Timezone: »
This is a tutorial about real-world use of interactive and online learning. We focus on systems for practical applications ranging from recommendation tasks and ad-display, to clinical trials and adaptive decision making in computer systems. There is quite a bit of foundational theory and algorithms from the field of machine learning yet practical use is fraught with several challenges. Success in interactive learning requires a complete learning system which handles exploration, data-flow, logging and real-time updating supporting the core algorithm.
Each potential application also comes with multiple design choices and often do not fit the setting in theory as-is. We cover both foundational principles which have proved practically essential as well as recipes for success from practical experience. After the tutorial, participants should have both a firm understanding of the foundations and the practical ability to deploy and start using such a system in an hour.
Author Information
Alekh Agarwal (Microsoft Research)
John Langford (Microsoft Research)
More from the Same Authors
-
2021 : Provable RL with Exogenous Distractors via Multistep Inverse Dynamics »
Yonathan Efroni · Dipendra Misra · Akshay Krishnamurthy · Alekh Agarwal · John Langford -
2022 : Interaction-Grounded Learning with Action-inclusive Feedback »
Tengyang Xie · Akanksha Saran · Dylan Foster · Lekan Molu · Ida Momennejad · Nan Jiang · Paul Mineiro · John Langford -
2023 Workshop: Interactive Learning with Implicit Human Feedback »
Andi Peng · Akanksha Saran · Andreea Bobu · Tengyang Xie · Pierre-Yves Oudeyer · Anca Dragan · John Langford -
2023 Tutorial: Discovering Agent-Centric Latent States in Theory and in Practice »
John Langford · Alex Lamb -
2023 Expo Talk Panel: Vowpal Wabbit: year in review and looking ahead in an LLM world »
John Langford · Byron Xu · Cheng Tan · Jack Gerrits · Lili Wu · Mark Rucker · Olga Vrousgou -
2022 Poster: Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning »
Alberto Bietti · Chen-Yu Wei · Miroslav Dudik · John Langford · Steven Wu -
2022 Poster: Contextual Bandits with Large Action Spaces: Made Practical »
Yinglun Zhu · Dylan Foster · John Langford · Paul Mineiro -
2022 Spotlight: Contextual Bandits with Large Action Spaces: Made Practical »
Yinglun Zhu · Dylan Foster · John Langford · Paul Mineiro -
2022 Spotlight: Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning »
Alberto Bietti · Chen-Yu Wei · Miroslav Dudik · John Langford · Steven Wu -
2022 : Introduction »
John Langford -
2021 : RL + Recommender Systems Panel »
Alekh Agarwal · Ed Chi · Maria Dimakopoulou · Georgios Theocharous · Minmin Chen · Lihong Li -
2021 : RL Foundation Panel »
Matthew Botvinick · Thomas Dietterich · Leslie Kaelbling · John Langford · Warrren B Powell · Csaba Szepesvari · Lihong Li · Yuxi Li -
2021 Poster: Interaction-Grounded Learning »
Tengyang Xie · John Langford · Paul Mineiro · Ida Momennejad -
2021 Spotlight: Interaction-Grounded Learning »
Tengyang Xie · John Langford · Paul Mineiro · Ida Momennejad -
2021 Poster: ChaCha for Online AutoML »
Qingyun Wu · Chi Wang · John Langford · Paul Mineiro · Marco Rossi -
2021 Spotlight: ChaCha for Online AutoML »
Qingyun Wu · Chi Wang · John Langford · Paul Mineiro · Marco Rossi -
2021 Town Hall: Town Hall »
John Langford · Marina Meila · Tong Zhang · Le Song · Stefanie Jegelka · Csaba Szepesvari -
2021 Expo Workshop: Real World RL: Azure Personalizer & Vowpal Wabbit »
Sheetal Lahabar · Etienne Kintzler · Mark Rucker · Bogdan Mazoure · Qingyun Wu · Pavithra Srinath · Jack Gerrits · Olga Vrousgou · John Langford · Eduardo Salinas -
2020 : Discussion Panel »
Krzysztof Dembczynski · Prateek Jain · Alina Beygelzimer · Inderjit Dhillon · Anna Choromanska · Maryam Majzoubi · Yashoteja Prabhu · John Langford -
2020 Workshop: Workshop on eXtreme Classification: Theory and Applications »
Anna Choromanska · John Langford · Maryam Majzoubi · Yashoteja Prabhu -
2020 Poster: Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning »
Dipendra Kumar Misra · Mikael Henaff · Akshay Krishnamurthy · John Langford -
2019 : panel discussion with Craig Boutilier (Google Research), Emma Brunskill (Stanford), Chelsea Finn (Google Brain, Stanford, UC Berkeley), Mohammad Ghavamzadeh (Facebook AI), John Langford (Microsoft Research) and David Silver (Deepmind) »
Peter Stone · Craig Boutilier · Emma Brunskill · Chelsea Finn · John Langford · David Silver · Mohammad Ghavamzadeh -
2019 : Poster Session 1 (all papers) »
