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Found 7 papers, 4 papers with code
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks
This paper formalize an existing problem in NLP research: benchmarking when some systems scores are missing on the task, and proposes a novel approach to address it.
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
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.
What are the best systems? New perspectives on NLP Benchmarking
In Machine Learning, a benchmark refers to an ensemble of datasets associated with one or multiple metrics together with a way to aggregate different systems performances.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
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 $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$ say.
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments.
Rank aggregation for non-stationary data streams
Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more \textit{reliable} than others and show that this generalization can solve the problem of rank aggregation over non-stationary data streams.
