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Miroslav Dudík
Miro Dudík
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2020 – today
- 2024
- [c51]Christine Herlihy
, Kimberly Truong
A structured regression approach for evaluating model performance across intersectional subgroups. FAccT 2024: 313-325 - [c50]Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan P. Molu, Miroslav Dudík, John Langford, Alex Lamb:
PcLast: Discovering Plannable Continuous Latent States. ICML 2024 - [i39]Christine Herlihy, Kimberly Truong, Alexandra Chouldechova, Miroslav Dudík:
A structured regression approach for evaluating model performance across intersectional subgroups. CoRR abs/2401.14893 (2024) - 2023
- [j6]Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio:
Fairlearn: Assessing and Improving Fairness of AI Systems. J. Mach. Learn. Res. 24: 257:1-257:8 (2023) - [c49]Nataly Brukhim, Miro Dudík, Aldo Pacchiano, Robert E. Schapire:
A Unified Model and Dimension for Interactive Estimation. NeurIPS 2023 - [i38]Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio:
Fairlearn: Assessing and Improving Fairness of AI Systems. CoRR abs/2303.16626 (2023) - [i37]Nataly Brukhim, Miroslav Dudík, Aldo Pacchiano, Robert E. Schapire:
A Unified Model and Dimension for Interactive Estimation. CoRR abs/2306.06184 (2023) - [i36]Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam
PcLast: Discovering Plannable Continuous Latent States. CoRR abs/2311.03534 (2023) - 2022
- [c48]Madalina Vlasceanu, Miroslav Dudík, Ida Momennejad:
Interdisciplinarity, Gender Diversity, and Network Structure Predict the Centrality of AI Organizations. FAccT 2022: 1-10 - [c47]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML 2022: 1945-1962 - [c46]Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire:
Provably sample-efficient RL with side information about latent dynamics. NeurIPS 2022 - [i35]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR abs/2202.05318 (2022) - [i34]Miroslav Dudík, Ziwei Ji, Robert E. Schapire, Matus Telgarsky:
Convex Analysis at Infinity: An Introduction to Astral Space. CoRR abs/2205.03260 (2022) - [i33]Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire:
Provably Sample-Efficient RL with Side Information about Latent Dynamics. CoRR abs/2205.14237 (2022) - 2021
- [j5]Solon Barocas
Responsible computing during COVID-19 and beyond. Commun. ACM 64(7): 30-32 (2021) - [c45]Miroslav Dudík, Xintong Wang, David M. Pennock, David M. Rothschild:
Log-time Prediction Markets for Interval Securities. AAMAS 2021: 465-473 - [c44]Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miroslav Dudík, Patrick Shafto:
Interactive Learning from Activity Description. ICML 2021: 8096-8108 - [c43]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS 2021: 26382-26394 - [i32]Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miroslav Dudík, Patrick Shafto:
Interactive Learning from Activity Description. CoRR abs/2102.07024 (2021) - [i31]Miroslav Dudík, Xintong Wang, David M. Pennock, David M. Rothschild:
Log-time Prediction Markets for Interval Securities. CoRR abs/2102.07308 (2021) - [i30]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR abs/2107.01509 (2021) - 2020
- [j4]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-efficient Online Learning and Auction Design. J. ACM 67(5): 26:1-26:57 (2020) - [c42]Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky:
Gradient descent follows the regularization path for general losses. COLT 2020: 2109-2136 - [c41]Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík:
Doubly robust off-policy evaluation with shrinkage. ICML 2020: 9167-9176 - [c40]Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. NeurIPS 2020 - [i29]Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. CoRR abs/2006.05051 (2020) - [i28]Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky:
Gradient descent follows the regularization path for general losses. CoRR abs/2006.11226 (2020)
2010 – 2019
- 2019
- [c39]Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna M. Wallach:
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? CHI 2019: 600 - [c38]Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. ICML 2019: 120-129 - [c37]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. ICML 2019: 1665-1674 - [c36]Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire:
