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stat.ML2025
Simulating Biases for Interpretable Fairness in Offline and Online Classifiers
Ricardo Inácio, Zafeiris Kokkinogenis, Vitor Cerqueira +1
Predictive models often reinforce biases which were originally embedded in their training data, through skewed decisions. In such cases, mitigation methods are critical to ensure t…
stat.ML2024
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
Vitor Cerqueira, Luis Roque, Carlos Soares
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising per…
stat.ML2024
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
José Leites, Vitor Cerqueira, Carlos Soares
Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training ac…