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Conventional supervised learning algorithms require training data that includes 'optimal' labels. Unfortunately, such optimal labels may be difficult to annotate or even define for many constructive ML tasks. For example, what is the optimal layout of a personalized newspaper for a particular user on a given day? While the optimal layout may be unattainable as training data, it may be easy to infer the quality of a particular layout that was presented to the user (e.g., from behavioral signals). This means that we may easily get bandit feedback for learning, but not full-information feedback. In fact, such bandit-style log data is one of the most ubiquitous forms of data available, as it can be recorded from a variety of systems (e.g., search engines, recommender systems, ad placement) at little cost.
Author Information
Thorsten Joachims (Cornell)
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2021 Poster: Fairness in Ranking under Uncertainty »
Ashudeep Singh · David Kempe · Thorsten Joachims -
2020 Poster: MOReL: Model-Based Offline Reinforcement Learning »
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2019 : Opening Remarks »
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2019 Workshop: Machine Learning with Guarantees »
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2019 Workshop: “Do the right thing”: machine learning and causal inference for improved decision making »
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2019 : Thorsten Joachim: Fair Ranking with Biased Data »
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2019 Poster: Policy Learning for Fairness in Ranking »
Ashudeep Singh · Thorsten Joachims -
2017 : Equality of Opportunity in Rankings »
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2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
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2016 : Panel Discussion »
Gisbert Schneider · Ross E Goodwin · Simon Colton · Russ Salakhutdinov · Thorsten Joachims · Florian Pinel -
2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
Ricardo Silva · John Shawe-Taylor · Adith Swaminathan · Thorsten Joachims -
2015 Poster: The Self-Normalized Estimator for Counterfactual Learning »
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2015 Spotlight: The Self-Normalized Estimator for Counterfactual Learning »
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2013 Poster: Learning Trajectory Preferences for Manipulators via Iterative Improvement »
Ashesh Jain · Brian Wojcik · Thorsten Joachims · Ashutosh Saxena -
2011 Poster: Semantic Labeling of 3D Point Clouds for Indoor Scenes »
Hema Koppula · Abhishek Anand · Thorsten Joachims · Ashutosh Saxena -
2007 Workshop: Machine Learning for Web Search »
Denny Zhou · Olivier Chapelle · Thorsten Joachims · Thomas Hofmann