SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation
Lester Mackey, Miro Dudík, Alex Chouldechova
October 2024
Miro Dudík
Sr Principal Researcher Manager
Publications
Publications by Year
-
Convex Analysis at Infinity: An Introduction to Astral Space
Miro Dudík, Robert E. Schapire, Matus Telgarsky
July 2024
A structured regression approach for evaluating model performance across intersectional subgroups
Christine Herlihy, Kimberly Truong, Alex Chouldechova, Miro Dudík
FAccT 2024 | January 2024
-
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudík, John Langford, Alex Lamb
ICML 2024 | November 2023
-
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire
2022 Neural Information Processing Systems | May 2022
-
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
2021 International Conference on Machine Learning | July 2021
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
-
Assessing and mitigating unfairness in credit models with the Fairlearn toolkit
Miro Dudík, William Chen, Solon Barocas, Mario Inchiosa, Nick Lewins, Miruna Oprescu, Joy Qiao, Mehrnoosh Sameki, Mario Schlener, Jason Tuo, Hanna Wallach
MSR-TR-2020-34 | September 2020
Published by Microsoft
Log-time Prediction Markets for Interval Securities
Miro Dudík, Xintong Wang, David M. Pennock, David Rothschild
September 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Gradient Descent Follows the Regularization Path for General Losses
Ziwei Ji, Miro Dudík, Robert E. Schapire, Matus Telgarsky
COLT 2020 | June 2020
Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kiante Brantley, Miro Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
June 2020
Fairlearn: A toolkit for assessing and improving fairness in AI
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, Kathleen Walker
MSR-TR-2020-32 | May 2020
Published by Microsoft
-
Multifactorial Model to Predict Response to PD-(L)1 Blockade in Patients with High PD-L1 Metastatic Non-Small Cell Lung Cancer
K. Arbour, Miruna Oprescu, J. Hakim, H. Rizvi, M. Leiserson, M. Ginsburg, A. Plodkowski, J. Sauter, I. Preeshagul, Sharon Gillett, Philip Rosenfield, Lester Mackey, Miro Dudík, M. Hellmann
Journal of Thoracic Oncology | October 2019, Vol 14(10): pp. S290
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Alekh Agarwal, Miro Dudík, Zhiwei Steven Wu
36th International Conference on Machine Learning | September 2019
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
-
Perspective annotation for numerical representations
Jake Hofman, Miro Dudík, Daniel G. Goldstein
December 2018
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miro Dudík, Yisong Yue, Hal Daumé III
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miro Dudík, Haipeng Luo, Robert E. Schapire
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
-
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miro Dudík
The 34th International Conference on Machine Learning (ICML 2017) | August 2017
A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
-
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
-
Contextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
Proceedings of The 28th Conference on Learning Theory (COLT)
Budget Constraints in Prediction Markets
Miro Dudík, Nikhil Devanur, Zhiyi Huang, Dave Pennock
In Proc. UAI 2015 | January 2015
-
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
-
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
-
Efficient optimal learning for contextual bandits
Nikos Karampatziakis, John Langford, Miroslav Dudik, Lev Reyzin, Tong Zhang, Miro Dudík
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) | July 2011
Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
-
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
Publications by Research Area
-
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation
Lester Mackey, Miro Dudík, Alex Chouldechova
October 2024
Convex Analysis at Infinity: An Introduction to Astral Space
Miro Dudík, Robert E. Schapire, Matus Telgarsky
July 2024
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudík, John Langford, Alex Lamb
ICML 2024 | November 2023
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire
2022 Neural Information Processing Systems | May 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
2021 International Conference on Machine Learning | July 2021
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Assessing and mitigating unfairness in credit models with the Fairlearn toolkit
Miro Dudík, William Chen, Solon Barocas, Mario Inchiosa, Nick Lewins, Miruna Oprescu, Joy Qiao, Mehrnoosh Sameki, Mario Schlener, Jason Tuo, Hanna Wallach
MSR-TR-2020-34 | September 2020
Published by Microsoft
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Gradient Descent Follows the Regularization Path for General Losses
Ziwei Ji, Miro Dudík, Robert E. Schapire, Matus Telgarsky
COLT 2020 | June 2020
Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kiante Brantley, Miro Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
June 2020
Fairlearn: A toolkit for assessing and improving fairness in AI
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, Kathleen Walker
MSR-TR-2020-32 | May 2020
Published by Microsoft
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Alekh Agarwal, Miro Dudík, Zhiwei Steven Wu
36th International Conference on Machine Learning | September 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Perspective annotation for numerical representations
Jake Hofman, Miro Dudík, Daniel G. Goldstein
December 2018
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miro Dudík, Haipeng Luo, Robert E. Schapire
