most citedWhen Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation

3 citations · 4 across the 5 of their papers we have counts for

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5 papers

cs.AI2025

MPrune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation

Weizi Shao, Taolin Zhang, Zijie Zhou +3

Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that th…

cs.LG2025★ 1 cited

Online Scheduling for LLM Inference with KV Cache Constraints

Patrick Jaillet, Jiashuo Jiang, Konstantina Mellou +3

Large Language Model (LLM) inference, where a trained model generates text one word at a time in response to user prompts, is a computationally intensive process requiring efficien…

cs.DB2024

Exploring Distance Query Processing in Edge Computing Environments

Xiubo Zhang, Yujie He, Ye Li +4

In the context of changing travel behaviors and the expanding user base of Geographic Information System (GIS) services, conventional centralized architectures responsible for hand…

math.OC2024★ 3 cited

When Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation

Patrick Jaillet, Chara Podimata, Andrew Vakhutinsky +1

In this work, we study an upgrading scheme for online resource allocation problems. We work in a sequential setting, where at each round a request for a resource arrives and the de…

cs.GT2024

Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management

Patrick Jaillet, Chara Podimata, Zijie Zhou

Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? An…