Daniel Kang

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 59

Top Topics

Diffusion Models

Machine Learning

Natural Language

Unstructured Data

Top Venues

Proc. VLDB Endow.

Publications

Suppakit Waiwitlikhit, Ion Stoica, Yi Sun, Tatsunori Hashimoto, Daniel Kang
Trustless Audits without Revealing Data or Models. CoRR (2024)
Richard Fang, Rohan Bindu, Akul Gupta, Daniel Kang
LLM Agents can Autonomously Exploit One-day Vulnerabilities. CoRR (2024)
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)
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)
Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. CoRR (2024)
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 (2024)
Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, Daniel Kang
LLM Agents can Autonomously Hack Websites. CoRR (2024)
Bing-Jyue Chen, Suppakit Waiwitlikhit, Ion Stoica, Daniel Kang
ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs. EuroSys (2024)
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)
Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, Daniel Kang
Teams of LLM Agents can Exploit Zero-Day Vulnerabilities. CoRR (2024)
Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. ACL (Findings) (2024)
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 (2023)
Stefanos Baziotis, Daniel Kang, Charith Mendis
Dias: Dynamic Rewriting of Pandas Code. CoRR (2023)
Matthew Russo, Tatsunori Hashimoto, Daniel Kang, Yi Sun, Matei Zaharia
Accelerating Aggregation Queries on Unstructured Streams of Data. CoRR (2023)
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) (2023)
Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, Daniel Kang
Removing RLHF Protections in GPT-4 via Fine-Tuning. CoRR (2023)
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer
Q-Diffusion: Quantizing Diffusion Models. ICCV (2023)
Matthew Russo, Tatsunori Hashimoto, Daniel Kang, Yi Sun, Matei Zaharia
Accelerating Aggregation Queries on Unstructured Streams of Data. Proc. VLDB Endow. 16 (11) (2023)
Xiuyu Li, Long Lian, Yijiang Liu, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer
Q-Diffusion: Quantizing Diffusion Models. CoRR (2023)
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. CoRR (2023)
Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR (2022)
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)
Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun
ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation. CoRR (2022)
Daniel Kang, Francisco Romero, Peter D. Bailis, Christos Kozyrakis, Matei Zaharia
VIVA: An End-to-End System for Interactive Video Analytics. CIDR (2022)
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)
Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun
Scaling up Trustless DNN Inference with Zero-Knowledge Proofs. CoRR (2022)
Francisco Romero, Johann Hauswald, Aditi Partap, Daniel Kang, Matei Zaharia, Christos Kozyrakis
Optimizing Video Analytics with Declarative Model Relationships. Proc. VLDB Endow. 16 (3) (2022)
Daniel Kang
Accelerating Queries over Unstructured Data with ML. CIDR (2021)
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) (2021)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
Accelerating Approximate Aggregation Queries with Expensive Predicates. Proc. VLDB Endow. 14 (11) (2021)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR (2021)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR (2021)
Daniel Kang, Tatsunori Hashimoto
Improved Natural Language Generation via Loss Truncation. CoRR (2020)
Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13 (11) (2020)
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
Model Assertions for Monitoring and Improving ML Models. MLSys (2020)
Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. CoRR (2020)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. CoRR (2020)
Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
Approximate Selection with Guarantees using Proxies. CoRR (2020)
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) (2020)
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) (2020)
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)
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)
Daniel Kang, Tatsunori Hashimoto
Improved Natural Language Generation via Loss Truncation. ACL (2020)
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
Model Assertions for Monitoring and Improving ML Models. CoRR (2020)
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 (2019)
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 (2019)
Daniel Kang, Peter Bailis, Matei Zaharia
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proc. VLDB Endow. 13 (4) (2019)
Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, Jacob Steinhardt
Testing Robustness Against Unforeseen Adversaries. CoRR (2019)
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 (2019)
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) (2019)
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)
Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, Jacob Steinhardt
Transfer of Adversarial Robustness Between Perturbation Types. CoRR (2019)
Daniel Kang, Peter Bailis, Matei Zaharia
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR (2019)
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
LIT: Learned Intermediate Representation Training for Model Compression. ICML (2019)
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 (2018)
Daniel Kang, Peter Bailis, Matei Zaharia
BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR (2018)
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR (2018)
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
Optimizing Deep CNN-Based Queries over Video Streams at Scale. CoRR (2017)
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) (2017)
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)
Byeong-Seok Shin, Dong-Ryeol Oh, Daniel Kang
Real-Time Point-Based Rendering Using Visibility Map. IEICE Trans. Inf. Syst. (1) (2008)
Daniel Kang, Byeong-Seok Shin
Efficient Point Rendering Method Using Sequential Level-of-Detail. CIS (2) (2005)