Edge detection using fractional derivatives and information sets. - Vignesh Kothapalli; Shaveta Arora; Madasu Hanmandlu (J. Electronic Imaging, 2018)

Neural Collapse: A Review on Modelling Principles and Generalization. - Vignesh Kothapalli; Ebrahim Rasromani; Vasudev Awatramani (CoRR, 2022)

Randomized Schur Complement Views for Graph Contrastive Learning. - Vignesh Kothapalli (ICML, 2023)

A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks. - Vignesh Kothapalli; Tom Tirer; Joan Bruna (NeurIPS, 2023)

Neural Collapse: A Review on Modelling Principles and Generalization. - Vignesh Kothapalli (Trans. Mach. Learn. Res., 2023)

Kernel vs. Kernel: Exploring How the Data Structure Affects Neural Collapse. - Vignesh Kothapalli; Tom Tirer (CoRR, 2024)

Crafting Heavy-Tails in Weight Matrix Spectrum without Gradient Noise. - Vignesh Kothapalli; Tianyu Pang; Shenyang Deng; Zongmin Liu; Yaoqing Yang (CoRR, 2024)

Liger Kernel: Efficient Triton Kernels for LLM Training. - Pin-Lun Hsu; Yun Dai; Vignesh Kothapalli; Qingquan Song; Shao Tang; Siyu Zhu; Steven Shimizu; Shivam Sahni; Haowen Ning; Yanning Chen (CoRR, 2024)

CoT-ICL Lab: A Synthetic Framework for Studying Chain-of-Thought Learning from In-Context Demonstrations. - Vignesh Kothapalli; Hamed Firooz; Maziar Sanjabi (ACL (1), 2025)

Can Kernel Methods Explain How the Data Affects Neural Collapse? - Vignesh Kothapalli; Tom Tirer (Trans. Mach. Learn. Res., 2025)

360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation. - Hamed Firooz; Maziar Sanjabi; Adrian Englhardt; Aman Gupta; Ben Levine; Dre Olgiati; Gungor Polatkan; Iuliia Melnychuk; Karthik Ramgopal; Kirill Talanine; Kutta Srinivasan; Luke Simon; et al. (CoRR, 2025)

Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications. - Kayhan Behdin; Yun Dai; Ata Fatahi Baarzi; Aman Gupta; Qingquan Song; Shao Tang; Hejian Sang; Gregory Dexter; Sirou Zhu; Siyu Zhu; Tejas Dharamsi; Maziar Sanjabi; et al. (CoRR, 2025)

CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context Demonstrations. - Vignesh Kothapalli; Hamed Firooz; Maziar Sanjabi (CoRR, 2025)