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miro Dudík, Yisong Yue, Hal Daumé III
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miro Dudík
The 34th International Conference on Machine Learning (ICML 2017) | August 2017
A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Contextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
Proceedings of The 28th Conference on Learning Theory (COLT)
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
Efficient optimal learning for contextual bandits
Nikos Karampatziakis, John Langford, Miroslav Dudik, Lev Reyzin, Tong Zhang, Miro Dudík
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) | July 2011
Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
-
Convex Analysis at Infinity: An Introduction to Astral Space
Miro Dudík, Robert E. Schapire, Matus Telgarsky
July 2024
-
A structured regression approach for evaluating model performance across intersectional subgroups
Christine Herlihy, Kimberly Truong, Alex Chouldechova, Miro Dudík
FAccT 2024 | January 2024
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
-
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
-
Log-time Prediction Markets for Interval Securities
Miro Dudík, Xintong Wang, David M. Pennock, David Rothschild
September 2020
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
Budget Constraints in Prediction Markets
Miro Dudík, Nikhil Devanur, Zhiyi Huang, Dave Pennock
In Proc. UAI 2015 | January 2015
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
-
Multifactorial Model to Predict Response to PD-(L)1 Blockade in Patients with High PD-L1 Metastatic Non-Small Cell Lung Cancer
K. Arbour, Miruna Oprescu, J. Hakim, H. Rizvi, M. Leiserson, M. Ginsburg, A. Plodkowski, J. Sauter, I. Preeshagul, Sharon Gillett, Philip Rosenfield, Lester Mackey, Miro Dudík, M. Hellmann
Journal of Thoracic Oncology | October 2019, Vol 14(10): pp. S290
-
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
-
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
-
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
Publications by Type
-
SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation
Lester Mackey, Miro Dudík, Alex Chouldechova
October 2024
Convex Analysis at Infinity: An Introduction to Astral Space
Miro Dudík, Robert E. Schapire, Matus Telgarsky
July 2024
Log-time Prediction Markets for Interval Securities
Miro Dudík, Xintong Wang, David M. Pennock, David Rothschild
September 2020
-
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
A structured regression approach for evaluating model performance across intersectional subgroups
Christine Herlihy, Kimberly Truong, Alex Chouldechova, Miro Dudík
FAccT 2024 | January 2024
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudík, John Langford, Alex Lamb
ICML 2024 | November 2023
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire
2022 Neural Information Processing Systems | May 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
2021 International Conference on Machine Learning | July 2021
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Gradient Descent Follows the Regularization Path for General Losses
Ziwei Ji, Miro Dudík, Robert E. Schapire, Matus Telgarsky
COLT 2020 | June 2020
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Alekh Agarwal, Miro Dudík, Zhiwei Steven Wu
36th International Conference on Machine Learning | September 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miro Dudík, Haipeng Luo, Robert E. Schapire
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miro Dudík, Yisong Yue, Hal Daumé III
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miro Dudík
The 34th International Conference on Machine Learning (ICML 2017) | August 2017
A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Contextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
Proceedings of The 28th Conference on Learning Theory (COLT)
Budget Constraints in Prediction Markets
Miro Dudík, Nikhil Devanur, Zhiyi Huang, Dave Pennock
In Proc. UAI 2015 | January 2015
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
Efficient optimal learning for contextual bandits
Nikos Karampatziakis, John Langford, Miroslav Dudik, Lev Reyzin, Tong Zhang, Miro Dudík
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) | July 2011
Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
-
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Multifactorial Model to Predict Response to PD-(L)1 Blockade in Patients with High PD-L1 Metastatic Non-Small Cell Lung Cancer
K. Arbour, Miruna Oprescu, J. Hakim, H. Rizvi, M. Leiserson, M. Ginsburg, A. Plodkowski, J. Sauter, I. Preeshagul, Sharon Gillett, Philip Rosenfield, Lester Mackey, Miro Dudík, M. Hellmann
Journal of Thoracic Oncology | October 2019, Vol 14(10): pp. S290
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
-
Assessing and mitigating unfairness in credit models with the Fairlearn toolkit
Miro Dudík, William Chen, Solon Barocas, Mario Inchiosa, Nick Lewins, Miruna Oprescu, Joy Qiao, Mehrnoosh Sameki, Mario Schlener, Jason Tuo, Hanna Wallach
MSR-TR-2020-34 | September 2020
Published by Microsoft
Fairlearn: A toolkit for assessing and improving fairness in AI
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, Kathleen Walker
MSR-TR-2020-32 | May 2020
Published by Microsoft
-
Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kiante Brantley, Miro Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
June 2020
Perspective annotation for numerical representations
Jake Hofman, Miro Dudík, Daniel G. Goldstein
December 2018
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013