Convex Two-Layer Modeling with Latent Structure. - Vignesh Ganapathiraman; Xinhua Zhang; Yaoliang Yu; Junfeng Wen (NIPS, 2016)

Inductive Two-layer Modeling with Parametric Bregman Transfer. - Vignesh Ganapathiraman; Zhan Shi; Xinhua Zhang; Yaoliang Yu (ICML, 2018)

Learning Invariant Representations with Kernel Warping. - Yingyi Ma; Vignesh Ganapathiraman; Xinhua Zhang (AISTATS, 2019)

Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. - Yingyi Ma; Vignesh Ganapathiraman; Yaoliang Yu; Xinhua Zhang (ICML, 2020)

BANDIT SAMPLING FOR FASTER NEURAL NETWORK TRAINING WITH SGD. - Vignesh Ganapathiraman; Francisco Javier Calderon; Anila Joshi (Tiny Papers @ ICLR, 2023)

Learned Learning Rate Schedules for Deep Neural Network Training Using Reinforcement Learning. - Shreyas Subramanian; Vignesh Ganapathiraman; Aly El Gamal (Tiny Papers @ ICLR, 2023)

Efficient Learning rate schedules for Stochastic Non-negative Matrix Factorization via Reinforcement Learning. - Shreyas Subramanian; Vignesh Ganapathiraman; Aly El Gamal (Tiny Papers @ ICLR, 2023)

Hop, skip, jump to Convergence: Dynamics of Learning Rate Transitions for Improved Training of Large Language Models. - Shreyas Subramanian; Vignesh Ganapathiraman; Corey Barrett (EMNLP (Findings), 2024)

BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks. - Amrutha Varshini Ramesh; Vignesh Ganapathiraman; Issam H. Laradji; Mark Schmidt 001 (CoRR, 2024)