Zhaozhuo Xu

Email: zxu79 at stevens dot edu

I am starting as an Assistant Professor of Computer Science at Stevens Institute of Technology in Spring 2024. I received my Ph.D. in Computer Science from Rice University under the supervision of professor Anshumali Shrivastava. My research focuses on scaling up machine learning on commodity hardware using randomized algorithms.

Selected Publication

* indicates equal contribution

• Wentao Guo, Jikai Long, Yimeng Zeng, Zirui Liu, Xinyu Yang, Yide Ran, Jacob R. Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen and Zhaozhuo Xu, "Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity", ICLR 2025. PDF

• Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang and Zhaozhuo Xu, "ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Parity LLM Data Valuation", NAACL 2025 Oral. PDF

• Tianyi Zhang, Jonah Yi, Zhaozhuo Xu and Anshumali Shrivastava, "KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization", NeurIPS 2024. PDF

• Tianyi Zhang, Jonah Wonkyu Yi, Bowen Yao, Zhaozhuo Xu and Anshumali Shrivastava, "NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention", NeurIPS 2024. PDF Code

• Yang Zhou, Zhuoming Chen, Zhaozhuo Xu, Victoria Lin and Beidi Chen, "Sirius: Contextual Sparsity with Correction for Efficient LLMs", NeurIPS 2024. PDF Code

• Jialiang Xu, Shenglan Li, Zhaozhuo Xu and Denghui Zhang, "Do LLMs Know to Respect Copyright Notice?", EMNLP 2024. PDF Code

• Chuang Zhou, Junnan Dong, Xiao Huang, Zirui Liu, Kaixiong Zhou and Zhaozhuo Xu, "QUEST: Efficient Extreme Multi-Label Text Classification with Large Language Models on Commodity Hardware", Findings of EMNLP 2024. PDF

• Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu and Xia Hu, "KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches", Findings of EMNLP 2024. PDF Code

• Junda Su, Zirui Liu, Zeju Qiu, Weiyang Liu and Zhaozhuo Xu, "In Defense of Structural Sparse Adapters for Concurrent LLM Serving", Findings of EMNLP 2024. PDF

• Huawei Lin, Jikai Long, Zhaozhuo Xu and Weijie Zhao, "Token-wise Influential Training Data Retrieval for Large Language Models", ACL 2024. PDF Code

• Duy Le, Shaochen Zhong, Zirui Liu, Shuai Xu, Vipin Chaudhary, Kaixiong Zhou and Zhaozhuo Xu, "Knowledge Graphs Can be Learned with Just Intersection Features", ICML 2024. PDF

• Zhaozhuo Xu*, Zirui Liu*, Beidi Chen, Shaochen (Henry) Zhong, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu and Anshumali Shrivastava, "Soft Prompt Recovers Compressed LLMs, Transferably", ICML 2024. PDF Code

• Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen (Henry) Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu, "KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache", ICML 2024. PDF Code

• Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang, Shaochen Zhong, Zirui Liu, Zhaozhuo Xu, Kaixiong Zhou, Xuanting Cai and Xia Hu, "TVE: Learning Meta-attribution for Transferable Vision Explainer", ICML 2024. PDF Code

• Shaochen Zhong, Duy Le, Zirui Liu, Zhimeng Jiang, Andrew Ye, Jiamu Zhang, Jiayi Yuan, Kaixiong Zhou, Zhaozhuo Xu, Jing Ma, Shuai Xu, Vipin Chaudhary and Xia Hu, "GNNs Also Deserve Editing, and They Need It More Than Once", ICML 2024. PDF Code

• Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis and Anshumali Shrivastava, "Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time", NeurIPS 2023. PDF

• Zichang Liu*, Zhaozhuo Xu*, Benjamin Coleman and Anshumali Shrivastava, "One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning", NeurIPS 2023. PDF

• Zirui Liu*, Guanchu Wang*, Shaochen Zhong, Zhaozhuo Xu, Daochen Zha, Ruixiang Tang, Zhimeng Jiang, Kaixiong Zhou, Vipin Chaudhary, Shuai Xu and Xia Hu, "Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model", NeurIPS 2023. PDF

• Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu and Anshumali Shrivastava, "Graph Self-supervised Learning via Proximity Divergence Minimization", UAI 2023. PDF Code

• Zhuang Wang, Xinyu Wu, Zhaozhuo Xu and T. S. Eugene Ng, "Cupcake: A Compression Scheduler for Scalable Communication-Efficient Distributed Training", MLSys 2023. PDF

• Zhaozhuo Xu, Zhao Song and Anshumali Shrivastava, "A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space", AISTATS 2023. PDF

• Tianyi Zhang*, Zhaozhuo Xu*, Tharun Medini and Anshumali Shrivastava, "Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification", EMNLP Findings 2022. PDF Code

• Zhuang Wang*, Zhaozhuo Xu*, Xinyu Crystal Wu, Anshumali Shrivastava and T. S. Eugene Ng, "DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks", ICML 2022. PDF

• Zichang Liu*, Zhaozhuo Xu*, Alan Ji, Junyan Zhang, Jonathan Li, Beidi Chen and Anshumali Shrivastava, "HALOS: Hashing Large Output Space for Cheap Inference", MLSys 2022. PDF

• Zhaozhuo Xu, Zhao Song and Anshumali Shrivastava, "Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures", NeurIPS 2021. PDF

• Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin and Anshumali Shrivastava, "Locality Sensitive Teaching", NeurIPS 2021. PDF Code

• Aditya Desai*, Zhaozhuo Xu*, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy and Anshumali Shrivastava, "Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler", NeurIPS 2021. PDF

• Shulong Tan, Zhaozhuo Xu, Weijie Zhao, Hongliang Fei, Zhixin Zhou and Ping Li, "Norm Adjusted Proximity Graph for Fast Inner Product Retrieval", KDD 2021. PDF

• Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava and Christopher Re, "MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training ", ICLR 2021 Oral. PDF Code

• Shulong Tan, Zhixin Zhou, Zhaozhuo Xu and Ping Li, "Fast Item Ranking under Neural Network based Measures", WSDM 2020. PDF

• Zhaozhuo Xu, Alan Baonan Ji, Andrew Woods, Beidi Chen and Anshumali Shrivastava, "Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston", JSM 2020. PDF

• Zhixin Zhou, Shulong Tan, Zhaozhuo Xu and Ping Li, "Möbius Transformation for Fast Inner Product Search on Graph", NeurIPS 2019. PDF Code

• Shulong Tan, Zhixin Zhou, Zhaozhuo Xu and Ping Li, "On Efficient Retrieval of Top Similarity Vectors", EMNLP 2019. PDF

Tutorial

• AAAI 2025: KV Cache Compression for Efficient Long Context LLM Inference: Challenges, Trade-Offs, and Opportunities. Link

• AAAI 2025: LLMs and Copyright Risks: Benchmarks and Mitigation Approaches. Link

Honor

• AAAI-25 New Faculty Highlights. Link

• Ken Kennedy Institute BP Fellowship, 2021 - 2022. Link

Service

• Organizer: Research On Algorithms & Data Structures (ROADS) to Mega-AI Models Workshop, MLSys 2023. Link

• Area Chair: ICLR, ICML, ARR, EMNLP Main/Demo Track, NAACL, COLING