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2020 – today
- 2023
[i35]Steven H. Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks:
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding. CoRR abs/2301.00876 (2023)- 2022
- [j1]Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich:
PAC Guarantees and Effective Algorithms for Detecting Novel Categories. J. Mach. Learn. Res. 23: 44:1-44:47 (2022) - [c22]Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt:
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. CVPR 2022: 16762-16771 - [c21]Jiachen Sun
, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao:
A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. ECCV (4) 2022: 654-671 - [c20]Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song:
Scaling Out-of-Distribution Detection for Real-World Settings. ICML 2022: 8759-8773 - [i34]Dan Hendrycks, Mantas Mazeika:
X-Risk Analysis for AI Research. CoRR abs/2206.05862 (2022) - [i33]Anthony M. Barrett, Dan Hendrycks, Jessica Newman, Brandie Nonnecke:
Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks. CoRR abs/2206.08966 (2022) - [i32]Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks:
Forecasting Future World Events with Neural Networks. CoRR abs/2206.15474 (2022) - [i31]Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, Ziwei Liu:
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. CoRR abs/2210.07242 (2022) - [i30]Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan, Dawn Song, David A. Forsyth, Jacob Steinhardt, Dan Hendrycks:
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. CoRR abs/2210.10039 (2022) - 2021
- [c19]Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song:
Natural Adversarial Examples. CVPR 2021: 15262-15271 - [c18]Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer:
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. ICCV 2021: 8320-8329 - [c17]Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt:
Aligning AI With Shared Human Values. ICLR 2021 - [c16]Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt:
Measuring Massive Multitask Language Understanding. ICLR 2021 - [c15]Dina Bashkirova, Dan Hendrycks, Donghyun Kim, Haojin Liao, Samarth Mishra, Chandramouli Rajagopalan, Kate Saenko, Kuniaki Saito, Burhan Ul Tayyab, Piotr Teterwak, Ben Usman:
VisDA-2021 Competition: Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data. NeurIPS (Competition and Demos) 2021: 66-79 - [c14]Dan Hendrycks, Collin Burns, Anya Chen, Spencer Ball:
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review. NeurIPS Datasets and Benchmarks 2021 - [c13]Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt:
Measuring Mathematical Problem Solving With the MATH Dataset. NeurIPS Datasets and Benchmarks 2021 - [c12]Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt:
Measuring Coding Challenge Competence With APPS. NeurIPS Datasets and Benchmarks 2021 - [c11]Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, Jacob Steinhardt:
What Would Jiminy Cricket Do? Towards Agents That Behave Morally. NeurIPS Datasets and Benchmarks 2021 - [i29]Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt:
Measuring Mathematical Problem Solving With the MATH Dataset. CoRR abs/2103.03874 (2021) - [i28]Dan Hendrycks, Collin Burns, Anya Chen, Spencer Ball:
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review. CoRR abs/2103.06268 (2021) - [i27]Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt:
Measuring Coding Challenge Competence With APPS. CoRR abs/2105.09938 (2021) - [i26]Dina Bashkirova, Dan Hendrycks, Donghyun Kim, Samarth Mishra, Kate Saenko, Kuniaki Saito, Piotr Teterwak, Ben Usman:
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data. CoRR abs/2107.11011 (2021) - [i25]Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt:
Unsolved Problems in ML Safety. CoRR abs/2109.13916 (2021) - [i24]Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, Jacob Steinhardt:
What Would Jiminy Cricket Do? Towards Agents That Behave Morally. CoRR abs/2110.13136 (2021) - [i23]Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou:
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. CoRR abs/2110.14051 (2021) - [i22]Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao:
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. CoRR abs/2112.00659 (2021) - [i21]Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt:
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. CoRR abs/2112.05135 (2021) - 2020
- [c10]Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song:
Pretrained Transformers Improve Out-of-Distribution Robustness. ACL 2020: 2744-2751 - [c9]Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. ICLR 2020 - [i20]Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song:
Pretrained Transformers Improve Out-of-Distribution Robustness. CoRR abs/2004.06100 (2020) - [i19]Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer:
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. CoRR abs/2006.16241 (2020) - [i18]Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt:
Aligning AI With Shared Human Values. CoRR abs/2008.02275 (2020) - [i17]Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt:
Measuring Massive Multitask Language Understanding. CoRR abs/2009.03300 (2020)
2010 – 2019
- 2019
- [c8]Dan Hendrycks, Thomas G. Dietterich:
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. ICLR (Poster) 2019 - [c7]Dan Hendrycks, Mantas Mazeika, Thomas G. Dietterich:
Deep Anomaly Detection with Outlier Exposure. ICLR (Poster) 2019 - [c6]Dan Hendrycks, Kimin Lee, Mantas Mazeika:
Using Pre-Training Can Improve Model Robustness and Uncertainty. ICML 2019: 2712-2721 - [c5]Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song:
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. NeurIPS 2019: 15637-15648 - [i16]Dan Hendrycks, Kimin Lee, Mantas Mazeika:
Using Pre-Training Can Improve Model Robustness and Uncertainty. CoRR abs/1901.09960 (2019) - [i15]Dan Hendrycks, Thomas G. Dietterich:
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. CoRR abs/1903.12261 (2019) - [i14]Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, Jacob Steinhardt:
Transfer of Adversarial Robustness Between Perturbation Types. CoRR abs/1905.01034 (2019) - [i13]Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song:
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. CoRR abs/1906.12340 (2019) - [i12]Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song:
Natural Adversarial Examples. CoRR abs/1907.07174 (2019) - [i11]Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, Jacob Steinhardt:
Testing Robustness Against Unforeseen Adversaries. CoRR abs/1908.08016 (2019) - [i10]Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song:
A Benchmark for Anomaly Segmentation. CoRR abs/1911.11132 (2019) - [i9]Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. CoRR abs/1912.02781 (2019) - 2018
- [c4]Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks:
Open Category Detection with PAC Guarantees. ICML 2018: 3175-3184 - [c3]Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel:
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. NeurIPS 2018: 10477-10486 - [i8]Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel:
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. CoRR abs/1802.05300 (2018) - [i7]Dan Hendrycks, Thomas G. Dietterich:
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. CoRR abs/1807.01697 (2018) - [i6]Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks:
Open Category Detection with PAC Guarantees. CoRR abs/1808.00529 (2018) - [i5]Dan Hendrycks, Mantas Mazeika, Thomas G. Dietterich:
Deep Anomaly Detection with Outlier Exposure. CoRR abs/1812.04606 (2018) - 2017
- [c2]Dan Hendrycks, Kevin Gimpel:
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. ICLR (Poster) 2017 - [c1]Dan Hendrycks, Kevin Gimpel:
Early Methods for Detecting Adversarial Images. ICLR (Workshop) 2017 - 2016
- [i4]Dan Hendrycks, Kevin Gimpel:
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. CoRR abs/1606.08415 (2016) - [i3]Dan Hendrycks, Kevin Gimpel:
Generalizing and Improving Weight Initialization. CoRR abs/1607.02488 (2016) - [i2]Dan Hendrycks, Kevin Gimpel:
Visible Progress on Adversarial Images and a New Saliency Map. CoRR abs/1608.00530 (2016) - [i1]Dan Hendrycks, Kevin Gimpel:
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. CoRR abs/1610.02136 (2016)
Coauthor Index
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last updated on 2023-01-11 22:38 CET by the dblp team
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