Data Programming using Semi-Supervision and Subset Selection. - Ayush Maheshwari; Oishik Chatterjee; KrishnaTeja Killamsetty; Rishabh K. Iyer; Ganesh Ramakrishnan (CoRR, 2020)

Robust Semi-Supervised Learning with Out of Distribution Data. - Xujiang Zhao; KrishnaTeja Killamsetty; Rishabh K. Iyer; Feng Chen 001 (CoRR, 2020)

A Reweighted Meta Learning Framework for Robust Few Shot Learning. - KrishnaTeja Killamsetty; Changbin Li; Chen Zhao 010; Rishabh K. Iyer; Feng Chen 001 (CoRR, 2020)

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning. - KrishnaTeja Killamsetty; Durga Sivasubramanian; Ganesh Ramakrishnan; Rishabh K. Iyer (AAAI, 2021)

Semi-Supervised Data Programming with Subset Selection. - Ayush Maheshwari; Oishik Chatterjee; KrishnaTeja Killamsetty; Ganesh Ramakrishnan; Rishabh K. Iyer (ACL/IJCNLP (Findings), 2021)

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training. - KrishnaTeja Killamsetty; Durga Sivasubramanian; Ganesh Ramakrishnan; Abir De; Rishabh K. Iyer (ICML, 2021)

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning. - KrishnaTeja Killamsetty; Xujiang Zhao; Feng Chen 001; Rishabh K. Iyer (NeurIPS, 2021)

SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios. - Suraj Kothawade; Nathan Beck; KrishnaTeja Killamsetty; Rishabh K. Iyer (NeurIPS, 2021)

GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning. - KrishnaTeja Killamsetty; Durga Sivasubramanian; Baharan Mirzasoleiman; Ganesh Ramakrishnan; Abir De; Rishabh K. Iyer (CoRR, 2021)

A Nested Bi-level Optimization Framework for Robust Few Shot Learning. - KrishnaTeja Killamsetty; Changbin Li; Chen Zhao 010; Feng Chen 001; Rishabh K. Iyer (AAAI, 2022)

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming. - Ayush Maheshwari; KrishnaTeja Killamsetty; Ganesh Ramakrishnan; Rishabh K. Iyer; Marina Danilevsky; Lucian Popa 001 (ACL (Findings), 2022)

GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning. - Rishabh Tiwari; KrishnaTeja Killamsetty; Rishabh K. Iyer; Pradeep Shenoy (CVPR, 2022)

How Out-of-Distribution Data Hurts Semi-Supervised Learning. - Xujiang Zhao; KrishnaTeja Killamsetty; Rishabh K. Iyer; Feng Chen 001 (ICDM, 2022)

ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift. - Athresh Karanam; KrishnaTeja Killamsetty; Harsha Kokel; Rishabh K. Iyer (NeurIPS, 2022)

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning. - KrishnaTeja Killamsetty; Guttu Sai Abhishek; Aakriti; Ganesh Ramakrishnan; Alexandre V. Evfimievski; Lucian Popa 001; Rishabh K. Iyer (NeurIPS, 2022)

INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models. - H. S. V. N. S. Kowndinya Renduchintala; Krishnateja Killamsetty; Sumit Bhatia; Milan Aggarwal; Ganesh Ramakrishnan; Rishabh K. Iyer; Balaji Krishnamurthy (EMNLP (Findings), 2023)

MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning. - KrishnaTeja Killamsetty; Alexandre V. Evfimievski; Tejaswini Pedapati; Kiran Kate; Lucian Popa 001; Rishabh K. Iyer (CoRR, 2023)

INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models. - H. S. V. N. S. Kowndinya Renduchintala; Krishnateja Killamsetty; Sumit Bhatia; Milan Aggarwal; Ganesh Ramakrishnan; Rishabh K. Iyer; Balaji Krishnamurthy (CoRR, 2023)

SCoRe: Submodular Combinatorial Representation Learning for Real-World Class-Imbalanced Settings. - Anay Majee; Suraj Kothawade; Krishnateja Killamsetty; Rishabh K. Iyer (CoRR, 2023)

SCoRe: Submodular Combinatorial Representation Learning. - Anay Majee; Suraj Kothawade; Krishnateja Killamsetty; Rishabh K. Iyer (ICML, 2024)

Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification. - Nathan Beck; Krishnateja Killamsetty; Suraj Kothawade; Rishabh K. Iyer (WACV, 2024)

DELIFT: Data Efficient Language model Instruction Fine Tuning. - Ishika Agarwal; Krishnateja Killamsetty; Lucian Popa 001; Marina Danilevksy (CoRR, 2024)

DELIFT: Data Efficient Language model Instruction Fine-Tuning. - Ishika Agarwal; Krishnateja Killamsetty; Lucian Popa 001; Marina Danilevsky (ICLR, 2025)

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs. - Aldo Pareja; Nikhil Shivakumar Nayak; Hao Wang; Krishnateja Killamsetty; Shivchander Sudalairaj; Wenlong Zhao 001; Seungwook Han; Abhishek Bhandwaldar; Guangxuan Xu; Kai Xu 016; Ligong Han; Luke Inglis; et al. (ICLR, 2025)

Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning. - Nikhil Shivakumar Nayak; Krishnateja Killamsetty; Ligong Han; Abhishek Bhandwaldar; Prateek Chanda; Kai Xu 016; Hao Wang; Aldo Pareja; Oleg Silkin; Mustafa Eyceoz; Akash Srivastava (CoRR, 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)

Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning. - Prateek Chanda; Saral Sureka; Parth Pratim Chatterjee; Krishnateja Killamsetty; Nikhil Shivakumar Nayak; Ganesh Ramakrishnan (CoRR, 2025)