Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks
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   Chapter 2 Factorized Structured Regression for Large-Scale Varying Coefficient Models
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   Chapter 3 Ordinal Quantification Through Regularization
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   Chapter 4 Random Similarity Forests
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   Chapter 5 Spectral Ranking with Covariates
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   Chapter 6 Truly Unordered Probabilistic Rule Sets for Multi-class Classification
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   Chapter 7 From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm
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   Chapter 8 Sparse Horseshoe Estimation via Expectation-Maximisation
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    Chapter 9 Structure-Preserving Gaussian Process Dynamics
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    Chapter 10 Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation
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    Chapter 11 Optimization of Annealed Importance Sampling Hyperparameters
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    Chapter 12 Bayesian Nonparametrics for Sparse Dynamic Networks
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    Chapter 13 A Pre-screening Approach for Faster Bayesian Network Structure Learning
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    Chapter 14 On Projectivity in Markov Logic Networks
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    Chapter 15 A Non-parametric Bayesian Approach for Uplift Discretization and Feature Selection
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    Chapter 16 Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages
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    Chapter 17 Learning Optimal Transport Between Two Empirical Distributions with Normalizing Flows
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    Chapter 18 Feature-Robust Optimal Transport for High-Dimensional Data
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    Chapter 19 Penalized FTRL with Time-Varying Constraints
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    Chapter 20 Rethinking Exponential Averaging of the Fisher
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    Chapter 21 Mixed Integer Linear Programming for Optimizing a Hopfield Network
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    Chapter 22 Learning to Control Local Search for Combinatorial Optimization
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    Chapter 23 Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-Based Policy Learning
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    Chapter 24 Learning Optimal Decision Trees Under Memory Constraints
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    Chapter 25 SaDe: Learning Models that Provably Satisfy Domain Constraints
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    Chapter 26 On the Generalization of Neural Combinatorial Optimization Heuristics
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    Chapter 27 Time Constrained DL8.5 Using Limited Discrepancy Search
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    Chapter 28 Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search
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    Chapter 29 FASE : A Fast, Accurate and Seamless Emulator for Custom Numerical Formats
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    Chapter 30 GNNSampler: Bridging the Gap Between Sampling Algorithms of GNN and Hardware
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    Chapter 31 Training Parameterized Quantum Circuits with Triplet Loss
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    Chapter 32 Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests
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    Chapter 33 CGPM: Poverty Mapping Framework Based on Multi-Modal Geographic Knowledge Integration and Macroscopic Social Network Mining
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    Chapter 34 Bayesian Multi-head Convolutional Neural Networks with Bahdanau Attention for Forecasting Daily Precipitation in Climate Change Monitoring
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    Chapter 35 Cubism : Co-balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer
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    Chapter 36 Go Green: A Decision-Tree Framework to Select Optimal Box-Sizes for Product Shipments
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    Chapter 37 An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning