John Langford

John Langford, Ruben Ortega: Machine learning and algorithms; agile development. Commun. ACM 55(8): 10-11 (2012)

John Langford: Parallel machine learning on big data. ACM Crossroads 19(1): 60-62 (2012)

John Langford, Lihong Li, R. Preston McAfee, Kishore Papineni: Cloud control: voluntary admission control for intranet traffic management. Inf. Syst. E-Business Management 10(3): 295-308 (2012)

Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire: Contextual Bandit Learning with Predictable Rewards. Journal of Machine Learning Research - Proceedings Track 22: 19-26 (2012)

Lihong Li, Wei Chu, John Langford, Taesup Moon, Xuanhui Wang: Bandits with Generalized Linear Models. Journal of Machine Learning Research - Proceedings Track 26: 19-36 (2012)

Alina Beygelzimer, John Langford, David M. Pennock: Learning performance of prediction markets with Kelly bettors. AAMAS 2012: 1317-1318

Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li: Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. UAI 2012: 247-254

Alina Beygelzimer, John Langford, David Pennock: Learning Performance of Prediction Markets with Kelly Bettors. CoRR abs/1201.6655 (2012)

Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire: Contextual Bandit Learning with Predictable Rewards. CoRR abs/1202.1334 (2012)

John Langford, Joelle Pineau: Proceedings of the 29th International Conference on Machine Learning (ICML-12). CoRR abs/1207.4676 (2012)

Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li: Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. CoRR abs/1210.4862 (2012)

John Langford, Judy Robertson: Conferences and video lectures; scientific educational games. Commun. ACM 54(12): 8-9 (2011)

Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire: Contextual Bandit Algorithms with Supervised Learning Guarantees. Journal of Machine Learning Research - Proceedings Track 15: 19-26 (2011)

Miroslav Dudík, John Langford, Lihong Li: Doubly Robust Policy Evaluation and Learning. ICML 2011: 1097-1104

Miroslav Dudík, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang: Efficient Optimal Learning for Contextual Bandits. UAI 2011: 169-178

Nikos Karampatziakis, John Langford: Online Importance Weight Aware Updates. UAI 2011: 392-399

Lihong Li, Wei Chu, John Langford, Xuanhui Wang: Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. WSDM 2011: 297-306

Daniel Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola: Parallel Online Learning. CoRR abs/1103.4204 (2011)

Miroslav Dudík, John Langford, Lihong Li: Doubly Robust Policy Evaluation and Learning. CoRR abs/1103.4601 (2011)

Miroslav Dudík, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang: Efficient Optimal Learning for Contextual Bandits. CoRR abs/1106.2369 (2011)

Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford: A Reliable Effective Terascale Linear Learning System. CoRR abs/1110.4198 (2011)

John Langford, Lihong Li, Yevgeniy Vorobeychik, Jennifer Wortman: Maintaining Equilibria During Exploration in Sponsored Search Auctions. Algorithmica 58(4): 990-1021 (2010)

John Langford: Robust Efficient Conditional Probability Estimation. COLT 2010: 316-317

Alina Beygelzimer, Daniel Hsu, John Langford, Tong Zhang: Agnostic Active Learning Without Constraints. NIPS 2010: 199-207

Alexander L. Strehl, John Langford, Lihong Li, Sham Kakade: Learning from Logged Implicit Exploration Data. NIPS 2010: 2217-2225

Lihong Li, Wei Chu, John Langford, Robert E. Schapire: A contextual-bandit approach to personalized news article recommendation. WWW 2010: 661-670

John Langford: Efficient Exploration in Reinforcement Learning. Encyclopedia of Machine Learning 2010: 309-311

Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire: An Optimal High Probability Algorithm for the Contextual Bandit Problem. CoRR abs/1002.4058 (2010)

Alexander L. Strehl, John Langford, Sham M. Kakade: Learning from Logged Implicit Exploration Data. CoRR abs/1003.0120 (2010)

Lihong Li, Wei Chu, John Langford, Robert E. Schapire: A Contextual-Bandit Approach to Personalized News Article Recommendation. CoRR abs/1003.0146 (2010)

Lihong Li, Wei Chu, John Langford: An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms. CoRR abs/1003.5956 (2010)

Alina Beygelzimer, Daniel Hsu, John Langford, Tong Zhang: Agnostic Active Learning Without Constraints. CoRR abs/1006.2588 (2010)

Nikos Karampatziakis, John Langford: Importance Weight Aware Gradient Updates. CoRR abs/1011.1576 (2010)

Maria-Florina Balcan, Alina Beygelzimer, John Langford: Agnostic active learning. J. Comput. Syst. Sci. 75(1): 78-89 (2009)

