization P Yadav, L Choshen, C Raffel, M Bansal arXiv preprint arXiv:2311.13171, 2023	13	2023
Explanation graph generation via pre-trained language models: An empirical study with contrastive learning S Saha, P Yadav, M Bansal ACL 2022, 2022	9	2022
What Matters for Model Merging at Scale? P Yadav, T Vu, J Lai, A Chronopoulou, M Faruqui, M Bansal, ... arXiv preprint arXiv:2410.03617, 2024	8	2024
Aurora-m: The first open source multilingual language model red-teamed according to the us executive order T Nakamura, M Mishra, S Tedeschi, Y Chai, JT Stillerman, F Friedrich, ... arXiv e-prints, arXiv: 2404.00399, 2024	8	2024
Llm merging: Building llms efficiently through merging D Tam, M Li, P Yadav, RB Gabrielsson, J Zhu, K Greenewald, ... NeurIPS 2024 Competition Track, 2024	5	2024

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