32 citations · 88 across the 23 of their papers we have counts for
23 papers
Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
Aristotelis Ballas, Christos Diou
Sharpness-aware and gradient-alignment methods have been shown to improve generalization, however each family of methods targets a single geometric property of the loss landscape,…
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…
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…
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…
CycleMix: Mixing Source Domains for Domain Generalization in Style-Dependent Data
Aristotelis Ballas, Christos Diou
As deep learning-based systems have become an integral part of everyday life, limitations in their generalization ability have begun to emerge. Machine learning algorithms typicall…
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…
C-XGBoost: A tree boosting model for causal effect estimation
Niki Kiriakidou, Ioannis E. Livieris, Christos Diou
Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. T…
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…
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Vasilis Gkolemis, Anargiros Tzerefos, Theodore Dalamagas +2
Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate funct…
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi +1
Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated feature…
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques
Anastasios Iliopoulos, John Violos, Christos Diou +1
Anomaly Detection in multivariate time series is a major problem in many fields. Due to their nature, anomalies sparsely occur in real data, thus making the task of anomaly detecti…
Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization
Aristotelis Ballas, Christos Diou
During the past decade, deep neural networks have led to fast-paced progress and significant achievements in computer vision problems, for both academia and industry. Yet despite t…
Towards Fair Face Verification: An In-depth Analysis of Demographic Biases
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Deep learning-based person identification and verification systems have remarkably improved in terms of accuracy in recent years; however, such systems, including widely popular cl…
Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation
Niki Kiriakidou, Christos Diou
Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes th…
CNN Feature Map Augmentation for Single-Source Domain Generalization
Aristotelis Ballas, Christos Diou
In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce mo…
CNNs with Multi-Level Attention for Domain Generalization
Aristotelis Ballas, Christos Diou
In the past decade, deep convolutional neural networks have achieved significant success in image classification and ranking and have therefore found numerous applications in multi…
FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Bias in computer vision systems can perpetuate or even amplify discrimination against certain populations. Considering that bias is often introduced by biased visual datasets, many…
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Aristotelis Ballas, Christos Diou
Despite their immense success in numerous fields, machine and deep learning systems have not yet been able to firmly establish themselves in mission-critical applications in health…
Partially Oblivious Neural Network Inference
Panagiotis Rizomiliotis, Christos Diou, Aikaterini Triakosia +2
Oblivious inference is the task of outsourcing a ML model, like neural-networks, without disclosing critical and sensitive information, like the model's parameters. One of the most…
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations
Vasilis Gkolemis, Theodore Dalamagas, Christos Diou
Accumulated Local Effect (ALE) is a method for accurately estimating feature effects, overcoming fundamental failure modes of previously-existed methods, such as Partial Dependence…
An evaluation framework for comparing causal inference models
Niki Kiriakidou, Christos Diou
Estimation of causal effects is the core objective of many scientific disciplines. However, it remains a challenging task, especially when the effects are estimated from observatio…
A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks
Aristotelis Ballas, Christos Diou
Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases. As of late, they have also established themselves in t…
Multi-layer Representation Learning for Robust OOD Image Classification
Aristotelis Ballas, Christos Diou
Convolutional Neural Networks have become the norm in image classification. Nevertheless, their difficulty to maintain high accuracy across datasets has become apparent in the past…