2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing
Elad Romanov, Fangzhao Zhang, Mert Pilanci
Motivated by recent advances in serverless cloud computing, in particular the "function as a service" (FaaS) model, we consider the problem of minimizing a convex function in a mas…
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
Erica Zhang, Fangzhao Zhang, Mert Pilanci
Active learning methods aim to improve sample complexity in machine learning. In this work, we investigate an active learning scheme via a novel gradient-free cutting-plane trainin…
Spectral Adapter: Fine-Tuning in Spectral Space
Fangzhao Zhang, Mert Pilanci
Recent developments in Parameter-Efficient Fine-Tuning (PEFT) methods for pretrained deep neural networks have captured widespread interest. In this work, we study the enhancement…
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
Fangzhao Zhang, Mert Pilanci
Low-Rank Adaptation (LoRA) emerges as a popular parameter-efficient fine-tuning (PEFT) method, which proposes to freeze pretrained model weights and update an additive low-rank tra…
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
Fangzhao Zhang, Mert Pilanci
Diffusion models are gaining widespread use in cutting-edge image, video, and audio generation. Score-based diffusion models stand out among these methods, necessitating the estima…