Network Traffic Anomaly Detection Based on Wavelet Analysis. - Zhen Du; Lipeng Ma; Huakang Li; Qun Li; Guozi Sun; Zichang Liu (SERA, 2018)

Guigle: a GUI search engine for Android apps. - Carlos Bernal-Cárdenas; Kevin Moran; Michele Tufano; Zichang Liu; Linyong Nan; Zhehan Shi; Denys Poshyvanyk (ICSE (Companion Volume), 2019)

Climbing the WOL: Training for Cheaper Inference. - Zichang Liu; Zhaozhuo Xu; Alan Baonan Ji; Jonathan Li 002; Beidi Chen; Anshumali Shrivastava (CoRR, 2020)

Conditional Automated Channel Pruning for Deep Neural Networks. - Yixin Liu 002; Yong Guo; Zichang Liu; Haohua Liu; Jingjie Zhang; Zejun Chen; Jing Liu 048; Jian Chen 011 (CoRR, 2020)

Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. - Zichang Liu; Li Chou; Anshumali Shrivastava (DATE, 2021)

MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. - Beidi Chen; Zichang Liu; Binghui Peng; Zhaozhuo Xu; Jonathan Lingjie Li; Tri Dao; Zhao Song 002; Anshumali Shrivastava; Christopher Ré (ICLR, 2021)

Efficient and Less Centralized Federated Learning. - Li Chou; Zichang Liu; Zhuang Wang; Anshumali Shrivastava (ECML/PKDD (1), 2021)

Efficient Inference via Universal LSH Kernel. - Zichang Liu; Benjamin Coleman; Anshumali Shrivastava (CoRR, 2021)

DONet: Learning Category-Level 6D Object Pose and Size Estimation from Depth Observation. - Haitao Lin; Zichang Liu; Chilam Cheang; Lingwei Zhang; Yanwei Fu 001; Xiangyang Xue 001 (CoRR, 2021)

SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation. - Haitao Lin; Zichang Liu; Chilam Cheang; Yanwei Fu 001; Guodong Guo; Xiangyang Xue 001 (CVPR, 2022)

HALOS: Hashing Large Output Space for Cheap Inference. - Zichang Liu; Zhaozhuo Xu; Alan Baonan Ji; Junyan Zhang; Jonathan Li 002; Beidi Chen; Anshumali Shrivastava (MLSys, 2022)

Retaining Knowledge for Learning with Dynamic Definition. - Zichang Liu; Benjamin Coleman; Tianyi Zhang 011; Anshumali Shrivastava (NeurIPS, 2022)

Golden Sine SABO Integrating Multiple Strategies. - Zichang Liu; Hui Liang; Siyu Li; Rongcai Wang; Yueming Han (ICAICE, 2023)

Learning Multimodal Data Augmentation in Feature Space. - Zichang Liu; Zhiqiang Tang 001; Xingjian Shi; Aston Zhang; Mu Li 003; Anshumali Shrivastava; Andrew Gordon Wilson (ICLR, 2023)

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. - Zichang Liu; Jue Wang; Tri Dao; Tianyi Zhou 002; Binhang Yuan; Zhao Song 002; Anshumali Shrivastava; Ce Zhang 001; Yuandong Tian; Christopher Ré; Beidi Chen (ICML, 2023)

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

One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. - Zichang Liu; Zhaozhuo Xu; Benjamin Coleman; Anshumali Shrivastava (NeurIPS, 2023)

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces. - Aditya Desai; Benjamin Meisburger; Zichang Liu; Anshumali Shrivastava (CoRR, 2023)

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer. - Siyu Li; Zichang Liu; Yunbin Yan; Rongcai Wang; Enzhi Dong; Zhonghua Cheng (Sensors, 2023)

Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model. - Zichang Liu; Qingyun Liu 003; Yuening Li; Liang Liu 017; Anshumali Shrivastava; Shuchao Bi; Lichan Hong; Ed H. Chi; Zhe Zhao 001 (CoRR, 2024)