Matilde Gargiani · Yochai Zur · Chaim Baskin · Evgenii Zheltonozhskii · Liam Li · Ameet Talwalkar · Xuedong Shang · Harkirat Singh Behl · Atilim Gunes Baydin · Ivo Couckuyt · Tom Dhaene · Chieh Lin · Wei Wei · Min Sun · Orchid Majumder · Michele Donini · Yoshihiko Ozaki · Ryan P. Adams · Christian Geißler · Ping Luo · zhanglin peng · · Ruimao Zhang · John Langford · Rich Caruana · Debadeepta Dey · Charles Weill · Xavi Gonzalvo · Scott Yang · Scott Yak · Eugen Hotaj · Vladimir Macko · Mehryar Mohri · Corinna Cortes · Stefan Webb · Jonathan Chen · Martin Jankowiak · Noah Goodman · Aaron Klein · Frank Hutter · Mojan Javaheripi · Mohammad Samragh · Sungbin Lim · Taesup Kim · SUNGWOONG KIM · Michael Volpp · Iddo Drori · Yamuna Krishnamurthy · Kyunghyun Cho · Stanislaw Jastrzebski · Quentin de Laroussilhe · Mingxing Tan · Xiao Ma · Neil Houlsby · Andrea Gesmundo · Zalán Borsos · Krzysztof Maziarz · Felipe Petroski Such · Joel Lehman · Kenneth Stanley · Jeff Clune · Pieter Gijsbers · Joaquin Vanschoren · Felix Mohr · Eyke Hüllermeier · Zheng Xiong · Wenpeng Zhang · Wenwu Zhu · Weijia Shao · Aleksandra Faust · Michal Valko · Michael Y Li · Hugo Jair Escalante · Marcel Wever · Andrey Khorlin · Tara Javidi · Anthony Francis · Saurajit Mukherjee · Jungtaek Kim · Michael McCourt · Saehoon Kim · Tackgeun You · Seungjin Choi · Nicolas Knudde · Alexander Tornede · Ghassen Jerfel -
2019 : invited talk by John Langford (Microsoft Research): How do we make Real World Reinforcement Learning revolution? »
John Langford -
2019 Poster: Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback »
Chicheng Zhang · Alekh Agarwal · Hal Daumé III · John Langford · Sahand Negahban -
2019 Poster: Fair Regression: Quantitative Definitions and Reduction-Based Algorithms »
Alekh Agarwal · Miroslav Dudik · Steven Wu -
2019 Oral: Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback »
Chicheng Zhang · Alekh Agarwal · Hal Daumé III · John Langford · Sahand Negahban -
2019 Oral: Fair Regression: Quantitative Definitions and Reduction-Based Algorithms »
Alekh Agarwal · Miroslav Dudik · Steven Wu -
2019 Poster: Provably efficient RL with Rich Observations via Latent State Decoding »
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford -
2019 Poster: Contextual Memory Trees »
Wen Sun · Alina Beygelzimer · Hal Daumé III · John Langford · Paul Mineiro -
2019 Oral: Provably efficient RL with Rich Observations via Latent State Decoding »
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford -
2019 Oral: Contextual Memory Trees »
Wen Sun · Alina Beygelzimer · Hal Daumé III · John Langford · Paul Mineiro -
2018 Poster: Hierarchical Imitation and Reinforcement Learning »
Hoang Le · Nan Jiang · Alekh Agarwal · Miroslav Dudik · Yisong Yue · Hal Daumé III -
2018 Poster: A Reductions Approach to Fair Classification »
Alekh Agarwal · Alina Beygelzimer · Miroslav Dudik · John Langford · Hanna Wallach -
2018 Oral: Hierarchical Imitation and Reinforcement Learning »
Hoang Le · Nan Jiang · Alekh Agarwal · Miroslav Dudik · Yisong Yue · Hal Daumé III -
2018 Oral: A Reductions Approach to Fair Classification »
Alekh Agarwal · Alina Beygelzimer · Miroslav Dudik · John Langford · Hanna Wallach -
2018 Poster: Practical Contextual Bandits with Regression Oracles »
Dylan Foster · Alekh Agarwal · Miroslav Dudik · Haipeng Luo · Robert Schapire -
2018 Oral: Practical Contextual Bandits with Regression Oracles »
Dylan Foster · Alekh Agarwal · Miroslav Dudik · Haipeng Luo · Robert Schapire -
2018 Poster: Learning Deep ResNet Blocks Sequentially using Boosting Theory »
Furong Huang · Jordan Ash · John Langford · Robert Schapire -
2018 Oral: Learning Deep ResNet Blocks Sequentially using Boosting Theory »
Furong Huang · Jordan Ash · John Langford · Robert Schapire -
2017 : Corralling a Band of Bandit Algorithms »
Alekh Agarwal -
2017 Poster: Contextual Decision Processes with low Bellman rank are PAC-Learnable »
Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2017 Poster: Optimal and Adaptive Off-policy Evaluation in Contextual Bandits »
Yu-Xiang Wang · Alekh Agarwal · Miroslav Dudik -
2017 Talk: Contextual Decision Processes with low Bellman rank are PAC-Learnable »
Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2017 Talk: Optimal and Adaptive Off-policy Evaluation in Contextual Bandits »
Yu-Xiang Wang · Alekh Agarwal · Miroslav Dudik -
2017 Poster: Logarithmic Time One-Against-Some »
Hal Daumé · Nikos Karampatziakis · John Langford · Paul Mineiro -
2017 Poster: Active Learning for Cost-Sensitive Classification »
Akshay Krishnamurthy · Alekh Agarwal · Tzu-Kuo Huang · Hal Daumé III · John Langford -
2017 Talk: Active Learning for Cost-Sensitive Classification »
Akshay Krishnamurthy · Alekh Agarwal · Tzu-Kuo Huang · Hal Daumé III · John Langford -
2017 Talk: Logarithmic Time One-Against-Some »
Hal Daumé · Nikos Karampatziakis · John Langford · Paul Mineiro