John Langford
67 papers · 2004–2025 · 9 venues · across top CS/AI venues
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Achievements
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π Interdisciplinary Bridge Β· Silver πΊοΈ Taxonomy Completionist (94) π Conference Polyglot Β· Silver π Academic Marathon Β· Ultra π£ Hot Topic Early Bird
π±
Topic Pioneer
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Conference Loyalist
(22)
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Keyword Trendsetter Combo
(7)
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Grand Slam
π€
Dynamic Duo
(22)
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Triple Crown
π¬
Deep Specialist
(20)
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Keyword Champion
(5)
π§
Keyword Pioneer
π
Trend Setter
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Silver
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Century Club Β· Silver
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Renaissance Researcher
(7)
ποΈ
Keyword Collector
(276)
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Conference Pioneer
Top co-authors
Research topics
Machine Learning
(62)
Optimization & Theory
(37)
Learning Types
(36)
Artificial Intelligence
(30)
Core Methods
(24)
Core AI
(18)
Reinforcement Learning
(16)
Learning Paradigms
(16)
Methods
(16)
Mathematics & Optimization
(12)
Optimization
(12)
Application Areas
(8)
Deep Learning
(5)
Data Science & Analytics
(3)
Robotics
(3)
Keywords
contextual bandit
(18)
online learning
(14)
regret bound
(14)
reinforcement learning
(12)
sample complexity
(10)
latent state
(6)
active learning
(6)
representation learning
(5)
supervised learning
(5)
policy learning
(5)
multi-class classification
(4)
policy optimization
(4)
cost-sensitive classification
(4)
continuous action
(3)
sample-efficient learning
(3)
logarithmic time
(3)
online algorithm
(3)
structured prediction
(3)
convex loss
(3)
continuous control
(2)
Papers
The Belief State Transformer
ICLR 2025
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
NeurIPS 2024
PcLast: Discovering Plannable Continuous Latent States
ICML 2024
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
ICML 2024
Towards Principled Representation Learning from Videos for Reinforcement Learning
ICLR 2024
Principled Offline RL in the Presence of Rich Exogenous Information
ICML 2023
Streaming Active Learning with Deep Neural Networks
ICML 2023
Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting
AAAI 2022
Contextual Bandits with Large Action Spaces: Made Practical
ICML 2022
Interaction-Grounded Learning with Action-Inclusive Feedback
NeurIPS 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
ICML 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
ICLR 2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
COLT 2022
A Contextual Bandit Bake-off
JMLR 2021
ChaCha for Online AutoML
ICML 2021
Interaction-Grounded Learning
ICML 2021
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
ICLR 2021
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
JMLR 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
ICLR 2020
Efficient Contextual Bandits with Continuous Actions
NeurIPS 2020
Empirical Likelihood for Contextual Bandits
NeurIPS 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
ICML 2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
NeurIPS 2020
Active Learning for Cost-Sensitive Classification
JMLR 2019
Contextual bandits with continuous actions: Smoothing, zooming, and adapting
COLT 2019
Contextual Memory Trees
ICML 2019
Efficient Forward Architecture Search
NeurIPS 2019
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
COLT 2019
Provably efficient RL with Rich Observations via Latent State Decoding
ICML 2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
ICML 2019
A Reductions Approach to Fair Classification
ICML 2018
Efficient Contextual Bandits in Non-stationary Worlds
COLT 2018
Learning Deep ResNet Blocks Sequentially using Boosting Theory
ICML 2018
On Oracle-Efficient PAC RL with Rich Observations
NeurIPS 2018
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
ICLR 2018
Active Learning for Cost-Sensitive Classification
ICML 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
ICML 2017
Logarithmic Time One-Against-Some
ICML 2017
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
EMNLP 2017
Off-policy evaluation for slate recommendation
NeurIPS 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
COLT 2017
A Credit Assignment Compiler for Joint Prediction
NeurIPS 2016
Efficient Second Order Online Learning by Sketching
NeurIPS 2016
PAC Reinforcement Learning with Rich Observations
NeurIPS 2016
Search Improves Label for Active Learning
NeurIPS 2016
Efficient and Parsimonious Agnostic Active Learning
NeurIPS 2015
Hands-on Learning to Search for Structured Prediction
NAACL 2015
Learning to Search Better than Your Teacher
ICML 2015
Logarithmic Time Online Multiclass prediction
NeurIPS 2015
A Reliable Effective Terascale Linear Learning System
JMLR 2014
Resourceful Contextual Bandits
COLT 2014
Scalable Non-linear Learning with Adaptive Polynomial Expansions
NeurIPS 2014
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
ICML 2014
Contextual Bandit Learning with Predictable Rewards
AISTATS 2012
Contextual Bandit Algorithms with Supervised Learning Guarantees
AISTATS 2011
Agnostic Active Learning Without Constraints
NeurIPS 2010
Learning from Logged Implicit Exploration Data
NeurIPS 2010
Hash Kernels for Structured Data
JMLR 2009
Multi-Label Prediction via Compressed Sensing
NeurIPS 2009
Slow Learners are Fast
NeurIPS 2009
Sparse Online Learning via Truncated Gradient
JMLR 2009
Predictive Indexing for Fast Search
NeurIPS 2008
Sparse Online Learning via Truncated Gradient
NeurIPS 2008
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information
NeurIPS 2007
Hash Kernels
AISTATS 2005
Tutorial on Practical Prediction Theory for Classification
JMLR 2005
Computable Shell Decomposition Bounds
JMLR 2004