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09/16/2023
GPT as a Baseline for Recommendation Explanation Texts
In this work, we establish a baseline potential for how modern model-gen...
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09/04/2023
Fair Ranking under Disparate Uncertainty
Ranking is a ubiquitous method for focusing the attention of human evalu...
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07/10/2023
Ranking with Long-Term Constraints
The feedback that users provide through their choices (e.g., clicks, pur...
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06/30/2023
Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters
University admission at many highly selective institutions uses a holist...
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05/14/2023
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
We study off-policy evaluation (OPE) of contextual bandit policies for l...
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02/07/2023
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions
A growing number of college applications has presented an annual challen...
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10/15/2022
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
A critical need for industrial recommender systems is the ability to eva...
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08/01/2022
Boosted Off-Policy Learning
We investigate boosted ensemble models for off-policy learning from logg...
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06/15/2022
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
Rankings have become the primary interface in two-sided online markets. ...
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05/30/2022
Fairness in the First Stage of Two-Stage Recommender Systems
Many large-scale recommender systems consist of two stages, where the fi...
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04/22/2022
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
Conventional methods for query autocompletion aim to predict which compl...
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02/13/2022
Off-Policy Evaluation for Large Action Spaces via Embeddings
Off-policy evaluation (OPE) in contextual bandits has seen rapid adoptio...
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02/03/2022
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits
Methods for offline A/B testing and counterfactual learning are seeing r...
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02/02/2022
Improving Screening Processes via Calibrated Subset Selection
Many selection processes such as finding patients qualifying for a medic...
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07/14/2021
Fairness in Ranking under Uncertainty
Fairness has emerged as an important consideration in algorithmic decisi...
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06/03/2021
Optimizing Rankings for Recommendation in Matching Markets
Based on the success of recommender systems in e-commerce, there is grow...
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03/03/2021
Fairness of Exposure in Stochastic Bandits
Contextual bandit algorithms have become widely used for recommendation ...
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10/04/2020
Fairness and Diversity for Rankings in Two-Sided Markets
Ranking items by their probability of relevance has long been the goal o...
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08/11/2020
Content-based Music Similarity with Triplet Networks
We explore the feasibility of using triplet neural networks to embed son...
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06/16/2020
Off-policy Bandits with Deficient Support
Learning effective contextual-bandit policies from past actions of a dep...
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05/29/2020
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms m...
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05/12/2020
MOReL : Model-Based Offline Reinforcement Learning
In offline reinforcement learning (RL), the goal is to learn a successfu...
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11/19/2019
Fair Learning-to-Rank from Implicit Feedback
Addressing unfairness in rankings has become an increasingly important p...
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02/11/2019
Policy Learning for Fairness in Ranking
Conventional Learning-to-Rank (LTR) methods optimize the utility of the ...
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12/12/2018
Estimating Position Bias without Intrusive Interventions
Presentation bias is one of the key challenges when learning from implic...
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11/06/2018
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
The ability to perform offline A/B-testing and off-policy learning using...
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11/05/2018
Intervention Harvesting for Context-Dependent Examination-Bias Estimation
Accurate estimates of examination bias are crucial for unbiased learning...
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06/09/2018
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank
Presentation bias is one of the key challenges when learning from implic...
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04/30/2018
Counterfactual Learning-to-Rank for Additive Metrics and Deep Models
Implicit feedback (e.g., clicks, dwell times) is an attractive source of...
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02/21/2018
Improving Recommender Systems Beyond the Algorithm
Recommender systems rely heavily on the predictive accuracy of the learn...
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02/20/2018
Fairness of Exposure in Rankings
Rankings are ubiquitous in the online world today. As we have transition...
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12/01/2016
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
The ability to perform effective off-policy learning would revolutionize...
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02/23/2016
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
In the study of human learning, there is broad evidence that our ability...
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02/23/2016
Latent Skill Embedding for Personalized Lesson Sequence Recommendation
Students in online courses generate large amounts of data that can be us...
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02/17/2016
Recommendations as Treatments: Debiasing Learning and Evaluation
Most data for evaluating and training recommender systems is subject to ...
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01/05/2016
Learning Preferences for Manipulation Tasks from Online Coactive Feedback
We consider the problem of learning preferences over trajectories for mo...
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02/09/2015
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
We develop a learning principle and an efficient algorithm for batch lea...
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09/28/2013
Structured learning of sum-of-submodular higher order energy functions
Submodular functions can be exactly minimized in polynomial time, and th...
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06/26/2013
Learning Trajectory Preferences for Manipulators via Iterative Improvement
We consider the problem of learning good trajectories for manipulation t...
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07/04/2012
Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation
Tackling the problem of ordinal preference revelation and reasoning, we ...
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05/18/2012
Online Structured Prediction via Coactive Learning
We propose Coactive Learning as a model of interaction between a learnin...
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11/22/2011
Contextually Guided Semantic Labeling and Search for 3D Point Clouds
RGB-D cameras, which give an RGB image to- gether with depths, are becom...
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11/03/2011
Online Learning with Preference Feedback
We propose a new online learning model for learning with preference feed...
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10/10/2011