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Dimitris Fotakis

18 accepted papers

2026

GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas, Dimitrios Rontogiannis, Nikolaos Theologitis, Dimitris Sacharidis +6
AAAI 2026technical

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide suc

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2025

Improved Bounds for Online Facility Location with Predictions

Dimitris Fotakis, Evangelia Gergatsouli, Themistoklis Gouleakis, Nikolas Patris, Thanos Tolias
AAAI 2025technical

We consider the Online Facility Location (OFL) problem in the framework of learning-augmented online algorithms. In Online Facility Location (OFL), demands arrive one-by-one in a metric space and must be (irrevocably) assigned to an open facility upon arrival, without any knowledge about future dema…

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2025

On the Distortion of Committee Election with 1-Euclidean Preferences and Few Distance Queries

Dimitris Fotakis, Laurent Gourvès, Panagiotis Patsilinakos
AAAI 2025technical

We consider committee election of k >= 3 (out of m >= k + 1) candidates, where the voters and the candidates are associated with locations on the real line. Each voter’s cardinal preferences over candidates correspond to her distance to the candidate locations, and each voter’s cardinal preferences…

Cited by 3Source ↗PDF ↗Scholar ↗
2025

Polynomial Time Learning Augmented Algorithms for NP-hard Permutation Problems

Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis, Panagiotis Patsilinakos, Michalis Xefteris
ICML 2025poster

We consider a learning augmented framework for NP-hard permutation problems. The algorithm has access to predictions telling, given a pair $u,v$ of elements, whether $u$ is before $v$ or not in an optimal solution. Building on the work of Braverman and Mossel (SODA 2008), we show that for a class of…

Cited by 2Source ↗PDF ↗Scholar ↗
2023

Fairness Aware Counterfactuals for Subgroups

Loukas Kavouras, Konstantinos Tsopelas, Giorgos Giannopoulos, Dimitris Sacharidis, Eleni Psaroudaki, Nikolaos Theologitis +3
NeurIPS 2023poster

In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisiting (and generalizing) existing notions and introducing new, more refined notions of subgroup fairness. We aim to (a) fo…

Cited by 18Source ↗PDF ↗Scholar ↗
2023

Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method

Constantine Caramanis, Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos
NeurIPS 2023oral

Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems. In those methods a deep neural network is used as a solution generator which is then trained by gradient-based methods (e.g., policy gradient) to successively…

Cited by 8Source ↗PDF ↗Scholar ↗
2022

Differentially Private Regression with Unbounded Covariates

Jason Milionis, Alkis Kalavasis, Dimitris Fotakis, Stratis Ioannidis
AISTATS 2022poster

We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates. Prior to our work, privacy constraints in such regression settings were studied under strong a pr…

Cited by 16Source ↗PDF ↗Scholar ↗
2022

Dimensionality and Coordination in Voting: The Distortion of STV

Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
AAAI 2022technical

We study the performance of voting mechanisms from a utilitarian standpoint, under the recently introduced framework of metric-distortion, offering new insights along two main lines. First, if d represents the doubling dimension of the metric space, we show that the distortion of STV is O(d log log…

Cited by 9Source ↗PDF ↗Scholar ↗
2021

Efficient Truthful Scheduling and Resource Allocation through Monitoring

Dimitris Fotakis, Piotr Krysta, Carmine Ventre
AAAI 2021technical

We study the power and limitations of the Vickrey-Clarke-Groves mechanism with monitoring (VCGmon) for cost minimization problems with objective functions that are more general than the social cost. We identify a simple and natural sufficient condition for VCGmon to be truthful for general objective…

Cited by 2Source ↗PDF ↗Scholar ↗
2021

Estimating the Number of Induced Subgraphs from Incomplete Data and Neighborhood Queries

Dimitris Fotakis, Thanasis Pittas, Stratis Skoulakis
AAAI 2021technical

We consider a natural setting where network parameters are estimated from noisy and incomplete information about the network. More specifically, we investigate how we can efficiently estimate the number of small subgraphs (e.g., edges, triangles, etc.) based on full access to one or two noisy and in…

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2021

Identity testing for Mallows model

Robert Istvan Busa-Fekete, Dimitris Fotakis, Balazs Szorenyi, Manolis Zampetakis
NeurIPS 2021poster

In this paper, we devise identity tests for ranking data that is generated from Mallows model both in the \emph{asymptotic} and \emph{non-asymptotic} settings. First we consider the case when the central ranking is known, and devise two algorithms for testing the spread parameter of the Mallows mode…

Cited by 6Source ↗PDF ↗Scholar ↗
2021

Private and Non-private Uniformity Testing for Ranking Data

Robert Istvan Busa-Fekete, Dimitris Fotakis, Manolis Zampetakis
NeurIPS 2021poster

We study the problem of uniformity testing for statistical data that consists of rankings over $m$ items where the alternative class is restricted to Mallows models with single parameter. Testing ranking data is challenging because of the size of the large domain that is factorial in $m$, therefore…

Cited by 5Source ↗PDF ↗Scholar ↗