4 citations · 9 across the 8 of their papers we have counts for
10 papers
Does Execution Require Target KV Fidelity? A Mixed-Fidelity KV Runtime for LLM Serving
Jiantong Jiang, Yue Yang, Peiyu Yang +1
Large language model (LLM) serving is increasingly constrained by the GPU memory consumed by key-value (KV) caches. Existing compression, eviction, and offloading techniques allevi…
Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
Jiantong Jiang, Peiyu Yang, Rui Zhang +1
Despite the rapid advancements of large language models (LLMs), LLM serving systems remain memory-intensive and costly. The key-value (KV) cache, which stores KV tensors during aut…
Coordinated Scheduling for MoE LLM Serving
Yifan Sun, Zhexiang Zhang, Jiantong Jiang +5
Serving Mixture-of-Experts (MoE) large language models (LLMs) is challenging because dynamic request workloads interact with sparse expert routing, creating both data-parallel (DP)…
Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors
Peiyu Yang, Naveed Akhtar, Jiantong Jiang +1
The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to their opaque nature, rectifying models…
FLASH Viterbi: Fast and Adaptive Viterbi Decoding for Modern Data Systems
Ziheng Deng, Xue Liu, Jiantong Jiang +3
The Viterbi algorithm is a key operator for structured sequence inference in modern data systems, with applications in trajectory analysis, online recommendation, and speech recogn…
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification
Jiantong Jiang, Ajmal Mian
Hyperparameter optimization (HPO) and neural architecture search (NAS) are powerful in attaining state-of-the-art machine learning models, with Bayesian optimization (BO) standing…
Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference
Jiantong Jiang, Zeyi Wen, Peiyu Yang +2
Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex systems with uncertainty and extracting valuable insights from data. However, users face ch…
A Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attributions
Peiyu Yang, Naveed Akhtar, Jiantong Jiang +1
Attribution methods compute importance scores for input features to explain model predictions. However, assessing the faithfulness of these methods remains challenging due to the a…
Fast Parallel Bayesian Network Structure Learning
Jiantong Jiang, Zeyi Wen, Ajmal Mian
Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require perf…
Fast Parallel Exact Inference on Bayesian Networks: Poster
Jiantong Jiang, Zeyi Wen, Atif Mansoor +1
Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for comp…