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2020 – today
- 2024
[c22]Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang:
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. ACL (Findings) 2024: 10471-10506- [c21]Timothy Dai
, Austin Peters, Jonah B. Gelbach, David Freeman Engstrom, Daniel Kang:
tailwiz: Empowering Domain Experts with Easy-to-Use, Task-Specific Natural Language Processing Models. DEEM@SIGMOD 2024: 12-22 - [c20]Tengjun Jin, Akash Mittal, Chenghao Mo, Jiahao Fang, Chengsong Zhang, Timothy Dai, Daniel Kang:
AIDB: a Sparsely Materialized Database for Queries using Machine Learning. DEEM@SIGMOD 2024: 23-28 - [c19]Bing-Jyue Chen, Suppakit Waiwitlikhit, Ion Stoica, Daniel Kang:
ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs. EuroSys 2024: 560-574 - [c18]Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson:
Position: A Safe Harbor for AI Evaluation and Red Teaming. ICML 2024 - [c17]Suppakit Waiwitlikhit, Ion Stoica, Yi Sun, Tatsunori Hashimoto, Daniel Kang:
Trustless Audits without Revealing Data or Models. ICML 2024 - [c16]Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, Daniel Kang:
Removing RLHF Protections in GPT-4 via Fine-Tuning. NAACL (Short Papers) 2024: 681-687 - [c15]Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto:
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. SP (Workshops) 2024: 132-143 - [i31]Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, Daniel Kang:
LLM Agents can Autonomously Hack Websites. CoRR abs/2402.06664 (2024) - [i30]Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang:
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. CoRR abs/2403.02691 (2024) - [i29]Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson:
A Safe Harbor for AI Evaluation and Red Teaming. CoRR abs/2403.04893 (2024) - [i28]Suppakit Waiwitlikhit, Ion Stoica, Yi Sun, Tatsunori Hashimoto, Daniel Kang:
Trustless Audits without Revealing Data or Models. CoRR abs/2404.04500 (2024) - [i27]Richard Fang, Rohan Bindu, Akul Gupta, Daniel Kang:
LLM Agents can Autonomously Exploit One-day Vulnerabilities. CoRR abs/2404.08144 (2024) - [i26]Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, Daniel Kang:
Teams of LLM Agents can Exploit Zero-Day Vulnerabilities. CoRR abs/2406.01637 (2024) - 2023
- [j12]Clark W. Barrett, Brad Boyd, Elie Bursztein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John C. Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang:
Identifying and Mitigating the Security Risks of Generative AI. Found. Trends Priv. Secur. 6(1): 1-52 (2023) - [j11]Matthew Russo, Tatsunori Hashimoto, Daniel Kang, Yi Sun, Matei Zaharia:
Accelerating Aggregation Queries on Unstructured Streams of Data. Proc. VLDB Endow. 16(11): 2897-2910 (2023) - [c14]Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer:
Q-Diffusion: Quantizing Diffusion Models. ICCV 2023: 17489-17499 - [i25]Xiuyu Li, Long Lian, Yijiang Liu, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer:
Q-Diffusion: Quantizing Diffusion Models. CoRR abs/2302.04304 (2023) - [i24]Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto:
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. CoRR abs/2302.05733 (2023) - [i23]Stefanos Baziotis, Daniel Kang, Charith Mendis:
Dias: Dynamic Rewriting of Pandas Code. CoRR abs/2303.16146 (2023) - [i22]Matthew Russo, Tatsunori Hashimoto, Daniel Kang, Yi Sun, Matei Zaharia:
Accelerating Aggregation Queries on Unstructured Streams of Data. CoRR abs/2308.09157 (2023) - [i21]Clark W. Barrett, Brad Boyd, Ellie Burzstein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John C. Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang:
Identifying and Mitigating the Security Risks of Generative AI. CoRR abs/2308.14840 (2023) - [i20]Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, Daniel Kang:
Removing RLHF Protections in GPT-4 via Fine-Tuning. CoRR abs/2311.05553 (2023) - 2022
- [j10]Francisco Romero, Johann Hauswald, Aditi Partap, Daniel Kang, Matei Zaharia, Christos Kozyrakis:
Optimizing Video Analytics with Declarative Model Relationships. Proc. VLDB Endow. 16(3): 447-460 (2022) - [c13]Daniel Kang, Francisco Romero, Peter D. Bailis, Christos Kozyrakis, Matei Zaharia:
VIVA: An End-to-End System for Interactive Video Analytics. CIDR 2022 - [c12]Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter D. Bailis, Matei Zaharia:
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. SIGMOD Conference 2022: 496-505 - [c11]Daniel Kang, John Guibas, Peter D. Bailis, Tatsunori Hashimoto, Matei Zaharia:
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data. SIGMOD Conference 2022: 1934-1947 - [i19]Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia:
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR abs/2201.05797 (2022) - [i18]Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun:
