John Langford

John Langford is a person.

• See: Cost-Sensitive Learning, Feature Vector Hashing, Vowpal Wabbit, Contextual Bandit Algorithm, Efficient Exploration in Reinforcement Learning, Feature Vector Hashing; Counterfactual Evaluation.

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References

2017a

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/John_Langford_(computer_scientist) Retrieved:2017-2-15.
  • John Langford is a machine learning research scientist, a field which he says "is shifting from an academic discipline to an industrial tool". He is the author of the weblog hunch.net and the principal developer of Vowpal Wabbit. John works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM's Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. in Computer Science from Carnegie Mellon University in 2002. He was the program co-chair for the 2012 International Conference on Machine Learning.

2017b

• (Swaminathan et al., 2017) ⇒ Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, and Imed Zitouni. (2017). “Off-policy Evaluation for Slate Recommendation.” In:

2010

• (Li et al., 2010) ⇒ Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. (2010). “A Contextual-bandit Approach to Personalized News Article Recommendation.” In: Proceedings of the 19th International Conference on World wide web. doi:10.1145/1772690.1772758
• (Langford & Beygelzimer, 2010) ⇒ John Langford, and Alina Beygelzimer. (2010). “Learning through Exploration." Tutorial at the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1866286.1866290

2009

• (Weinberger et al., 2009) ⇒ Kilian Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, and Josh Attenberg. (2009). “Feature Hashing for Large Scale Multitask Learning.” In: Proceedings of the 26th Annual International Conference on Machine Learning. doi:10.1145/1553374.1553516
• (Beygelzimer & Langford, 2009) ⇒ Alina Beygelzimer, and John Langford. (2009). “The Offset Tree for Learning with Partial Labels.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557040

2008

• (Langford et al., 2008) ⇒ John Langford, Alexander Strehl, and Jennifer Wortman. (2008). “Exploration Scavenging.” In: Proceedings of the 25th International Conference on Machine learning (ICML-2008).

2005

• (Langford & Schapire, 2005) ⇒ John Langford, and Robert Schapire. “Tutorial on Practical Prediction Theory for Classification.” In: Journal of machine learning research 6, no. 3 (2005).

2004

• (Abe et al., 2004) ⇒ Naoki Abe, Bianca Zadrozny, and John Langford. (2004). “An iterative method for multi-class cost-sensitive learning.” In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

2003

• (Von Ahn et al., 2003) ⇒ Luis Von Ahn, Manuel Blum, Nicholas J. Hopper, and John Langford. “CAPTCHA: Using hard AI problems for security." In Advances in Cryptology — EUROCRYPT 2003.

2000

• (Tenenbaum et al., 2000) ⇒ Joshua B. Tenenbaum, Vin De Silva, Thomas L. Griffiths, and John C. Langford. (2000). “A Global Geometric Framework for Nonlinear Dimensionality Reduction.” In: Science, 290(5500).

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