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stat.ML2025
Beyond Real Data: Synthetic Data through the Lens of Regularization
Amitis Shidani, Tyler Farghly, Yang Sun +2
Synthetic data can improve generalization when real data is scarce, but excessive reliance may introduce distributional mismatches that degrade performance. In this paper, we prese…
stat.ML2022
Ranking In Generalized Linear Bandits
Amitis Shidani, George Deligiannidis, Arnaud Doucet
We study the ranking problem in generalized linear bandits. At each time, the learning agent selects an ordered list of items and observes stochastic outcomes. In recommendation sy…
stat.ML2022
Chained Generalisation Bounds
Eugenio Clerico, Amitis Shidani, George Deligiannidis +1
This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique. By developing a general t…