Machine Learning and Knowledge Discovery in Databases. Research Track

Name: Machine Learning and Knowledge Discovery in Databases. Research Track | European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part II
Brand: Springer
Price: 106.99 EUR
Availability: OnlineOnly

European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part II

Nuria Oliver Fernando Pérez-Cruz Stefan Kramer Jesse Read Jose A. Lozano(Herausgeber*in)

Springer (Verlag)

Erschienen am 9. September 2021

XXXV, 817 Seiten

E-Book

PDF mit Wasserzeichen-DRM

Systemvoraussetzungen

978-3-030-86520-7 (ISBN)

106,99 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Wasserzeichen-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Inhalt

Intro
Preface
Organization
Contents - Part II
Generative Models
Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
1 Introduction
2 Related Work
3 Background
4 Methodology
5 Experiments
6 Conclusion
References
Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection
1 Introduction
2 Related Work
2.1 Community Detection
2.2 Node Representation Learning
2.3 Joint Community Detection and Node Representation Learning
3 Methodology
3.1 Problem Formulation
3.2 Variational Model
3.3 Design Choices
3.4 Practical Aspects
3.5 Complexity
4 Experiments
4.1 Synthetic Example
4.2 Datasets
4.3 Baselines
4.4 Settings
4.5 Discussion of Results
4.6 Hyperparameter Sensitivity
4.7 Training Time
4.8 Visualization
5 Conclusion
References
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
1 Introduction
2 Related Work
3 Problem Definition
4 Proposed Algorithm
4.1 GAN Modeling
4.2 Architecture
4.3 Training Procedure
5 Datasets
6 Experiments
6.1 Baselines
6.2 Comparative Evaluation
6.3 Side-by-Side Diagnostics
7 Conclusion
References
The Bures Metric for Generative Adversarial Networks
1 Introduction
2 Method
3 Empirical Evaluation of Mode Collapse
3.1 Artificial Data
3.2 Real Images
4 High Quality Generation Using a ResNet Architecture
5 Conclusion
References
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
1 Introduction
2 Background and Related Work
2.1 Energy-Based Models
2.2 Alternatives to the Softmax Classifier
3 Methodology
3.1 Approach 1: Discriminative Training
3.2 Approach 2: Generative Training
3.3 Approach 3: Joint Training
3.4 GMMC for Inference
4 Experiments
4.1 Hybrid Modeling
4.2 Calibration
4.3 Out-Of-Distribution Detection
4.4 Robustness
4.5 Training Stability
4.6 Joint Training
5 Conclusion and Future Work
References
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty
1 Introduction
1.1 Contributions
2 Background
2.1 Variational Autoencoder
2.2 Latent Variance Estimates of NN
2.3 Mismatch Between the Prior and Approximate Posterior
3 Methodology
3.1 Gaussian Process Encoder
3.2 The Implications of a Gaussian Process Encoder
3.3 Out-of-Distribution Detection
4 Experiments
4.1 Log Likelihood
4.2 Uncertainty in the Latent Space
4.3 Benchmarking OOD Detection
4.4 OOD Polution of the Training Data
4.5 Synthesizing Variants of Input Data
4.6 Interpretable Kernels
5 Related Work
6 Conclusion
References
Variational Hyper-encoding Networks
1 Introduction
2 Variational Autoencoder (VAE)
3 Variational Hyper-encoding Networks
3.1 Hyper-auto-encoding Problem
3.2 Hyper-encoding Problem
3.3 Minimum Description Length
3.4 Compact Hyper-decoder Architecture
3.5 Applications
4 Experiments
4.1 Data Sets
4.2 Model Settings
4.3 Model Behavior
4.4 Robust Outlier Detection
4.5 Novelty Discovery
5 Related Work
6 Conclusion
References
Principled Interpolation in Normalizing Flows
1 Introduction
2 An Intuitive Solution
3 Normalizing Flows
4 Base Distributions on p-Norm Spheres
4.1 The Case p = 1
4.2 The Case p = 2
5 Experiments
5.1 Performance Metrics and Setup
5.2 Data
5.3 Architecture
5.4 Quantitative Results
5.5 Qualitative Results
6 Related Work
