John Langford
#21,416
Most Influential Person Now
American computer scientist
John Langford 's AcademicInfluence.com Rankings
John Langford computer-science Degrees
Computer Science
#1137
World Rank
#1177
Historical Rank
#599
USA Rank
Machine Learning
#107
World Rank
#108
Historical Rank
#38
USA Rank
Artificial Intelligence
#346
World Rank
#353
Historical Rank
#128
USA Rank
Database
#1574
World Rank
#1652
Historical Rank
#406
USA Rank
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Computer Science
John Langford 's Degrees
- PhD Computer Science Carnegie Mellon University
- Masters Computer Science Carnegie Mellon University
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Why Is John Langford Influential?
(Suggest an Edit or Addition)According to Wikipedia, John Langford is a computer scientist working in machine learning and learning theory, a field that he says, "is shifting from an academic discipline to an industrial tool". He is well known for work on the Isomap embedding algorithm, CAPTCHA challenges, Cover Trees for nearest neighbor search, Contextual Bandits for reinforcement learning applications, and learning reductions.
John Langford 's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- A global geometric framework for nonlinear dimensionality reduction. (2000) (13289)
- A contextual-bandit approach to personalized news article recommendation (2010) (2366)
- CAPTCHA: Using Hard AI Problems for Security (2003) (1596)
- Feature hashing for large scale multitask learning (2009) (946)
- Telling humans and computers apart automatically (2004) (904)
- Cover trees for nearest neighbor (2006) (867)
- Approximately Optimal Approximate Reinforcement Learning (2002) (785)
- A Reductions Approach to Fair Classification (2018) (732)
- Cost-sensitive learning by cost-proportionate example weighting (2003) (706)
- Search-based structured prediction (2009) (596)
- The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information (2007) (573)
- Doubly Robust Policy Evaluation and Learning (2011) (565)
- Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2010) (521)
- Agnostic active learning (2006) (513)
- Sparse Online Learning via Truncated Gradient (2008) (471)
- PAC model-free reinforcement learning (2006) (453)
- Multi-Label Prediction via Compressed Sensing (2009) (431)
- Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (2014) (422)
- Scaling up machine learning: parallel and distributed approaches (2011) (399)
- The Epoch-Greedy algorithm for contextual multi-armed bandits (2007) (392)
- A reliable effective terascale linear learning system (2011) (368)
- Slow Learners are Fast (2009) (366)
- Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds (2019) (355)
- Importance weighted active learning (2008) (346)
- Outlier detection by active learning (2006) (335)
- Tutorial on Practical Prediction Theory for Classification (2005) (328)
- Beating the hold-out: bounds for K-fold and progressive cross-validation (1999) (314)
- Contextual Decision Processes with low Bellman rank are PAC-Learnable (2016) (309)
- Graph approximations to geodesics on embedded manifolds (2000) (301)
- Contextual Bandit Algorithms with Supervised Learning Guarantees (2010) (271)
- Efficient Optimal Learning for Contextual Bandits (2011) (257)
- Hash Kernels for Structured Data (2009) (246)
- Learning from Logged Implicit Exploration Data (2010) (221)
- Learning to Search Better than Your Teacher (2015) (216)
- Doubly Robust Policy Evaluation and Optimization (2014) (204)
- Mapping Instructions and Visual Observations to Actions with Reinforcement Learning (2017) (201)
- PAC-Bayes & Margins (2002) (198)
- Provably Secure Steganography (2002) (187)
- Off-policy evaluation for slate recommendation (2016) (179)
- Agnostic Active Learning Without Constraints (2010) (171)
- Efficient Exploration in Reinforcement Learning (2010) (170)
- The offset tree for learning with partial labels (2008) (169)
- PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (2020) (168)
- Provably efficient RL with Rich Observations via Latent State Decoding (2019) (163)
- An iterative method for multi-class cost-sensitive learning (2004) (159)
- Probabilistic Planning in the Graphplan Framework (1999) (155)
- Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches (2018) (149)
- Exploration in Metric State Spaces (2003) (136)
- Exploration scavenging (2008) (133)
- PAC Reinforcement Learning with Rich Observations (2016) (118)
- (Not) Bounding the True Error (2001) (112)
- Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning (2019) (108)
- Telling Humans and Computers Apart Automatically or How Lazy Cryptographers do AI (2002) (108)
- Reduction A Global Geometric Framework for Nonlinear Dimensionality (2011) (105)
- Correlated equilibria in graphical games (2003) (104)
- Efficient Contextual Bandits in Non-stationary Worlds (2017) (104)
