49 citations · 101 across the 20 of their papers we have counts for
7 papers · 1 filter
Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
Pura Peetathawatchai, Wei-Ning Chen, Berivan Isik +2
Personalizing large-scale diffusion models poses serious privacy risks, especially when adapting to small, sensitive datasets. A common approach is to fine-tune the model using dif…
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
Ashwinee Panda, Berivan Isik, Xiangyu Qi +3
Existing methods for adapting large language models (LLMs) to new tasks are not suited to multi-task adaptation because they modify all the model weights -- causing destructive int…
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations
Rylan Schaeffer, Victor Lecomte, Dhruv Bhandarkar Pai +10
Maximum Manifold Capacity Representations (MMCR) is a recent multi-view self-supervised learning (MVSSL) method that matches or surpasses other leading MVSSL methods. MMCR is intri…
Improved Communication-Privacy Trade-offs in Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen, Berivan Isik, Peter Kairouz +3
We study mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simu…
On Fairness of Low-Rank Adaptation of Large Models
Zhoujie Ding, Ken Ziyu Liu, Pura Peetathawatchai +2
Low-rank adaptation of large models, particularly LoRA, has gained traction due to its computational efficiency. This efficiency, contrasted with the prohibitive costs of full-mode…
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
Onur G. Guleryuz, Philip A. Chou, Berivan Isik +6
We propose sandwiching standard image and video codecs between pre- and post-processing neural networks. The networks are jointly trained through a differentiable codec proxy to mi…