7 Conclusion
References
CycleGAN Through the Lens of (Dynamical) Optimal Transport
1 Introduction
2 Desiderata for UDT and Analysis of CycleGAN
2.1 What Should Be the Properties of a UDT Solution?
2.2 CycleGAN Is Biased Towards Low Energy Transformations
3 UDT as Optimal Transport
3.1 A (Dynamical) OT Model for UDT
3.2 Regularity of OT Maps
3.3 Computing the Inverse
4 A Residual Instantiation from Dynamical OT
4.1 Linking the Dynamical Formulation with CycleGAN
4.2 A Typical UDT Task
4.3 Imbalanced CelebA task
5 Related Work
6 Discussion and Conclusion
References
Decoupling Sparsity and Smoothness in Dirichlet Belief Networks
1 Introduction
2 Preliminary Knowledge
3 Sparse and Smooth Dirichlet Belief Networks
3.1 Generative Process
3.2 Necessity of Fixing biK(l)=1
4 Related Work
5 ssDirBN for Relational Modelling
5.1 Inference
5.2 Experimental Results
6 Conclusion
References
Algorithms and Learning Theory
Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound
1 Introduction
2 Majority Vote Learning
2.1 Notations and Setting
2.2 Gibbs Risk, Joint Error and C-Bound
2.3 Related Works
3 PAC-Bayesian C-Bounds
3.1 An Intuitive Bound-McAllester's View
3.2 A Tighter Bound-Seeger's View
3.3 Another Tighter Bound-Lacasse's View
4 Self-bounding Algorithms for PAC-Bayesian C-Bounds
4.1 Algorithm Based on McAllester's View
4.2 Algorithm Based on Seeger's View
4.3 Algorithm Based on Lacasse's View
5 Experimental Evaluation
5.1 Empirical Setting
5.2 Analysis of the Results
6 Conclusion and Future Work
References
Midpoint Regularization: From High Uncertainty Training Labels to Conservative Classification Decisions
1 Introduction
2 Related Work
3 Label Smoothing over Midpoint Samples
3.1 Preliminaries
3.2 Midpoint Generation
3.3 Learning Smoothing Distribution for Midpoints
3.4 Optimization
4 Experiments
4.1 Datasets, Baselines, and Settings
4.2 Predictive Accuracy
4.3 Ablation Studies
4.4 Uncertainty Label and Conservative Classification
4.5 Testing on Out-of-Distribution Data
5 Conclusion and Future Work
References
Learning Weakly Convex Sets in Metric Spaces
1 Introduction
2 Preliminaries
3 Weak Convexity in Metric Spaces
3.1 Some Basic Properties of Weakly Convex Sets
4 Learning in the Extensional Problem Setting
4.1 Application Scenario: Vertex Classification
5 The Intensional Problem Setting
5.1 Learning Weakly Convex Boolean Functions
5.2 Learning Weakly Convex Axis-Aligned Hyperrectangles
6 Concluding Remarks
References
Disparity Between Batches as a Signal for Early Stopping
1 Introduction
2 Related Work
3 Generalization Penalty
4 Gradient Disparity
5 Early Stopping Criterion
6 Discussion and Final Remarks
References
Learning from Noisy Similar and Dissimilar Data
1 Introduction
2 Problem Setup
3 Loss Correction Approach
4 Weighted Classification Approach
5 Experiments
6 Conclusion and Future Work
References
Knowledge Distillation with Distribution Mismatch
1 Introduction
2 Related Works
3 Framework
3.1 Problem Definition
3.2 Proposed Method KDDM
4 Experiments and Discussions
4.1 Datasets
4.2 Baselines
4.3 Results on MNIST
4.4 Results on CIFAR-10
4.5 Results on CIFAR-100
4.6 Distillation When Teacher-Data and Student-Data Are Identical
5 Conclusion
References
Certification of Model Robustness in Active Class Selection
1 Introduction
1.1 Active Class Selection Constitutes a Domain Gap
1.2 A Qualitative Intuition from Information Theory
2 A Quantitative Perspective from Learning Theory
2.1 Quantification of the Domain Gap
2.2 Certification of Domain Robustness for Binary Predictors
3 Experiments
3.1 Binary (, ) Certificates Are Tight
3.2 Binary (, ) Certificates in Astro-Particle Physics
4 Related Work
5 Conclusion
References
Graphs and Networks
Inter-domain Multi-relational Link Prediction
1 Introduction
2 Preliminary
2.1 RESCAL
2.2 Optimal Transport