Reinforcement Learning with Convex Constraints. NeurIPS 2019: 14070-14079 - [i27]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. CoRR abs/1901.09018 (2019) - [i26]Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-based Algorithms. CoRR abs/1905.12843 (2019) - [i25]Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire:
Reinforcement Learning with Convex Constraints. CoRR abs/1906.09323 (2019) - [i24]Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík:
Doubly robust off-policy evaluation with shrinkage. CoRR abs/1907.09623 (2019) - 2018
- [c35]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. ICML 2018: 60-69 - [c34]Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire:
Practical Contextual Bandits with Regression Oracles. ICML 2018: 1534-1543 - [c33]Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III:
Hierarchical Imitation and Reinforcement Learning. ICML 2018: 2923-2932 - [i23]Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III:
Hierarchical Imitation and Reinforcement Learning. CoRR abs/1803.00590 (2018) - [i22]Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire:
Practical Contextual Bandits with Regression Oracles. CoRR abs/1803.01088 (2018) - [i21]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. CoRR abs/1803.02453 (2018) - [i20]Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna M. Wallach:
Improving fairness in machine learning systems: What do industry practitioners need? CoRR abs/1812.05239 (2018) - 2017
- [c32]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Online Learning and Auction Design. FOCS 2017: 528-539 - [c31]Nikhil Rao, Miroslav Dudík, Zaïd Harchaoui:
The group k-support norm for learning with structured sparsity. ICASSP 2017: 2402-2406 - [c30]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. ICML 2017: 3589-3597 - [c29]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. NIPS 2017: 3632-3642 - [c28]Miroslav Dudík, Sébastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan:
A Decomposition of Forecast Error in Prediction Markets. NIPS 2017: 4371-4380 - [i19]Miroslav Dudík, Sébastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan:
A Decomposition of Forecast Error in Prediction Markets. CoRR abs/1702.07810 (2017) - 2016
- [c27]Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík:
Contextual semibandits via supervised learning oracles. NIPS 2016: 2388-2396 - [c26]Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan:
Arbitrage-Free Combinatorial Market Making via Integer Programming. EC 2016: 161-178 - [i18]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. CoRR abs/1605.04812 (2016) - [i17]Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan:
Arbitrage-Free Combinatorial Market Making via Integer Programming. CoRR abs/1606.02825 (2016) - [i16]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Learning and Auction Design. CoRR abs/1611.01688 (2016) - [i15]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. CoRR abs/1612.01205 (2016) - 2015
- [c25]Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi:
Contextual Dueling Bandits. COLT 2015: 563-587 - [c24]Matus Telgarsky, Miroslav Dudík:
Convex Risk Minimization and Conditional Probability Estimation. COLT 2015: 1629-1682 - [c23]Nikhil R. Devanur, Miroslav Dudík, Zhiyi Huang, David M. Pennock:
Budget Constraints in Prediction Markets. UAI 2015: 238-247 - [i14]Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík:
Efficient Contextual Semi-Bandit Learning. CoRR abs/1502.05890 (2015) - [i13]Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi:
Contextual Dueling Bandits. CoRR abs/1502.06362 (2015) - [i12]Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Optimization. CoRR abs/1503.02834 (2015) - [i11]Matus Telgarsky, Miroslav Dudík, Robert E. Schapire:
Convex Risk Minimization and Conditional Probability Estimation. CoRR abs/1506.04513 (2015) - [i10]Nikhil R. Devanur, Miroslav Dudík, Zhiyi Huang, David M. Pennock:
Budget Constraints in Prediction Markets. CoRR abs/1510.02045 (2015) - 2014
- [j3]Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford:
A reliable effective terascale linear learning system. J. Mach. Learn. Res. 15(1): 1111-1133 (2014) - [c22]Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins:
Robust Multi-objective Learning with Mentor Feedback. COLT 2014: 726-741 - [c21]Miroslav Dudík, Rafael M. Frongillo, Jennifer Wortman Vaughan:
Market Making with Decreasing Utility for Information. UAI 2014: 152-161 - [i9]Miroslav Dudík, Rafael M. Frongillo, Jennifer Wortman Vaughan:
