1 citations · 1 across the 12 of their papers we have counts for
8 papers · 1 filter
Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy
Joseph Rowan, Buu Phan, Ashish J. Khisti
Channel simulation has recently emerged as a useful component in machine learning systems where samples from a prescribed probability distribution are to be compressed. Yet, genera…
Multi-Bitwidth Quantization for LLMs Using Additive Codebooks
Liza Babaoglu, Shuangyi Chen, Ashish Khisti
As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively manage the performance-efficien…
Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence
Shuangyi Chen, Ashish Khisti
We study black-box detection of machine-generated text under practical constraints: the scoring model (proxy LM) may mismatch the unknown source model, and per-input contrastive ge…
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…
Minimum Entropy Coupling with Bottleneck
M. Reza Ebrahimi, Jun Chen, Ashish Khisti
This paper investigates a novel lossy compression framework operating under logarithmic loss, designed to handle situations where the reconstruction distribution diverges from the…
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…