Scaling up Trustless DNN Inference with Zero-Knowledge Proofs. CoRR abs/2210.08674 (2022) - [i17]Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun:
ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation. CoRR abs/2211.04775 (2022) - 2021
- [j9]Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone:
Network offloading policies for cloud robotics: a learning-based approach. Auton. Robots 45(7): 997-1012 (2021) - [j8]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Accelerating Approximate Aggregation Queries with Expensive Predicates. Proc. VLDB Endow. 14(11): 2341-2354 (2021) - [c10]Daniel Kang:
Accelerating Queries over Unstructured Data with ML. CIDR 2021 - [i16]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR abs/2107.12525 (2021) - [i15]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR abs/2108.06313 (2021) - 2020
- [j7]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13(11): 1990-2003 (2020) - [j6]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Proc. VLDB Endow. 13(12): 2833-2836 (2020) - [j5]Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia:
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. Proc. VLDB Endow. 14(2): 87-100 (2020) - [c9]Daniel Kang, Tatsunori Hashimoto:
Improved Natural Language Generation via Loss Truncation. ACL 2020: 718-731 - [c8]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. MLSys 2020 - [c7]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. MLSys 2020 - [c6]Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. MLSys 2020 - [i14]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. CoRR abs/2003.01668 (2020) - [i13]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. CoRR abs/2004.00827 (2020) - [i12]Daniel Kang, Tatsunori Hashimoto:
Improved Natural Language Generation via Loss Truncation. CoRR abs/2004.14589 (2020) - [i11]Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia:
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. CoRR abs/2007.13005 (2020) - [i10]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. CoRR abs/2009.04540 (2020)
2010 – 2019
- 2019
- [j4]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proc. VLDB Endow. 13(4): 533-546 (2019) - [j3]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Oper. Syst. Rev. 53(1): 14-25 (2019) - [c5]Daniel Kang, Peter Bailis, Matei Zaharia:
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR 2019 - [c4]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Learned Intermediate Representation Training for Model Compression. ICML 2019: 3509-3518 - [c3]Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone:
Network Offloading Policies for Cloud Robotics: A Learning-Based Approach. Robotics: Science and Systems 2019 - [i9]Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone:
Network Offloading Policies for Cloud Robotics: a Learning-based Approach. CoRR abs/1902.05703 (2019) - [i8]Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, Jacob Steinhardt:
Transfer of Adversarial Robustness Between Perturbation Types. CoRR abs/1905.01034 (2019) - [i7]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. CoRR abs/1906.01974 (2019) - [i6]Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, Jacob Steinhardt:
Testing Robustness Against Unforeseen Adversaries. CoRR abs/1908.08016 (2019) - [i5]Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. CoRR abs/1910.01500 (2019) - 2018
- [i4]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR abs/1805.01046 (2018) - [i3]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. CoRR abs/1806.01427 (2018) - [i2]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR abs/1810.01937 (2018) - 2017
- [j2]Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia:
NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. Proc. VLDB Endow. 10(11): 1586-1597 (2017) - [i1]Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia:
Optimizing Deep CNN-Based Queries over Video Streams at Scale. CoRR abs/1703.02529 (2017) - 2014
- [c2]Tim Chan, Joseph I, Carlos Macasaet, Daniel Kang, Robert M. Hardy, Carlos Ruiz, Rigel Porras, Brian Baron, Karim Qazi, Padraic Hannon, Tomonori Honda:
Predictive Models for Determining If and When to Display Online Lead Forms. AAAI 2014: 2882-2889
2000 – 2009
- 2008
- [j1]Byeong-Seok Shin, Dong-Ryeol Oh, Daniel Kang:
Real-Time Point-Based Rendering Using Visibility Map. IEICE Trans. Inf. Syst. 91-D(1): 124-131 (2008) - 2005
- [c1]Daniel Kang, Byeong-Seok Shin:
Efficient Point Rendering Method Using Sequential Level-of-Detail. CIS (2) 2005: 1127-1133
Coauthor Index
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last updated on 2024-09-26 01:00 CEST by the dblp team
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