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Found 20 papers, 8 papers with code
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications.
Time-dependent Probabilistic Generative Models for Disease Progression
We propose a Markovian generative model of treatments developed to (i) model the irregular time intervals between medical events; (ii) classify treatments into subtypes based on the patient sequence of medical events and the time intervals between them; and (iii) segment treatments into subsequences of disease progression patterns.
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity.
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting.
A Survey on Preserving Fairness Guarantees in Changing Environments
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair.
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification.
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations.
Analysis of Dominant Classes in Universal Adversarial Perturbations
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion.
A revisited branch-and-cut algorithm for large-scale orienteering problems
We propose a revisited version of the branch-and-cut algorithm for the orienteering problem which includes new contributions in the separation algorithms of inequalities stemming from the cycle problem, in the separation loop, in the variables pricing, and in the calculation of the lower and upper bounds of the problem.
Optimization and Control Data Structures and Algorithms
On Solving Cycle Problems with Branch-and-Cut: Extending Shrinking and Exact Subcycle Elimination Separation Algorithms
Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination separation problems.
Data Structures and Algorithms Combinatorics 05C38, 90C10, 90C57
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible yet malicious perturbations to natural inputs.
A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series.
Evolving Gaussian Process kernels from elementary mathematical expressions
Gaussian Process is a state-of-the-art technique for regression and classification that heavily relies on a kernel function.
Sentiment analysis with genetically evolved Gaussian kernels
Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks
Numerical Weather Prediction (NWP) models represent sub-grid processes using parameterizations, which are often complex and a major source of uncertainty in weather forecasting.
Atmospheric and Oceanic Physics 86-08 I.4.9; I.6.6; J.2
Merge Non-Dominated Sorting Algorithm for Many-Objective Optimization
Many Pareto-based multi-objective evolutionary algorithms require to rank the solutions of the population in each iteration according to the dominance principle, what can become a costly operation particularly in the case of dealing with many-objective optimization problems.
A review on distance based time series classification
The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach.
An efficient K -means clustering algorithm for massive data
The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields.
Towards Competitive Classifiers for Unbalanced Classification Problems: A Study on the Performance Scores
We conclude that using unweighted H\"older means with exponent $p \leq 1$ to average the recalls of all the classes produces adequate scores which are capable of determining whether a classifier is competitive.
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
NM-landscapes have been recently introduced as a class of tunable rugged models.