2.3 Maximum Mean Discrepancy
3 Problem Setting and Proposed Method
3.1 Problem Setting
3.2 Proposed Objective Function
4 Experiments
4.1 Datasets
4.2 Evaluation Methods and Baselines
4.3 Implementation Details
4.4 Experimental Results
5 Related Work
6 Conclusion and Future Work
References
GraphSVX: Shapley Value Explanations for Graph Neural Networks
1 Introduction
2 Related Work
3 Preliminary Concepts and Background
3.1 Graph Neural Networks
3.2 The Shapley Value
4 A Unified Framework for GNN Explainers
5 Proposed Method
5.1 Mask and Graph Generators
5.2 Explanation Generator
5.3 Decomposition Model
5.4 Efficient Approximation Specific to GNNs
5.5 Desirable Properties of Explanations
6 Experimental Evaluation
6.1 Synthetic and Real Datasets with Ground Truth
6.2 Real-World Datasets Without Ground Truth
7 Conclusion
References
Multi-view Self-supervised Heterogeneous Graph Embedding
1 Introduction
2 Related Work
2.1 Self-supervised Learning on Graphs
2.2 Heterogeneous Graph Embedding
3 The Proposed Model
3.1 Model Framework
3.2 Heterogeneous Context Encoding
3.3 Multi-view Contrastive Learning
4 Experiment
4.1 Experimental Setup
4.2 Node Classification
4.3 Link Prediction
4.4 Ablation Study
4.5 Visualization
5 Conclusion
References
Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation
1 Introduction
2 Related Work
2.1 Heterogeneous Graph Neural Network
2.2 Graph Domain Adaptation
3 Preliminaries
4 The Proposed Model
4.1 Semantic-Specific GNN for DA
4.2 Hierarchical Domain Alignment
4.3 Optimization Objective
4.4 Discussion of the Proposed Model
5 Experiments
5.1 Datasets
5.2 Baselines and Implementation Details
5.3 Results
5.4 Analysis
6 Conclusion
References
The KL-Divergence Between a Graph Model and its Fair I-Projection as a Fairness Regularizer
1 Introduction
2 Related Work
3 Fair Information Projection
3.1 Notation
3.2 Fairness Constraints
3.3 Information Projection
4 The KL-Divergence to the I-projection as a Fairness Regularizer
4.1 I-Projection Regularization
4.2 Practical Considerations
5 Experiments
5.1 Datasets
5.2 Algorithms
5.3 Fair Graph Embedding Baselines
5.4 Evaluation
5.5 Results
6 Conclusion
References
On Generalization of Graph Autoencoders with Adversarial Training
1 Introduction
2 Related Work
3 Preliminaries
3.1 Graph Autoencoders
3.2 Graph Variational Autoencoders
3.3 Adversarial Training
4 Graph Adversarial Training
4.1 Adversarial Training in Graph Autoencoder
4.2 Adversarial Training in Variational Graph Autoencoder
5 Experiments
5.1 Link Prediction
5.2 Node Clustering
5.3 Graph Anomaly Detection
6 Understanding Adversarial Training
6.1 The Impact of
6.2 The Impact of T
6.3 The Impact of
6.4 Performance w.r.t. Degree
7 Conclusion
References
Inductive Link Prediction with Interactive Structure Learning on Attributed Graph
1 Introduction
2 Model Formulation
2.1 Notations and Definitions
2.2 Overview of PaGNN
2.3 Broadcasting Operation
2.4 Aggregation Operation
2.5 Edge Representation Learning
2.6 Cache Strategy in Inference
2.7 Summary
3 Experiments
3.1 Experiment Setup
3.2 Performance Comparison
3.3 Efficiency Analysis
4 Related Work
5 Conclusion
References
Representation Learning on Multi-layered Heterogeneous Network
1 Introduction
2 Related Work
3 Definitions and Problem Formulation
4 Model Architecture
4.1 Intra-layer Proximity Modeling
4.2 Cross-Layer Proximity Modeling
4.3 Learning Strategy
5 Experiments
5.1 Setup
5.2 Node Classification
5.3 Node Clustering
5.4 Link Prediction
5.5 Network Visualization
5.6 Model Analysis
5.7 Case Study
6 Conclusion
References
Adaptive Node Embedding Propagation for Semi-supervised Classification
1 Introduction
2 Preliminaries
2.1 Problem Definition
2.2 Graph Convolutional Networks
3 Our Approach
3.1 Embedding Propagation Loss
3.2 Anti-smoothness Loss
