3 citations · 6 across the 11 of their papers we have counts for
11 papers
Online Resource Allocation via Static Bundle Pricing
Dimitris Fotakis, Charalampos Platanos, Thanos Tolias
Online Resource Allocation addresses the problem of efficiently allocating limited resources to buyers with incomplete knowledge of future requests. In our setting, buyers arrive s…
Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
In this paper, we study the factors that contribute to the effect of oversmoothing in deep Graph Neural Networks (GNNs). Specifically, our analysis is based on a new metric (Mean A…
A Query-Driven Approach to Space-Efficient Range Searching
Dimitris Fotakis, Andreas Kalavas, Ioannis Psarros
We initiate a study of a query-driven approach to designing partition trees for range-searching problems. Our model assumes that a data structure is to be built for an unknown quer…
Polynomial Time Learning-Augmented Algorithms for NP-hard Permutation Problems
Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis +2
We consider a learning-augmented framework for NP-hard permutation problems. The algorithm has access to predictions telling, given a pair of elements, whether is before…
A Competitive Posted-Price Mechanism for Online Budget-Feasible Auctions
Andreas Charalampopoulos, Dimitris Fotakis, Panagiotis Patsilinakos +1
We consider online procurement auctions, where the agents arrive sequentially, in random order, and have private costs for their services. The buyer aims to maximize a monotone sub…
Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
In this work, we generalize the ideas of Kaiming initialization to Graph Neural Networks (GNNs) and propose a new scheme (G-Init) that reduces oversmoothing, leading to very good r…
Partially Trained Graph Convolutional Networks Resist Oversmoothing
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
In this work we investigate an observation made by Kipf \& Welling, who suggested that untrained GCNs can generate meaningful node embeddings. In particular, we investigate the eff…
On the Distortion of Committee Election with 1-Euclidean Preferences and Few Distance Queries
Dimitris Fotakis, Laurent Gourvès, Panagiotis Patsilinakos
We consider committee election of (out of ) candidates, where the voters and the candidates are associated with locations on the real line. Each voter's card…
Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks
Agapi Rissaki, Orestis Pavlou, Dimitris Fotakis +2
The mid-infrared spectra of galaxies are rich in features such as the Polycyclic Aromatic Hydrocarbon (PAH) and silicate dust features which give valuable information about the phy…
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas +9
The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods…
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek +1
There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recomm…