Papers
Frontier models and responsible AI · 2021 to 2025
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HealthBench: Evaluating Large Language Models Towards Improved Human Health
Rahul K. Arora, Jason Wei, and others, including me as a co-author
OpenAI
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Preparedness Framework, Version 2
OpenAI. Written by the Preparedness team while I led it. Announcement
OpenAI, 15 April 2025
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OpenAI. Produced by the Preparedness team while I led it
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OpenAI. Produced by the Preparedness team while I led it
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Disentangling and Operationalizing AI Fairness at LinkedIn
Joaquin Quiñonero Candela, Yuwen Wu, Brian Hsu, Sakshi Jain, Jen Ramos, Jon Adams, Robert Hallman, Kinjal Basu
ACM Conference on Fairness, Accountability, and Transparency (FAccT), pages 1213 to 1228
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Technology Primer: Social Media Recommendation Algorithms
Constanza M. Vidal Bustamante, Lucas Wright, Leisel Bogan, Marc Faddoul, Joaquin Quiñonero Candela
Belfer Center for Science and International Affairs, Harvard Kennedy School
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Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
Chloé Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle Lam, Joaquin Quiñonero Candela, Manish Raghavan, Joshua Simons, Jonathan Tannen, Edmund Tong, Kate Vredenburgh, Jiejing Zhao
arXiv 2103.06172
Ads, click prediction and counterfactuals · 2009 to 2014
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Practical Lessons from Predicting Clicks on Ads at Facebook
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela
ADKDD 2014, Eighth International Workshop on Data Mining for Online Advertising, pages 1 to 9
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Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
Journal of Machine Learning Research, volume 14, pages 3207 to 3260
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Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal
Journal of Machine Learning Research, volume 11, pages 1865 to 1881
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Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine
Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich
27th International Conference on Machine Learning, Haifa, pages 13 to 20
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Dataset Shift in Machine Learning
Joaquin Quiñonero Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence, editors
MIT Press, Cambridge, MA
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Scalable Clustering and Keyword Suggestion for Online Advertisements
Anton Schwaighofer, Joaquin Quiñonero Candela, Thomas Borchert, Thore Graepel, Ralf Herbrich
ADKDD 2009, Third International Workshop on Data Mining and Audience Intelligence for Advertising, pages 27 to 36
Gaussian processes and sparse Bayesian learning · 2002 to 2007
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Sparse Spectral Sampling Gaussian Processes
Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Aníbal R. Figueiras-Vidal
Microsoft Research Technical Report MSR-TR-2007-152
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Sensible Priors for Sparse Bayesian Learning
Joaquin Quiñonero Candela, Edward Snelson, Oliver Williams
Microsoft Research Technical Report MSR-TR-2007-121
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Approximation Methods for Gaussian Process Regression
Joaquin Quiñonero Candela, Carl Edward Rasmussen, Christopher K. I. Williams
In Large-Scale Kernel Machines, edited by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston, pages 203 to 224, MIT Press. Also Microsoft Research Technical Report MSR-TR-2007-124
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Gaussian Processes in Practice
Neil Lawrence, Anton Schwaighofer, Joaquin Quiñonero Candela, editors
JMLR Workshop and Conference Proceedings, volume 1
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Local Distance Preservation in the GP-LVM Through Back Constraints
Neil Lawrence, Joaquin Quiñonero Candela
23rd International Conference on Machine Learning, Pittsburgh, pages 512 to 520
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Machine Learning Challenges: Evaluating Predictive Uncertainty, Textual Entailment and Object Recognition Systems
Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc, editors
Lecture Notes in Computer Science, volume 3944, Springer
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Evaluating Predictive Uncertainty Challenge
Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian Sinz, Olivier Bousquet, Bernhard Schölkopf
In Machine Learning Challenges, Lecture Notes in Computer Science, volume 3944, pages 1 to 27, Springer
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A Unifying View of Sparse Approximate Gaussian Process Regression
Joaquin Quiñonero Candela, Carl Edward Rasmussen
Journal of Machine Learning Research, volume 6, pages 1939 to 1959
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Large Margin Non-linear Embedding
Alexander Zien, Joaquin Quiñonero Candela
22nd International Conference on Machine Learning, Bonn, pages 1060 to 1067
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Healing the Relevance Vector Machine through Augmentation
Carl Edward Rasmussen, Joaquin Quiñonero Candela
22nd International Conference on Machine Learning, Bonn, pages 689 to 696
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Analysis of Some Methods for Reduced Rank Gaussian Process Regression
Joaquin Quiñonero Candela, Carl Edward Rasmussen
In Switching and Learning in Feedback Systems, edited by Roderick Murray-Smith and Robert Shorten, Lecture Notes in Computer Science, volume 3355, pages 98 to 127, Springer
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Learning Depth from Stereo
Fabian Sinz, Joaquin Quiñonero Candela, Gökhan H. Bakir, Carl Edward Rasmussen, Matthias O. Franz
26th DAGM Symposium, Lecture Notes in Computer Science, volume 3175, Springer
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Learning with Uncertainty: Gaussian Processes and Relevance Vector Machines
Joaquin Quiñonero Candela
PhD thesis, Technical University of Denmark, IMM-PHD-2004-135
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Propagation of Uncertainty in Bayesian Kernel Models: Application to Multiple-Step Ahead Forecasting
Joaquin Quiñonero Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen
International Conference on Acoustics, Speech and Signal Processing, pages 701 to 704
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Incremental Gaussian Processes
Joaquin Quiñonero Candela, Ole Winther
Advances in Neural Information Processing Systems 15, pages 1001 to 1008
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Gaussian Process Priors with Uncertain Inputs: Application to Multiple-Step Ahead Time Series Forecasting
Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith
Advances in Neural Information Processing Systems 15, pages 529 to 536
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Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines: Application to Multiple-Step Ahead Time-Series Forecasting
Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela
Technical Report IMM-2003-18, Technical University of Denmark
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Time Series Prediction Based on the Relevance Vector Machine with Adaptive Kernels
Joaquin Quiñonero Candela, Lars Kai Hansen
International Conference on Acoustics, Speech and Signal Processing, pages 985 to 988