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan: Hash Kernels. Journal of Machine Learning Research - Proceedings Track 5: 496-503 (2009)

John Langford, Lihong Li, Tong Zhang: Sparse Online Learning via Truncated Gradient. Journal of Machine Learning Research 10: 777-801 (2009)

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan: Hash Kernels for Structured Data. Journal of Machine Learning Research 10: 2615-2637 (2009)

Hal Daumé III, John Langford, Daniel Marcu: Search-based structured prediction. Machine Learning 75(3): 297-325 (2009)

Nicholas J. Hopper, Luis von Ahn, John Langford: Provably Secure Steganography. IEEE Trans. Computers 58(5): 662-676 (2009)

Alina Beygelzimer, John Langford, Pradeep D. Ravikumar: Error-Correcting Tournaments. ALT 2009: 247-262

Alina Beygelzimer, Sanjoy Dasgupta, John Langford: Importance weighted active learning. ICML 2009: 7

John Langford, Ruslan Salakhutdinov, Tong Zhang: Learning nonlinear dynamic models. ICML 2009: 75

Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg: Feature hashing for large scale multitask learning. ICML 2009: 140

Alina Beygelzimer, John Langford, Bianca Zadrozny: Tutorial summary: Reductions in machine learning. ICML 2009: 172

Sanjoy Dasgupta, John Langford: Tutorial summary: Active learning. ICML 2009: 178

Alina Beygelzimer, John Langford: The offset tree for learning with partial labels. KDD 2009: 129-138

Daniel Hsu, Sham Kakade, John Langford, Tong Zhang: Multi-Label Prediction via Compressed Sensing. NIPS 2009: 772-780

Martin Zinkevich, Alex J. Smola, John Langford: Slow Learners are Fast. NIPS 2009: 2331-2339

Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl: Conditional Probability Tree Estimation Analysis and Algorithms. UAI 2009: 51-58

Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang: Multi-Label Prediction via Compressed Sensing. CoRR abs/0902.1284 (2009)

Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola: Feature Hashing for Large Scale Multitask Learning. CoRR abs/0902.2206 (2009)

Alina Beygelzimer, John Langford, Pradeep D. Ravikumar: Error-Correcting Tournaments. CoRR abs/0902.3176 (2009)

Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl: Conditional Probability Tree Estimation Analysis and Algorithms. CoRR abs/0903.4217 (2009)

John Langford, Ruslan Salakhutdinov, Tong Zhang: Learning Nonlinear Dynamic Models. CoRR abs/0905.3369 (2009)

Hal Daumé III, John Langford, Daniel Marcu: Search-based Structured Prediction. CoRR abs/0907.0786 (2009)

Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin: Robust reductions from ranking to classification. Machine Learning 72(1-2): 139-153 (2008)

John Langford, Alexander L. Strehl, Jennifer Wortman: Exploration scavenging. ICML 2008: 528-535

Sharad Goel, John Langford, Alexander L. Strehl: Predictive Indexing for Fast Search. NIPS 2008: 505-512

John Langford, Lihong Li, Tong Zhang: Sparse Online Learning via Truncated Gradient. NIPS 2008: 905-912

Nicolas S. Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock: Self-financed wagering mechanisms for forecasting. ACM Conference on Electronic Commerce 2008: 170-179

John Langford, Lihong Li, Tong Zhang: Sparse Online Learning via Truncated Gradient. CoRR abs/0806.4686 (2008)

Alina Beygelzimer, John Langford: The Offset Tree for Learning with Partial Labels. CoRR abs/0812.4044 (2008)

Alina Beygelzimer, Sanjoy Dasgupta, John Langford: Importance Weighted Active Learning. CoRR abs/0812.4952 (2008)

Peter Grünwald, John Langford: Suboptimal behavior of Bayes and MDL in classification under misspecification. Machine Learning 66(2-3): 119-149 (2007)

Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin: Robust Reductions from Ranking to Classification. COLT 2007: 604-619

John Langford, Tong Zhang: The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. NIPS 2007

Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, John Langford: Maintaining Equilibria During Exploration in Sponsored Search Auctions. WINE 2007: 119-130

John Langford, Jeffrey Roy: E-government and public-private partnerships in Canada: when failure is no longer an option. IJEB 4(2): 118-135 (2006)

Jacob Abernethy, John Langford, Manfred K. Warmuth: Continuous Experts and the Binning Algorithm. COLT 2006: 544-558

Maria-Florina Balcan, Alina Beygelzimer, John Langford: Agnostic active learning. ICML 2006: 65-72

Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor. ICML 2006: 97-104

Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman: PAC model-free reinforcement learning. ICML 2006: 881-888

Naoki Abe, Bianca Zadrozny, John Langford: Outlier detection by active learning. KDD 2006: 504-509

John Langford, Roberto Oliveira, Bianca Zadrozny: Predicting Conditional Quantiles via Reduction to Classification. UAI 2006

John Langford: Tutorial on Practical Prediction Theory for Classification. Journal of Machine Learning Research 6: 273-306 (2005)

Alina Beygelzimer, John Langford, Bianca Zadrozny: Weighted One-Against-All. AAAI 2005: 720-725

John Langford, Alina Beygelzimer: Sensitive Error Correcting Output Codes. COLT 2005: 158-172

John Langford: The Cross Validation Problem. COLT 2005: 687-688

Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny: Error limiting reductions between classification tasks. ICML 2005: 49-56

Matti Kääriäinen, John Langford: A comparison of tight generalization error bounds. ICML 2005: 409-416

John Langford, Bianca Zadrozny: Relating reinforcement learning performance to classification performance. ICML 2005: 473-480

Luis von Ahn, Nicholas J. Hopper, John Langford: Covert two-party computation. STOC 2005: 513-522

Luis von Ahn, Manuel Blum, John Langford: Telling humans and computers apart automatically. Commun. ACM 47(2): 56-60 (2004)

John Langford, David A. McAllester: Computable Shell Decomposition Bounds. Journal of Machine Learning Research 5: 529-547 (2004)

Peter Grünwald, John Langford: Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification. COLT 2004: 331-347

Naoki Abe, Bianca Zadrozny, John Langford: An iterative method for multi-class cost-sensitive learning. KDD 2004: 3-11

Arindam Banerjee, John Langford: An objective evaluation criterion for clustering. KDD 2004: 515-520

Peter Grünwald, John Langford: Suboptimal behaviour of Bayes and MDL in classification under misspecification. CoRR math.ST/0406221 (2004)

Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford: Reductions Between Classification Tasks. Electronic Colloquium on Computational Complexity (ECCC)(077) (2004)

John Langford, Avrim Blum: Microchoice Bounds and Self Bounding Learning Algorithms. Machine Learning 51(2): 165-179 (2003)

Avrim Blum, John Langford: PAC-MDL Bounds. COLT 2003: 344-357

Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford: CAPTCHA: Using Hard AI Problems for Security. EUROCRYPT 2003: 294-311

Bianca Zadrozny, John Langford, Naoki Abe: Cost-Sensitive Learning by Cost-Proportionate Example Weighting. ICDM 2003: 435-

Sham Kakade, Michael J. Kearns, John Langford: Exploration in Metric State Spaces. ICML 2003: 306-312

Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz: Correlated equilibria in graphical games. ACM Conference on Electronic Commerce 2003: 42-47

Nicholas J. Hopper, John Langford, Luis von Ahn: Provably Secure Steganography. CRYPTO 2002: 77-92

Sham Kakade, John Langford: Approximately Optimal Approximate Reinforcement Learning. ICML 2002: 267-274

John Langford: Combining Trainig Set and Test Set Bounds. ICML 2002: 331-338

John Langford, Martin Zinkevich, Sham Kakade: Competitive Analysis of the Explore/Exploit Tradeoff. ICML 2002: 339-346

John Langford, John Shawe-Taylor: PAC-Bayes & Margins. NIPS 2002: 423-430

Nicholas J. Hopper, John Langford, Luis von Ahn: Provably Secure Steganography. IACR Cryptology ePrint Archive 2002: 137 (2002)

John Langford, Matthias Seeger, Nimrod Megiddo: An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML 2001: 290-297

John Langford, Rich Caruana: (Not) Bounding the True Error. NIPS 2001: 809-816

Sebastian Thrun, John Langford, Vandi Verma: Risk Sensitive Particle Filters. NIPS 2001: 961-968

John Langford, David A. McAllester: Computable Shell Decomposition Bounds. COLT 2000: 25-34

Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum: FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000: 703-710

Avrim Blum, Adam Kalai, John Langford: Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT 1999: 203-208

John Langford, Avrim Blum: Microchoice Bounds and Self Bounding Learning Algorithms. COLT 1999: 209-214

Avrim Blum, John Langford: Probabilistic Planning in the Graphplan Framework. ECP 1999: 319-332

Sebastian Thrun, John Langford, Dieter Fox: Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. ICML 1999: 415-424

Avrim Blum, Carl Burch, John Langford: On Learning Monotone Boolean Functions. FOCS 1998: 408-415