Market Making with Decreasing Utility for Information. CoRR abs/1407.8161 (2014) - 2013
- [c20]Miroslav Dudík, Sébastien Lahaie, David M. Pennock, David M. Rothschild:
A combinatorial prediction market for the U.S. elections. EC 2013: 341-358 - [i8]Alekh Agarwal, Léon Bottou, Miroslav Dudík, John Langford:
Para-active learning. CoRR abs/1310.8243 (2013) - 2012
- [c19]Zaïd Harchaoui, Matthijs Douze, Mattis Paulin, Miroslav Dudík, Jérôme Malick:
Large-scale image classification with trace-norm regularization. CVPR 2012: 3386-3393 - [c18]Brian D. Ziebart, Miroslav Dudík, Geoffrey J. Gordon, Katia P. Sycara, Wendi L. Adair
Identifying Culture and Leveraging Cultural Differences for Negotiation Agents. HICSS 2012: 618-627 - [c17]Miroslav Dudík, Sébastien Lahaie, David M. Pennock:
A tractable combinatorial market maker using constraint generation. EC 2012: 459-476 - [c16]Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li:
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. UAI 2012: 247-254 - [c15]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. AISTATS 2012: 19-26 - [c14]Miroslav Dudík, Zaïd Harchaoui, Jérôme Malick:
Lifted coordinate descent for learning with trace-norm regularization. AISTATS 2012: 327-336 - [i7]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. CoRR abs/1202.1334 (2012) - [i6]Geoffrey J. Gordon, Sue Ann Hong, Miroslav Dudík:
First-Order Mixed Integer Linear Programming. CoRR abs/1205.2644 (2012) - [i5]Miroslav Dudík, Geoffrey J. Gordon:
A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form Games. CoRR abs/1205.2649 (2012) - [i4]Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li:
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. CoRR abs/1210.4862 (2012) - 2011
- [c13]Roie Zivan, Miroslav Dudík, Praveen Paruchuri, Katia P. Sycara:
Maximizing revenue in symmetric resource allocation systems when user utilities exhibit diminishing returns. AAMAS 2011: 1165-1166 - [c12]Miroslav Dudík, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Learning. ICML 2011: 1097-1104 - [c11]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. UAI 2011: 169-178 - [e1]Geoffrey J. Gordon, David B. Dunson, Miroslav Dudík:
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011. JMLR Proceedings 15, JMLR.org 2011 [contents] - [i3]Miroslav Dudík, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Learning. CoRR abs/1103.4601 (2011) - [i2]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. CoRR abs/1106.2369 (2011) - [i1]Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford:
A Reliable Effective Terascale Linear Learning System. CoRR abs/1110.4198 (2011) - 2010
- [c10]Roie Zivan, Miroslav Dudík, Steven Okamoto, Katia P. Sycara:
Reducing Untruthful Manipulation in Envy-Free Pareto Optimal Resource Allocation. IAT 2010: 391-398
2000 – 2009
- 2009
- [c9]Miroslav Dudík, Geoffrey J. Gordon:
A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form Games. UAI 2009: 151-160 - [c8]Geoffrey J. Gordon, Sue Ann Hong, Miroslav Dudík:
First-Order Mixed Integer Linear Programming. UAI 2009: 213-222 - 2008
- [c7]Miroslav Dudík, Steven J. Phillips:
Generative and Discriminative Learning with Unknown Labeling Bias. NIPS 2008: 401-408 - 2007
- [j2]Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling. J. Mach. Learn. Res. 8: 1217-1260 (2007) - [c6]Miroslav Dudík, David M. Blei, Robert E. Schapire:
Hierarchical maximum entropy density estimation. ICML 2007: 249-256 - [c5]Jonathan D. Chang, Miroslav Dudík, David M. Blei:
PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks. SemEval@ACL 2007: 272-276 - 2006
- [c4]Miroslav Dudík, Robert E. Schapire:
Maximum Entropy Distribution Estimation with Generalized Regularization. COLT 2006: 123-138 - 2005
- [c3]Miroslav Dudík, Robert E. Schapire, Steven J. Phillips:
Correcting sample selection bias in maximum entropy density estimation. NIPS 2005: 323-330 - 2004
- [c2]Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
Performance Guarantees for Regularized Maximum Entropy Density Estimation. COLT 2004: 472-486 - [c1]Steven J. Phillips, Miroslav Dudík, Robert E. Schapire:
A maximum entropy approach to species distribution modeling. ICML 2004 - 2003
- [j1]Miroslav Dudík, Leonard J. Schulman
Reconstruction from subsequences. J. Comb. Theory A 103(2): 337-348 (2003)
Coauthor Index
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last updated on 2024-10-07 22:08 CEST by the dblp team
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