- Resourceful Contextual Bandits (2014) (97)
- Making Contextual Decisions with Low Technical Debt (2016) (92)
- Sensitive Error Correcting Output Codes (2005) (89)
- Error limiting reductions between classification tasks (2005) (88)
- Conditional Probability Tree Estimation Analysis and Algorithms (2009) (86)
- Learning Deep ResNet Blocks Sequentially using Boosting Theory (2017) (85)
- Risk Sensitive Particle Filters (2001) (84)
- Error-Correcting Tournaments (2009) (83)
- PAC Bayes and Margins (2003) (81)
- Hash Kernels (2009) (80)
- Online Importance Weight Aware Updates (2010) (80)
- Efficient Second Order Online Learning by Sketching (2016) (78)
- Logarithmic Time Online Multiclass prediction (2014) (77)
- Suboptimal behavior of Bayes and MDL in classification under misspecification (2004) (75)
- On Oracle-Efficient PAC RL with Rich Observations (2018) (74)
- A Contextual Bandit Bake-off (2018) (74)
- Normalized Online Learning (2013) (69)
- PAC-MDL Bounds (2003) (65)
- An unbiased offline evaluation of contextual bandit algorithms with generalized linear models (2011) (65)
- Active Learning for Cost-Sensitive Classification (2017) (64)
- Relating reinforcement learning performance to classification performance (2005) (62)
- Contextual Bandit Learning with Predictable Rewards (2012) (60)
- FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (2000) (53)
- On learning monotone Boolean functions (1998) (53)
- Estimating Class Membership Probabilities using Classifier Learners (2005) (47)
- Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting (2019) (46)
- Learning nonlinear dynamic models (2009) (46)
- Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes (1999) (44)
- An Improved Predictive Accuracy Bound for Averaging Classifiers (2001) (42)
- Efficient and Parsimonious Agnostic Active Learning (2015) (42)
- Logarithmic Time One-Against-Some (2016) (42)
- Self-financed wagering mechanisms for forecasting (2008) (41)
- Weighted One-Against-All (2005) (41)
- Non-Parametric Fault Identification for Space R overs (2001) (39)
- Quantitatively tight sample complexity bounds (2002) (38)
- Covert two-party computation (2005) (37)
- An objective evaluation criterion for clustering (2004) (36)
- Scaling up Machine Learning (2011) (35)
- Efficient Forward Architecture Search (2019) (34)
- Machine Learning Techniques—Reductions Between Prediction Quality Metrics (2008) (33)
- Computable Shell Decomposition Bounds (2000) (33)
- The arbitrariness of reviews, and advice for school administrators (2015) (33)
- Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits (2012) (32)
- Microchoice Bounds and Self Bounding Learning Algorithms (1999) (31)
- Robust reductions from ranking to classification (2007) (31)
- A Multiworld Testing Decision Service (2016) (31)
- Federated Residual Learning (2020) (29)
- Learning performance of prediction markets with Kelly bettors (2012) (29)
- An Optimal High Probability Algorithm for the Contextual Bandit Problem (2010) (26)
- An axiomatic characterization of wagering mechanisms (2015) (25)
- Practical Evaluation and Optimization of Contextual Bandit Algorithms (2018) (25)
- Search Improves Label for Active Learning (2016) (25)
- Predicting Conditional Quantiles via Reduction to Classification (2006) (23)
- Parallel Online Learning (2011) (22)
- Maintaining Equilibria During Exploration in Sponsored Search Auctions (2010) (22)
- Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback (2019) (22)
- Learning Reductions That Really Work (2015) (21)
- Efficient programmable learning to search (2014) (21)
- Open Problem: First-Order Regret Bounds for Contextual Bandits (2017) (21)
- A comparison of tight generalization error bounds (2005) (20)
- Reducing T-step reinforcement learning to classifica-tion (2003) (20)
- Learning to Search for Dependencies (2015) (19)
- A Credit Assignment Compiler for Joint Prediction (2014) (19)
- Predictive Indexing for Fast Search (2008) (19)
- Continuous Experts and the Binning Algorithm (2006) (18)
- Efficient Contextual Bandits with Continuous Actions (2020) (17)
- Search-Based Structured Prediction as Classification (17)
- Scaling Up Machine Learning: Supervised and Unsupervised Learning Algorithms (2011) (16)
- A Review of Bot Protection using CAPTCHA for Web Security (2013) (16)
- Learning the Linear Quadratic Regulator from Nonlinear Observations (2020) (16)
- Proceedings of the 29th International Conference on Machine Learning (ICML-12) (2012) (15)
- Maintaining Equilibria During Exploration in Sponsored Search Auctions (2007) (15)
- Model-Based Reinforcement Learning in Contextual Decision Processes (2018) (14)
- Provable RL with Exogenous Distractors via Multistep Inverse Dynamics (2021) (13)
- Reductions Between Classification Tasks (2004) (13)
- Monte Carlo Hidden Markov Models (1998) (12)
- Tutorial summary: Active learning (2009) (12)
- Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models (2022) (11)
- Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information (2022) (10)
- Combining Trainig Set and Test Set Bounds (2002) (10)
- Proceedings of the Twenty-Ninth International Conference on Machine Learning (2012) (10)
- Importance Weight Aware Gradient Updates (2010) (10)
- On Oracle-Efficient PAC Reinforcement Learning with Rich Observations (2018) (10)
- Generic quantum block compression (2001) (9)
- On Polynomial Time PAC Reinforcement Learning with Rich Observations (2018) (9)
- The Cross Validation Problem (2005) (9)
- Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics (2022) (8)
- Contextual Bandits with Large Action Spaces: Made Practical (2022) (8)
- Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations (2016) (8)
- Efficient Online Bootstrapping for Large Scale Learning (2013) (7)
- An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms (2010) (7)
- Provable Rich Observation Reinforcement Learning with Combinatorial Latent States (2021) (7)
- Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting (2020) (7)
- Parallel machine learning on big data (2012) (7)
- Contextual Memory Trees (2018) (6)
- Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback (2018) (6)
- Provably secure steganography: (Extended abstract) (2002) (6)
- Machine learning and algorithms; agile development (2012) (6)
- Scalable Non-linear Learning with Adaptive Polynomial Expansions (2014) (6)
- Competitive Analysis of the Explore/Exploit Tradeoff (2002) (6)
- CentMail: Rate Limiting via Certified Micro-Donations (2009) (5)
- Empirical Likelihood for Contextual Bandits (2019) (5)
- Interaction-Grounded Learning (2021) (4)
- Para-active learning (2013) (4)
- Cloud control: voluntary admission control for intranet traffic management (2012) (4)
- Scaling Up Machine Learning: Scaling Up Machine Learning: Introduction (2011) (3)
- Interaction-Grounded Learning with Action-inclusive Feedback (2022) (3)
- ChaCha for Online AutoML (2021) (3)
- Scaling Up Machine Learning: Subject Index (2011) (3)
- Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning (2022) (3)
- MACHINE LEARNING REDUCTIONS (2003) (2)
- Robust Efficient Conditional Probability Estimation (2010) (2)
- Tutorial summary: Reductions in machine learning (2009) (2)
- Conferences and video lectures; scientific educational games (2011) (1)
- The Binning Algorithm (1)
- Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization (2022) (1)
- Active Learning with an ERM Oracle (2009) (1)
- Learning through exploration (2010) (1)
- Bandits with Generalized Linear Models (2012) (1)
- The solution to AI, what real researchers do, and expectations for CS classrooms (2016) (1)
- L G ] 2 4 O ct 2 01 6 Search Improves Label for Active Learning (2018) (1)
- Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication (2020) (1)
- Finding a research job, and teaching CS in high school (2014) (1)
- Resourceful Contextual Bandits * Ashwinkumar Badanidiyuru (2015) (0)
- Lessons from learning theory for benchmark design (2004) (0)
- Active Learning via Reduction To Supervised Classication (2010) (0)
- AReductionsApproach to FairClassification (2017) (0)
- Towards Data-Driven Offline Simulations for Online Reinforcement Learning (2022) (0)
- Cloud control: voluntary admission control for intranet traffic management (2011) (0)
- Learning Reductions That Really Work This paper summarizes the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of tasks. (2016) (0)
- Assignment 2 - Deep Learning with Sparse Coding (2013) (0)
- Competitive Analysis of the Explore / Exploit Tradeo (2002) (0)
- Distributed Machine Learning (2011) (0)
- Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information (2022) (0)
- Watermarking Shape Datasets with Utility and Distance Preservation (2016) (0)
- Robust Learning with FeatureBoostJoseph (2007) (0)
- MULTISTEP INVERSE DYNAMICS (0)
- An Improved Predi tive A ura y Bound for Averaging Classi ersJohn (2001) (0)
- Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models (2022) (0)
- Subject index (2004) (0)
- Hands-on Learning to Search for Structured Prediction (2015) (0)
- Streaming Active Learning with Deep Neural Networks (2023) (0)
- Doubly Robust Policy Evaluation and Optimization 1 (2015) (0)
- Contextual reinforcement learning (2017) (0)
- Final Program Report : SAMSI Computational Advertising Program Summer (2012) (0)
- The 2nd Learning from Limited Labeled Data (LLD) Workshop: Representation Learning for Weak Supervision and Beyond (0)
- PACT : P rivacy-Sensitive Protocols A nd Mechanisms for Mobile C ontact T racing (2020) (0)
- Scaling Up Machine Learning: Preface (2011) (0)
- Provably Secure Steganography ( Extended (0)
- Decision Theoretic Particle Filters (0)
- Resourceful Contextual (2014) (0)
- Logarithmic Time Prediction (2013) (0)
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