Thesis
Data quality in causal machine learning with applications to algorithmic fairness
- Abstract:
-
The success of modern machine learning methods can be attributed to three main factors: i) The availability of increasing amounts of high quality data, ii) the sustained growth in computational resources, iii) the invention of algorithms that can reap the gains of both of these simultaneously, being specifically tailored to consume ever larger datasets on cutting-edge hardware. In this thesis, we focus on the first question of data quality in the subfield of ML that intersects with another fi...
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Authors
Contributors
+ Sejdinovic, D
- Role:
- Supervisor
+ Evans, R
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Statistics
- Role:
- Supervisor
- ORCID:
- 0000-0002-9341-1313
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- STAT2021 _ EPSRCDTP_ 1012750
- Programme:
- EPSRC DTP in Statistics
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
-
English
- Keywords:
- Subjects:
- Deposit date:
-
2025-10-22
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