5 papers
Channel Simulation and Distributed Compression with Ensemble Rejection Sampling
Buu Phan, Ashish Khisti
We study channel simulation and distributed matching, two fundamental problems with several applications to machine learning, using a recently introduced generalization of the stan…
List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression
Joseph Rowan, Buu Phan, Ashish Khisti
We study a relaxation of the problem of coupling probability distributions -- a list of samples is generated from one distribution and an accept is declared if any one of these sam…
On Self-Adaptive Perception Loss Function for Sequential Lossy Compression
Sadaf Salehkalaibar, Buu Phan, Likun Cai +4
We consider causal, low-latency, sequential lossy compression, with mean squared-error (MSE) as the distortion loss, and a perception loss function (PLF) to enhance the realism of…
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
Shuangyi Chen, Yuanxin Guo, Yue Ju +3
Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) optimize federated training by reducing computational and communication costs. We propose RoLoRA, a f…
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA
Shuangyi Chen, Yue Ju, Hardik Dalal +2
Parameter-Efficient Fine-Tuning (PEFT) has risen as an innovative training strategy that updates only a select few model parameters, significantly lowering both computational and m…