Daniel Kang

According to our database, Daniel Kang authored at least 43 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

A Safe Harbor for AI Evaluation and Red Teaming.

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

CoRR, 2024

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents.

Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang

CoRR, 2024

LLM Agents can Autonomously Hack Websites.

Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, Daniel Kang

CoRR, 2024

ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs.

Bing-Jyue Chen, Suppakit Waiwitlikhit, Ion Stoica, Daniel Kang

Proceedings of the Nineteenth European Conference on Computer Systems, 2024

2023

Accelerating Aggregation Queries on Unstructured Streams of Data.

Matthew Russo, Tatsunori Hashimoto, Daniel Kang, Yi Sun, Matei Zaharia

Proc. VLDB Endow., 2023

Identifying and Mitigating the Security Risks of Generative AI.

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

Found. Trends Priv. Secur., 2023

Removing RLHF Protections in GPT-4 via Fine-Tuning.

Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, Daniel Kang

CoRR, 2023

Identifying and Mitigating the Security Risks of Generative AI.

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

CoRR, 2023

Dias: Dynamic Rewriting of Pandas Code.

Stefanos Baziotis, Daniel Kang, Charith Mendis

CoRR, 2023

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks.

Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto

CoRR, 2023

Q-Diffusion: Quantizing Diffusion Models.

Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Optimizing Video Analytics with Declarative Model Relationships.

Francisco Romero, Johann Hauswald, Aditi Partap, Daniel Kang, Matei Zaharia, Christos Kozyrakis

Proc. VLDB Endow., 2022

ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation.

Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun

CoRR, 2022

Scaling up Trustless DNN Inference with Zero-Knowledge Proofs.

Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun

CoRR, 2022

TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data.

Daniel Kang, John Guibas, Peter D. Bailis, Tatsunori Hashimoto, Matei Zaharia

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Finding Label and Model Errors in Perception Data With Learned Observation Assertions.

Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter D. Bailis, Matei Zaharia

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

VIVA: An End-to-End System for Interactive Video Analytics.

Daniel Kang, Francisco Romero, Peter D. Bailis, Christos Kozyrakis, Matei Zaharia

Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

2021

Accelerating Approximate Aggregation Queries with Expensive Predicates.

Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia

Proc. VLDB Endow., 2021

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates.

Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia

CoRR, 2021

Network offloading policies for cloud robotics: a learning-based approach.

Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone

Auton. Robots, 2021

Accelerating Queries over Unstructured Data with ML.

Daniel Kang

Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

2020

A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference.

Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2020

Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics.

Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2020

Approximate Selection with Guarantees using Proxies.

Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia

Proc. VLDB Endow., 2020

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data.

Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia

CoRR, 2020

MLPerf Training Benchmark.

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

Proceedings of Machine Learning and Systems 2020, 2020

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference.

Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia

Proceedings of Machine Learning and Systems 2020, 2020

Model Assertions for Monitoring and Improving ML Models.

Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia

Proceedings of Machine Learning and Systems 2020, 2020

Improved Natural Language Generation via Loss Truncation.

Daniel Kang, Tatsunori Hashimoto

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark.

Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia

ACM SIGOPS Oper. Syst. Rev., 2019

BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics.

Daniel Kang, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2019

MLPerf Training Benchmark.

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

CoRR, 2019

Testing Robustness Against Unforeseen Adversaries.

Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, Jacob Steinhardt

CoRR, 2019

Transfer of Adversarial Robustness Between Perturbation Types.

Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, Jacob Steinhardt

CoRR, 2019

LIT: Learned Intermediate Representation Training for Model Compression.

Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia

Proceedings of the 36th International Conference on Machine Learning, 2019

Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine.

Daniel Kang, Peter Bailis, Matei Zaharia

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

2018

LIT: Block-wise Intermediate Representation Training for Model Compression.

Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia

CoRR, 2018

BlazeIt: Fast Exploratory Video Queries using Neural Networks.

Daniel Kang, Peter Bailis, Matei Zaharia

CoRR, 2018

2017

NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale.

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2017

Optimizing Deep CNN-Based Queries over Video Streams at Scale.

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

CoRR, 2017

2014

Predictive Models for Determining If and When to Display Online Lead Forms.

Tim Chan, Joseph I, Carlos Macasaet, Daniel Kang, Robert M. Hardy, Carlos Ruiz, Rigel Porras, Brian Baron, Karim Qazi, Padraic Hannon, Tomonori Honda

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2008

Real-Time Point-Based Rendering Using Visibility Map.

Byeong-Seok Shin, Dong-Ryeol Oh, Daniel Kang

IEICE Trans. Inf. Syst., 2008

2005

Efficient Point Rendering Method Using Sequential Level-of-Detail.

Daniel Kang, Byeong-Seok Shin

Proceedings of the Computational Intelligence and Security, International Conference, 2005