A Primal Dual Formulation For Deep Learning With Constraints. - Yatin Nandwani; Abhishek Pathak; Mausam; Parag Singla (NeurIPS, 2019)

OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion. - Yatin Nandwani; Ankesh Gupta; Aman Agrawal; Mayank Singh Chauhan; Parag Singla; Mausam (AKBC, 2020)

Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces. - Yatin Nandwani; Deepanshu Jindal; Mausam; Parag Singla (ICLR, 2021)

CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion. - Keshav Kolluru; Mayank Singh Chauhan; Yatin Nandwani; Parag Singla; Mausam (CoRR, 2021)

Neural Models for Output-Space Invariance in Combinatorial Problems. - Yatin Nandwani; Vidit Jain; Mausam; Parag Singla (ICLR, 2022)

A Solver-free Framework for Scalable Learning in Neural ILP Architectures. - Yatin Nandwani; Rishabh Ranjan; Mausam; Parag Singla (NeurIPS, 2022)

Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs. - Yatin Nandwani; Vineet Kumar; Dinesh Raghu; Sachindra Joshi; Luis A. Lastras (EMNLP, 2023)

Neuro-Symbolic Architectures for Combinatorial Problems in Structured Output Spaces. - Yatin Nandwani; Mausam; Parag Singla (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Few shot chain-of-thought driven reasoning to prompt LLMs for open-ended medical question answering. - Saeel Sandeep Nachane; Ojas Gramopadhye; Prateek Chanda; Ganesh Ramakrishnan; Kshitij Sharad Jadhav; Yatin Nandwani; Dinesh Raghu; Sachindra Joshi (EMNLP (Findings), 2024)

BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback. - Gaurav Pandey 001; Yatin Nandwani; Tahira Naseem; Mayank Mishra; Guangxuan Xu; Dinesh Raghu; Sachindra Joshi; Asim Munawar; Ramón Fernandez Astudillo (ICML, 2024)

Matching papers and reviewers at large conferences. - Kevin Leyton-Brown; Mausam; Yatin Nandwani; Hedayat Zarkoob; Chris Cameron; Neil Newman; Dinesh Raghu (Artif. Intell., 2024)

Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models. - Sonam Gupta 001; Yatin Nandwani; Asaf Yehudai; Mayank Mishra; Gaurav Pandey 001; Dinesh Raghu; Sachindra Joshi (CoRR, 2024)

Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG. - Kushagra Bhushan; Yatin Nandwani; Dinesh Khandelwal; Sonam Gupta 001; Gaurav Pandey 001; Dinesh Raghu; Sachindra Joshi (NAACL (Findings), 2025)

Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models. - Sonam Gupta 001; Yatin Nandwani; Asaf Yehudai; Dinesh Khandelwal; Dinesh Raghu; Sachindra Joshi (NAACL (Findings), 2025)

A Library of LLM Intrinsics for Retrieval-Augmented Generation. - Marina Danilevsky; Kristjan Greenewald; R. Chulaka Gunasekara; Maeda F. Hanafi; Lihong He 001; Yannis Katsis; Krishnateja Killamsetty; Yatin Nandwani; Lucian Popa 001; Dinesh Raghu; Frederick Reiss 001; Vraj Shah; et al. (CoRR, 2025)