12 citations · 21 across the 8 of their papers we have counts for
9 papers
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks
Anas Himmi, Ekhine Irurozki, Nathan Noiry +2
The evaluation of natural language processing (NLP) systems is crucial for advancing the field, but current benchmarking approaches often assume that all systems have scores availa…
On the Fair Comparison of Optimization Algorithms in Different Machines
Etor Arza, Josu Ceberio, Ekhiñe Irurozki +1
An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the s…
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Morgane Goibert, Clément Calauzènes, Ekhine Irurozki +1
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Prefere…
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach
Etor Arza, Josu Ceberio, Ekhiñe Irurozki +1
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a…
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Laurens Bliek, Paulo da Costa, Reza Refaei Afshar +19
This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCA…
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki +1
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation of a finite se…
Concentric mixtures of Mallows models for top- rankings: sampling and identifiability
Collas Fabien, Irurozki Ekhine
In this paper, we consider mixtures of two Mallows models for top- rankings, both with the same location parameter but with different scale parameters, i.e., a mixture of concen…
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Etor Arza, Aritz Perez, Ekhine Irurozki +1
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivat…
Rank aggregation for non-stationary data streams
Ekhine Irurozki, Jesus Lobo, Aritz Perez +1
We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the…