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ORCIDSofia S. Villar
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2020 – today
- 2024
[c6]Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov, Rachel Kornfield, Jonah Meyerhoff, Ananya Bhattacharjee, Chris J. Karr, Theresa Nguyen, David C. Mohr, Anna N. Rafferty, Sofia S. Villar, Nina Deliu
, Joseph Jay Williams:
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health. AAAI 2024: 22906-22912- 2023
- [i8]James K. He, Sofia S. Villar, Lida Mavrogonatou:
Computing the Performance of A New Adaptive Sampling Algorithm Based on The Gittins Index in Experiments with Exponential Rewards. CoRR abs/2301.01107 (2023) - [i7]Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov, Rachel Kornfield, Jonah Meyerhoff, Ananya Bhattacharjee, Chris J. Karr, Theresa Nguyen, David C. Mohr, Anna N. Rafferty, Sofia S. Villar, Nina Deliu, Joseph Jay Williams:
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health. CoRR abs/2310.18326 (2023) - 2022
- [j2]Lida Mavrogonatou, Yuxuan Sun, David S. Robertson, Sofia S. Villar:
A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity. Comput. Stat. Data Anal. 176: 107559 (2022) - [c5]Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus:
Multi-disciplinary fairness considerations in machine learning for clinical trials. FAccT 2022: 906-924 - [i6]Xijin Chen, Kim May Lee, Sofia S. Villar, David S. Robertson:
Some performance considerations when using multi-armed bandit algorithms in the presence of missing data. CoRR abs/2205.03820 (2022) - [i5]Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus:
Multi-disciplinary fairness considerations in machine learning for clinical trials. CoRR abs/2205.08875 (2022) - 2021
- [i4]Joseph Jay Williams, Jacob Nogas, Nina Deliu, Hammad Shaikh, Sofia S. Villar, Audrey Durand, Anna N. Rafferty:
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments. CoRR abs/2103.12198 (2021) - [i3]Nina Deliu, Joseph Jay Williams, Sofia S. Villar:
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling. CoRR abs/2111.00137 (2021) - [i2]Jacob Nogas, Tong Li, Fernando J. Yanez, Arghavan Modiri, Nina Deliu, Ben Prystawski, Sofia S. Villar, Anna N. Rafferty, Joseph Jay Williams:
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization. CoRR abs/2112.08507 (2021) - 2020
- [c4]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. ICML 2020: 8730-8740 - [i1]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. CoRR abs/2006.05026 (2020)
2010 – 2019
- 2017
- [j1]S. Faye Williamson, Peter Jacko, Sofia S. Villar, Thomas Jaki:
A Bayesian adaptive design for clinical trials in rare diseases. Comput. Stat. Data Anal. 113: 136-153 (2017) - 2012
- [c3]Peter Jacko, Sofia S. Villar:
Opportunistic schedulers for optimal scheduling of flows in wireless systems with ARQ feedback. ITC 2012: 1-8 - 2011
- [c2]José Niño-Mora, Sofia S. Villar:
Sensor scheduling for hunting elusive hiding targets via whittle's restless bandit index policy. NetGCoop 2011: 1-8
2000 – 2009
- 2009
- [c1]José Niño-Mora, Sofia S. Villar:
Multitarget tracking via restless bandit marginal productivity indices and Kalman filter in discrete time. CDC 2009: 2905-2910
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