| REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. | 0 | 0.34 | 2022 |
| Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support | 0 | 0.34 | 2022 |
| Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values | 0 | 0.34 | 2022 |
| Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. | 0 | 0.34 | 2021 |
| Datasheets for datasets | 8 | 0.54 | 2021 |
| Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI | 6 | 0.40 | 2020 |
| No-Regret and Incentive-Compatible Online Learning | 0 | 0.34 | 2020 |
| Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning | 2 | 0.39 | 2020 |
| Understanding the Effect of Accuracy on Trust in Machine Learning Models. | 6 | 0.42 | 2019 |
| The Disparate Effects of Strategic Classification. | 0 | 0.34 | 2018 |
| Manipulating and Measuring Model Interpretability | 10 | 0.47 | 2018 |
| The Externalities of Exploration and How Data Diversity Helps Exploitation. | 6 | 0.46 | 2018 |
| A Decomposition of Forecast Error in Prediction Markets. | 0 | 0.34 | 2017 |
| The Double Clinching Auction for Wagering. | 1 | 0.41 | 2017 |
| Oracle-Efficient Learning and Auction Design. | 0 | 0.34 | 2016 |
| Mathematical foundations for social computing. | 1 | 0.35 | 2016 |
| Mathematical foundations for social computing. | 1 | 0.35 | 2016 |
| An axiomatic characterization of wagering mechanisms. | 4 | 0.43 | 2015 |
| Incentivizing High Quality Crowdwork. | 25 | 0.87 | 2015 |
| Market Making With Decreasing Utility For Information | 1 | 0.37 | 2014 |
| Computational social science and social computing | 8 | 0.62 | 2014 |
| Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems | 8 | 0.59 | 2014 |
| An axiomatic characterization of adaptive-liquidity market makers | 5 | 0.49 | 2013 |
| Adaptive Task Assignment for Crowdsourced Classification. | 84 | 2.23 | 2013 |
| Efficient Market Making via Convex Optimization, and a Connection to Online Learning | 31 | 1.48 | 2013 |
| Online Decision Making in Crowdsourcing Markets: Theoretical Challenges (Position Paper). | 6 | 0.49 | 2013 |
| Designing Informative Securities | 3 | 0.39 | 2012 |
| Online Task Assignment in Crowdsourcing Markets. | 112 | 3.23 | 2012 |
| Towards Social Norm Design for Crowdsourcing Markets. | 0 | 0.34 | 2012 |
| An optimization-based framework for automated market-making | 27 | 1.88 | 2011 |
| A theory of learning from different domains | 426 | 13.27 | 2010 |
| Connections between markets and learning | 1 | 0.35 | 2010 |
| Regret Minimization With Concept Drift | 14 | 0.68 | 2010 |
| Evolution with Drifting Targets | 11 | 0.94 | 2010 |
| A new understanding of prediction markets via no-regret learning | 42 | 2.50 | 2010 |
| Censored exploration and the dark pool problem | 11 | 1.63 | 2009 |
| The true sample complexity of active learning | 69 | 2.93 | 2008 |
