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stat.ML2020
Limits of Transfer Learning
Jake Williams, Abel Tadesse, Tyler Sam +2
Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer lea…
stat.ML2020
Decomposable Probability-of-Success Metrics in Algorithmic Search
Tyler Sam, Jake Williams, Abel Tadesse +2
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric p…
stat.ML2019
The Bias-Expressivity Trade-off
Julius Lauw, Dominique Macias, Akshay Trikha +2
Learning algorithms need bias to generalize and perform better than random guessing. We examine the flexibility (expressivity) of biased algorithms. An expressive algorithm can ada…