3.3 Adaptive Propagation Control
3.4 Architecture of ANEPN
4 Experiments
4.1 Settings
4.2 Results
5 Related Work
5.1 Graph-Based Semi-supervised Learning
5.2 Graph Convolutional Networks
6 Conclusion
References
Probing Negative Sampling for Contrastive Learning to Learn Graph Representations
1 Introduction
2 Related Work
2.1 Graph Representation Learning
2.2 Contrastive Learning and Negative Sampling
3 Preliminaries and Problem Formulation
3.1 Determinantal Point Process
3.2 Notations and Definitions
3.3 Problem Setup
4 The Proposed Approach
4.1 Graph Embeddings
4.2 Resolving Class Collision
4.3 Sampling Diverse Negative Examples
4.4 Node-Wise Contrastive Learning Loss
5 Experimental Results
5.1 Experimental Setup
5.2 RQ1: Performance Comparison
5.3 RQ2: Ablation Study
5.4 RQ3: Parameter Analysis
5.5 RQ4: Visualization Results
6 Conclusion
References
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs
1 Introduction
2 Preliminaries
3 Proposed Approach
4 Experiments
4.1 Experimental Setup
4.2 Results and Discussion
5 Related Work
6 Conclusion
References
Zero-Shot Scene Graph Relation Prediction Through Commonsense Knowledge Integration
1 Introduction
2 Motivating Analysis
2.1 Ignorance yet Importance of Zero-Shot Triplets
2.2 Commonsense Knowledge from ConceptNet Neighbors
2.3 Commonsense Knowledge from ConceptNet Paths
3 COACHER
3.1 Backbone Scene Graph Generation Pipeline
3.2 Commonsense Integrator
4 Experiments
4.1 Experimental Settings
4.2 Hyper-parameter Setting
4.3 Performance Evaluations
4.4 Case Studies
5 Related Work
5.1 Scene Graph Generation
5.2 External Knowledge Enhanced Deep Neural Networks
6 Conclusion
References
Graph Fraud Detection Based on Accessibility Score Distributions
1 Introduction
2 Related Work
3 Preliminaries
3.1 Random Walk with Restart
3.2 RWR for Bipartite Graphs
4 Proposed Method
4.1 Accessibility
4.2 Skewness in Accessibility Score Distributions
4.3 Theoretical Analysis
4.4 SkewA
5 Experiments
5.1 Setup
5.2 Robustness to Sparse Frauds
5.3 Camouflage-Resistance
5.4 Effects of Camouflage Ratio
5.5 Effectiveness of Theoretical Analysis
6 Conclusion
References
Correlation Clustering with Global Weight Bounds
1 Introduction
2 Related Work
3 Problem Definition
4 Theoretical Results and Algorithms
4.1 PC-Reduction
4.2 Preserving the Approximation Factor Across PC-Reduction
4.3 Ultimate Global Weight Bounds
4.4 Algorithms
5 Experiments
5.1 Analysis of the Global-Weight-Bounds Condition
5.2 Application to Fair Clustering
6 Conclusions
References
Modeling Multi-factor and Multi-faceted Preferences over Sequential Networks for Next Item Recommendation
1 Introduction
2 Related Work
3 Problem Formulation
4 The Proposed Method
4.1 Construction and Embedding of Sequential Networks
4.2 Multi-factor Preference Modeling
4.3 Multi-faceted Preference Modeling
4.4 Recommendation Model
5 Experiment
5.1 Experimental Setup
5.2 Model Comparison
5.3 Ablation Studies
5.4 Hyper-Parameter Study
6 Conclusion
References
PATHATTACK: Attacking Shortest Paths in Complex Networks
1 Introduction
2 Problem Statement
3 Proposed Method: PATHATTACK
3.1 Path Cutting as Set Cover
3.2 Linear Programming Formulation
3.3 Constraint Generation
3.4 PATHATTACK
4 Experiments
4.1 Baseline Methods
4.2 Synthetic and Real Networks
4.3 Experimental Setup
4.4 Results
5 Related Work
6 Conclusions
References
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
1 Introduction
2 Related Work
3 Background and Notation
4 Method
4.1 Model Formulation
4.2 Centrality-Attentive Embedding
4.3 Position-Attentive Embedding:
5 Theoretical Analysis
5.1 Connection to Preceding KGE Methods
5.2 Expressive Power
5.3 Complexity Analysis
6 Experiments
6.1 Transductive Link Prediction Experiment
6.2 Inductive Link Prediction Experiment
7 Conclusion
References
Interpretation, Explainability, Transparency, Safety
