Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Frank Hutter,Kristian Kersting,Isabel Valera Conference proceed

消极词汇

Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Ruleseries. Under the Gaussian assumption, we derive the updating in closed-form. We derive two algorithms, which differ as for the assumed independencies. We discuss their relation with the MinT reconciliation algorithm and with the Kalman filter, and we compare them experimentally.

流逝

To Ensemble or Not Ensemble: When Does End-to-End Training Fail?e find clear failure cases, where overparameterized models .. A surprising result is that the optimum can sometimes lie in between the two, neither an ensemble or an E2E system. The work also uncovers links to Dropout, and raises questions around the nature of ensemble diversity and multi-branch networks.

arbovirus

artless

Learning Gradient Boosted Multi-label Classification Rulesassification rules that is able to minimize decomposable as well as non-decomposable loss functions. Using the well-known Hamming loss and subset 0/1 loss as representatives, we analyze the abilities and limitations of our approach on synthetic data and evaluate its predictive performance on multi-label benchmarks.

initiate

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boostingclassifier based on a small ensemble of learned kernel “landmarks” better suited for the underlying application. We conduct a thorough experimental analysis to highlight the advantages of our method compared to both boosting-based and kernel-learning state-of-the-art methods.

巧思

Fairness by Explicability and Adversarial SHAP Learningess explicability constraints to classical statistical fairness metrics. We demonstrate our approaches using gradient and adaptive boosting on: a synthetic dataset, the UCI Adult (Census) dataset and a real-world credit scoring dataset. The models produced were fairer and performant.

火光在摇曳

End-to-End Learning for Prediction and Optimization with Gradient Boostingxisting gradient-based optimization through implicit differentiation to the second-order optimization for efficiently learning gradient boosting. We also conduct computational experiments to analyze how the end-to-end approaches work well and show the effectiveness of our end-to-end approach.

Munificent

Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictionsprediction which captures its uncertainty in that prediction. We theoretically analyze the convergence behaviour of our confidence estimate. Empirically, we demonstrate the effectiveness of the framework in quantifying a detector’s confidence in its predictions on a large benchmark of datasets.

Foreshadow

Climate