Angelopoulos, Anastasios N.

A gentle introduction to conformal prediction and distribution-free uncertainty quantification

Angelopoulos, A. N., & Bates, S. (2021) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Algorithms

Cited by 240

Distribution-free, Risk-controlling Prediction Sets

Bates, S., Angelopoulos, A., Lei, L., Malik, J., & Jordan, M. (2021) · Journal of the ACM

Adversarial Robustness in Machine LearningMachine Learning and Algorithms

Cited by 144

Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonzalez, J. E., et al. (2024) · arXiv

Constraint Satisfaction and OptimizationNatural Language Processing Techniques

Cited by 118

Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control

Angelopoulos, A. N., Bates, S., Candès, E. J., Jordan, M. I., & Lei, L. (2021) · arXiv

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 100

Uncertainty Sets for Image Classifiers using Conformal Prediction

Angelopoulos, A., Bates, S., Malik, J., & Jordan, M. I. (2020) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 79

Conformal Risk Control

Angelopoulos, A. N., Bates, S., Fisch, A., Lei, L., & Schuster, T. (2022) · arXiv

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 37

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

Lekeufack, J., Angelopoulos, A. N., Bajcsy, A., Jordan, M. I., & Malik, J. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Machine Learning and AlgorithmsAdversarial Robustness in Machine Learning

Cited by 18

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards

Huang, Y., Nasr, M., Angelopoulos, A., Carlini, N., Chiang, W.-L., Choquette-Choo, C. A., Ippolito, D., Jagielski, M., Lee, K., Liu, K. Z., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningSpam and Phishing Detection

Cited by 17

Private Prediction Sets

Angelopoulos, A. N., Bates, S., Zrnic, T., & Jordan, M. I. (2022) · Harvard Data Science Review

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 8

Specifications: The missing link to making the development of LLM systems an engineering discipline

Stoica, I., Zaharia, M., Gonzalez, J., Goldberg, K., Sen, K., Zhang, H., Angelopoulos, A., Patil, S. G., Chen, L., Chiang, W.-L., et al. (2024) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 2

Search Arena: Analyzing Search-Augmented LLMs

Miroyan, M., Wu, T.-H., King, L., Li, T., Pan, J., Hu, X., Chiang, W.-L., Angelopoulos, A. N., Darrell, T., Norouzi, N., et al. (2025) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 1

AutoEval Done Right: Using Synthetic Data for Model Evaluation

Boyeau, P., Angelopoulos, A. N., Yosef, N., Malik, J., & Jordan, M. I. (2024) · arXiv

Machine Learning and Data ClassificationMachine Learning and Algorithms

Cited by 1

Automatically Adaptive Conformal Risk Control

Blot, V., Angelopoulos, A. N., Jordan, M. I., & Brunel, N. J.-B. (2024) · arXiv

Machine Learning and AlgorithmsAdversarial Robustness in Machine Learning

Cited by 1

Prompt-to-Leaderboard

Frick, E., Chen, C., Tennyson, J., Li, T., Chiang, W.-L., Angelopoulos, A. N., & Stoica, I. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0