9 papers
Efficient Weighted Sampling via Score-based Generative Models
Heasung Kim, Taekyun Lee, Hyeji Kim +1
Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-rang…
CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization
Seohyeon Cha, Huancheng Chen, Dongjun Kim +4
Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats without retraining, typically using a small…
Online Learning for Multi-Layer Hierarchical Inference under Partial and Policy-Dependent Feedback
Haoran Zhang, Seohyeon Cha, Hasan Burhan Beytur +3
Hierarchical inference systems route tasks across multiple computational layers, where each node may either finalize a prediction locally or offload the task to a node in the next…
Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
Hasan Burhan Beytur, Gustavo de Veciana, Haris Vikalo +1
We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provi…
Inferring Causal Relationships to Improve Caching for Clients with Correlated Requests: Applications to VR
Agrim Bari, Gustavo de Veciana, Yuqi Zhou
Efficient edge caching reduces latency and alleviates backhaul congestion in modern networks. Traditional caching policies, such as Least Recently Used (LRU) and Least Frequently U…
Batching-Aware Joint Model Onloading and Offloading for Hierarchical Multi-Task Inference
Seohyeon Cha, Kevin Chan, Gustavo de Veciana +1
The growing demand for intelligent services on resource-constrained edge devices has spurred the development of collaborative inference systems that distribute workloads across end…