1 citations · 1 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits
Ashish Khisti, M. Reza Ebrahimi, Hassan Dbouk +3
We consider multi-draft speculative sampling, where the proposal sequences are sampled independently from different draft models. At each step, a token-level draft selection scheme…
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…
Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption
Shuangyi Chen, Yue Ju, Zhongwen Zhu +1
We propose a secure inference protocol for a distributed setting involving a single server node and multiple client nodes. We assume that the observed data vector is partitioned ac…
SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy
Shuangyi Chen, Ashish Khisti
In the context of prediction-as-a-service, concerns about the privacy of the data and the model have been brought up and tackled via secure inference protocols. These protocols are…
Importance Matching Lemma for Lossy Compression with Side Information
Buu Phan, Ashish Khisti, Christos Louizos
We propose two extensions to existing importance sampling based methods for lossy compression. First, we introduce an importance sampling based compression scheme that is a variant…