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Found 5 papers, 1 papers with code
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms.
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
In the fully adversarial case our bounds gracefully deteriorate to match the minimax regret.
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
case, our bounds match the rates one would expect from results in stochastic acceleration, and in the fully adversarial case they gracefully deteriorate to match the minimax regret.
Robust Online Convex Optimization in the Presence of Outliers
If the outliers are chosen adversarially, we show that a simple filtering strategy on extreme gradients incurs O(k) additive overhead compared to the usual regret bounds, and that this is unimprovable, which means that k needs to be sublinear in the number of rounds.
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era.
