| NoScope: Optimizing Neural Network Queries over Video at Scale D Kang, J Emmons, F Abuzaid, P Bailis, M Zaharia Proceedings of the VLDB Endowment 10 (11), 1586-1597, 2017 | 540* | 2017 |
| DAWNBench: An End-to-End Deep Learning Benchmark and Competition C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ... NIPS ML Systems Workshop, 2017 | 385 | 2017 |
| MLPerf Training Benchmark P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ... arXiv preprint arXiv:1910.01500, 2019 | 329 | 2019 |
| BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-based Video Analytics D Kang, P Bailis, M Zaharia PVLDB 13 (4), 2019 | 228* | 2019 |
| Testing Robustness Against Unforeseen Adversaries D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt arXiv preprint arXiv:1908.08016, 2019 | 187* | 2019 |
| Exploiting programmatic behavior of llms: Dual-use through standard security attacks D Kang, X Li, I Stoica, C Guestrin, M Zaharia, T Hashimoto 2024 IEEE Security and Privacy Workshops (SPW), 132-143, 2024 | 165 | 2024 |
| Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark C Coleman, D Kang, D Narayanan, L Nardi, T Zhao, J Zhang, P Bailis, ... ACM SIGOPS Operating Systems Review 53 (1), 14-25, 2019 | 141 | 2019 |
| Network Offloading Policies for Cloud Robotics: a Learning-based Approach S Chinchali, A Sharma, J Harrison, A Elhafsi, D Kang, E Pergament, ... arXiv preprint arXiv:1902.05703, 2019 | 129 | 2019 |
| Model Assertions for Monitoring and Improving ML Models D Kang, D Raghavan, P Bailis, M Zaharia MLSys, 2020 | 118* | 2020 |
| Improved Natural Language Generation via Loss Truncation D Kang, T Hashimoto ACL, 2020 | 97 | 2020 |
| Q-diffusion: Quantizing diffusion models X Li, Y Liu, L Lian, H Yang, Z Dong, D Kang, S Zhang, K Keutzer Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 90 | 2023 |
| LIT: Learned Intermediate Representation Training for Model Compression A Koratana, D Kang, P Bailis, M Zaharia International Conference on Machine Learning, 3509-3518, 2019 | 82* | 2019 |
| Identifying and mitigating the security risks of generative ai C Barrett, B Boyd, E Bursztein, N Carlini, B Chen, J Choi, AR Chowdhury, ... Foundations and Trends® in Privacy and Security 6 (1), 1-52, 2023 | 58 | 2023 |
| Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics D Kang, A Mathur, T Veeramacheneni, P Bailis, M Zaharia arXiv preprint arXiv:2007.13005, 2020 | 55 | 2020 |
| Removing RLHF Protections in GPT-4 via Fine-Tuning Q Zhan, R Fang, R Bindu, A Gupta, T Hashimoto, D Kang arXiv preprint arXiv:2311.05553, 2023 | 54 | 2023 |
| GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding H Zeng, T Hashimoto, DD Kang, DK Gifford Bioinformatics 32 (4), 490-496, 2016 | 50 | 2016 |
| TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data D Kang, J Guibas, PD Bailis, T Hashimoto, M Zaharia Proceedings of the 2022 International Conference on Management of Data, 1934 …, 2022 | 43* | 2022 |
| Approximate Selection with Guarantees using Proxies D Kang, E Gan, P Bailis, T Hashimoto, M Zaharia PVLDB, 2020 | 35 | 2020 |
| Scaling up Trustless DNN Inference with Zero-Knowledge Proofs D Kang, T Hashimoto, I Stoica, Y Sun arXiv preprint arXiv:2210.08674, 2022 | 29 | 2022 |
| Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference P Kraft, D Kang, D Narayanan, S Palkar, P Bailis, M Zaharia MLSys, 2020 | 29 | 2020 |
Matei ZahariaUC Berkeley and DatabricksVerified email at berkeley.edu
