From the 1 of 9 linked papers with an AI index.
9 papers
Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
Aristotelis Ballas, Christos Diou
The paper shows that both loss‑landscape flatness and gradient alignment are essential for multi‑distribution learning and introduces SAGE, a method that jointly optimizes these pr…
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Vasilis Gkolemis, Christos Diou, Michael U. Gutmann
Bayesian parameter inference for complex stochastic simulators is challenging due to intractable likelihood functions. Existing simulation-based inference methods often require lar…
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos +5
Generalized additive models (GAMs) offer interpretability through independent univariate feature effects but underfit when interactions are present in data. GAMs add selected p…
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partia…
Gradient-Guided Annealing for Domain Generalization
Aristotelis Ballas, Christos Diou
Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even stat…
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning
Aristotelis Ballas, Vasileios Papapanagiotou, Christos Diou
Despite recent advancements in deep learning, its application in real-world medical settings, such as phonocardiogram (PCG) classification, remains limited. A significant barrier i…