Anna Filippova
Biographic Data
| ID | 4274591 |
|---|---|
| NAME | Anna Filippova |
| GIVEN NAMES | Anna |
| FAMILY NAME | Filippova |
| SIGNATURE | FILIPPOVA A |
| AFFILIATIONS | GitHub, San Francisco, CA 94107 |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
Measuring the predictability of life outcomes with a scientific mass collaboration
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly…
Corporate hackathons, how and why? A multiple case study of motivation, projects proposal and selection, goal setting, coordination, and outcomes
Time-bounded events such as hackathons, data dives, codefests, hack-days, sprints or edit-a-thons have increasingly gained attention from practitioners and researchers. Yet there is a paucity of research on corporate hackathons, which are nearly ubiquitous and present significant organizational, cultural, and managerial challenges. To provide a comprehensive understanding of team processes and broad array of outcomes of corporate hackathons, we c…
Humans in the Loop
Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a 'human-in-the-loop' approach in the Fragile Families Challenge. The authors use surv…
Humans in the Loop
Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a 'human-in-the-loop' approach in the Fragile Families Challenge. The authors use surv…
Humans in the Loop
Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a 'human-in-the-loop' approach in the Fragile Families Challenge. The authors use surv…
Measuring the predictability of life outcomes with a scientific mass collaboration
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly…
Corporate hackathons, how and why? A multiple case study of motivation, projects proposal and selection, goal setting, coordination, and outcomes
Time-bounded events such as hackathons, data dives, codefests, hack-days, sprints or edit-a-thons have increasingly gained attention from practitioners and researchers. Yet there is a paucity of research on corporate hackathons, which are nearly ubiquitous and present significant organizational, cultural, and managerial challenges. To provide a comprehensive understanding of team processes and broad array of outcomes of corporate hackathons, we c…
Computer Science (3 works) · Engineering (2 works) · Machine learning (2 works) · Mathematics (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Benchmark (surveying) (1 work) · Biomedical and Engineering Education (1 work) · Business (1 work)