Delayed Impact of Fair Machine Learning. - Lydia T. Liu; Sarah Dean; Esther Rolf; Max Simchowitz; Moritz Hardt (ICML, 2018)

Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. - Sarah Dean; Horia Mania; Nikolai Matni; Benjamin Recht; Stephen Tu (NeurIPS, 2018)

A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics. - Roel Dobbe; Sarah Dean; Thomas Krendl Gilbert; Nitin Kohli (CoRR, 2018)

Safely Learning to Control the Constrained Linear Quadratic Regulator. - Sarah Dean; Stephen Tu; Nikolai Matni; Benjamin Recht (ACC, 2019)

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions. - Sarah Dean; Andrew J. Taylor; Ryan K. Cosner; Benjamin Recht; Aaron D. Ames (CoRL, 2020)

Recommendations and user agency: the reachability of collaboratively-filtered information. - Sarah Dean; Sarah Rich; Benjamin Recht (FAT*, 2020)

Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. - Esther Rolf; Max Simchowitz; Sarah Dean; Lydia T. Liu; Daniel Björkegren; Moritz Hardt; Joshua Blumenstock (ICML, 2020)

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks. - McKane Andrus; Sarah Dean; Thomas Krendl Gilbert; Nathan Lambert 001; Tom Zick (ISTAS, 2020)

Robust Guarantees for Perception-Based Control. - Sarah Dean; Nikolai Matni; Benjamin Recht; Vickie Ye (L4DC, 2020)

Do Offline Metrics Predict Online Performance in Recommender Systems? - Karl Krauth; Sarah Dean; Alex Zhao; Wenshuo Guo; Mihaela Curmei; Benjamin Recht; Michael I. Jordan (CoRR, 2020)

On the Sample Complexity of the Linear Quadratic Regulator. - Sarah Dean; Horia Mania; Nikolai Matni; Benjamin Recht; Stephen Tu (Found. Comput. Math., 2020)

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty. - Andrew J. Taylor; Victor D. Dorobantu; Sarah Dean; Benjamin Recht; Yisong Yue; Aaron D. Ames (CDC, 2021)

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability. - Mihaela Curmei; Sarah Dean; Benjamin Recht (ICML, 2021)

Certainty Equivalent Perception-Based Control. - Sarah Dean; Benjamin Recht (L4DC, 2021)

Axes for Sociotechnical Inquiry in AI Research. - Sarah Dean; Thomas Krendl Gilbert; Nathan Lambert 001; Tom Zick (CoRR, 2021)

Reliable Machine Learning in Feedback Systems - Sarah Dean (2021)

Preference Dynamics Under Personalized Recommendations. - Sarah Dean; Jamie Morgenstern (EC, 2022)

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems. - Thomas Krendl Gilbert; Sarah Dean; Tom Zick; Nathan Lambert 001 (CoRR, 2022)

Reward Reports for Reinforcement Learning. - Thomas Krendl Gilbert; Sarah Dean; Nathan Lambert 001; Tom Zick; Aaron J. Snoswell (CoRR, 2022)

Multi-learner risk reduction under endogenous participation dynamics. - Sarah Dean; Mihaela Curmei; Lillian J. Ratliff; Jamie Morgenstern; Maryam Fazel (CoRR, 2022)

Perception-Based Sampled-Data Optimization of Dynamical Systems. - Liliaokeawawa Cothren; Gianluca Bianchin; Sarah Dean; Emiliano Dall'Anese (CoRR, 2022)

Cross-Dataset Propensity Estimation for Debiasing Recommender Systems. - Fengyu Li; Sarah Dean (CoRR, 2022)

Reward Reports for Reinforcement Learning. - Thomas Krendl Gilbert; Nathan Lambert 001; Sarah Dean; Tom Zick; Aaron J. Snoswell; Soham Mehta (AIES, 2023)

Modeling content creator incentives on algorithm-curated platforms. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus; Sarah Dean (ICLR, 2023)

Online Convex Optimization with Unbounded Memory. - Raunak Kumar; Sarah Dean; Robert Kleinberg (NeurIPS, 2023)

Foreword for Workshop on Decision Making for Information Retrieval and Recommender Systems. - Da Xu; Tobias Schnabel; Xiquan Cui; Sarah Dean; Aniket Deshmukh 001; Bo Yang 062; Shipeng Yu (WWW (Companion Volume), 2023)

Decision-aid or Controller? Steering Human Decision Makers with Algorithms. - Ruqing Xu; Sarah Dean (CoRR, 2023)

Emergent specialization from participation dynamics and multi-learner retraining. - Sarah Dean; Mihaela Curmei; Lillian J. Ratliff; Jamie Morgenstern; Maryam Fazel (AISTATS, 2024)

Strategic Usage in a Multi-Learner Setting. - Eliot Shekhtman; Sarah Dean (AISTATS, 2024)

Learning from Streaming Data when Users Choose. - Jinyan Su; Sarah Dean (ICML, 2024)

Harm Mitigation in Recommender Systems under User Preference Dynamics. - Jerry Chee; Shankar Kalyanaraman; Sindhu Kiranmai Ernala; Udi Weinsberg; Sarah Dean; Stratis Ioannidis (KDD, 2024)

Random features approximation for control-affine systems. - Kimia Kazemian; Yahya Sattar; Sarah Dean (L4DC, 2024)

Initializing Services in Interactive ML Systems for Diverse Users. - Avinandan Bose; Mihaela Curmei; Daniel L. Jiang; Jamie H. Morgenstern; Sarah Dean; Lillian J. Ratliff; Maryam Fazel (NeurIPS, 2024)

Ranking with Long-Term Constraints. - Kianté Brantley; Zhichong Fang; Sarah Dean; Thorsten Joachims (WSDM, 2024)

To Ask or Not To Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding. - Rohan Banerjee; Rajat Kumar Jenamani; Sidharth Vasudev; Amal Nanavati; Sarah Dean; Tapomayukh Bhattacharjee (CoRR, 2024)

Learning Linear Dynamics from Bilinear Observations. - Yahya Sattar; Yassir Jedra; Sarah Dean (ACC, 2025)

Finite Sample Identification of Partially Observed Bilinear Dynamical Systems. - Yahya Sattar; Yassir Jedra; Maryam Fazel; Sarah Dean (L4DC, 2025)

Datasets for Navigating Sensitive Topics in Recommendation Systems. - Amelia Kovacs; Jerry Chee; Sarah Dean (WWW (Companion Volume), 2025)

Accounting for AI and Users Shaping One Another: The Role of Mathematical Models. - Sarah Dean; Evan Dong; Meena Jagadeesan; Liu Leqi (Trans. Mach. Learn. Res., 2025)

Policy Design for Two-sided Platforms with Participation Dynamics. - Haruka Kiyohara; Fan Yao; Sarah Dean (CoRR, 2025)

Sub-optimality of the Separation Principle for Quadratic Control from Bilinear Observations. - Yahya Sattar; Sunmook Choi; Yassir Jedra; Maryam Fazel; Sarah Dean (CoRR, 2025)

Pre-trained Large Language Models Learn Hidden Markov Models In-context. - Yijia Dai; Zhaolin Gao; Yahya Sattar; Sarah Dean; Jennifer J. Sun (CoRR, 2025)