Reconnaissance for Reinforcement Learning with Safety Constraints
1 Introduction
2 Background
3 Theory
4 Reconnaissance-MDP and Planning-MDP
4.1 The RP Algorithm
4.2 Threat Decomposition and the Approximate RP Algorithm
5 Experiments
5.1 Experimental Setup
5.2 Frozen Lake Results
5.3 High-Dimensional Tasks: Point Gather, Circuit, and Jam
5.4 Recycling the Threat Function for a Different Environment
6 Related Work
6.1 Optimization-Based Approaches
6.2 Sampling and Learning-Based Approaches
7 Conclusion
References
VeriDL: Integrity Verification of Outsourced Deep Learning Services
1 Introduction
2 Preliminaries
3 Problem Statement
4 Authentication Method
4.1 Setup Protocol
4.2 Certify Protocol
4.3 Verify Protocol
4.4 Dealing with Decimal and Negative Values
5 Experiments
5.1 Setup
5.2 Efficiency of VeriDL
5.3 Verification vs. Re-computation of Model Updates
5.4 Robustness of Verification
6 Related Work
7 Conclusion and Future Work
References
A Unified Batch Selection Policy for Active Metric Learning
1 Introduction
2 Related Work
2.1 Perceptual Metric Learning
2.2 Active Learning for Classification
2.3 Active Learning of Perceptual Metrics
3 Proposed Method
3.1 Triplet-Based Active Metric Learning
3.2 Joint Entropy Measure for Batch Selection
3.3 Maximum-Entropy Model of the Joint Distribution
3.4 Greedy Algorithm for Batch Selection
3.5 Recursive Computation of Determinant of Covariance Matrix
4 Experiments
5 Conclusion, Limitations, and Future Work
References
Off-Policy Differentiable Logic Reinforcement Learning
1 Introduction
2 Preliminary
2.1 First-Order Logic Programming
2.2 Differentiable Inductive Logic Programming
2.3 Maximum Entropy Reinforcement Learning
3 Off-Policy Differentiable Logic Reinforcement Learning
3.1 Differentiable Logic Policy
3.2 Approximate Inference
3.3 Off-Policy Training
3.4 Hierarchical Policy Implementation
4 Experiments
4.1 Methods for Evaluation
4.2 Experiment Setting
4.3 Results and Analysis
4.4 Rabbids Game
5 Discussion
6 Conclusion
References
Causal Explanation of Convolutional Neural Networks
1 Introduction
2 Related Work
3 Causal Explanation of CNNs
3.1 Filters as Actual Causes
4 Experiments
4.1 Evaluating Visualizations
4.2 MNIST
4.3 Weakly Supervised Object Localization (WSOL)
4.4 Fine-Tuning Parameters and Compact Representation
5 Conclusion and Future Work
References
Interpretable Counterfactual Explanations Guided by Prototypes
1 Introduction
2 Related Work
3 Methodology
3.1 Background
3.2 Prototype Loss Term
3.3 Using K-D Trees as Class Representations
3.4 Categorical Variables
3.5 Removing Lpred
3.6 FISTA Optimization
4 Experiments
4.1 Evaluation
4.2 Handwritten Digits
4.3 Breast Cancer Wisconsin (Diagnostic) Dataset
4.4 Adult (Census) Dataset
5 Discussion
References
Finding High-Value Training Data Subset Through Differentiable Convex Programming
1 Introduction
2 Related Work
3 High-Value Data Subset Selection
3.1 Motivation and Problem Formulation
3.2 Joint Online Training of Subset Selection and Learning Model
3.3 Trainable Convex Online Subset Selection Layer
4 Experiment
4.1 Experimental Setup
4.2 Valuable Training Data
4.3 Fixing Mislabelled Data
4.4 Qualitative Analogy of Data Valuation
4.5 Ranking Function for Value Points
4.6 Computational Complexity
5 Conclusion
References
Consequence-Aware Sequential Counterfactual Generation
1 Introduction
2 Related Work
3 Problem Statement
3.1 Actions, Sequences of Actions and States
3.2 Consequence-Aware Cost Model
3.3 Consequence-Aware Sequential Counterfactual Generation
4 Consequence-Aware Sequential Counterfactuals (CSCF)
5 Experiments
5.1 Sequence Costs of Sequential Counterfactuals
5.2 Diversity of Sequential Counterfactuals
5.3 Effect of the Action Positions on Achieving the Counterfactual
6 Conclusion and Future Work
References
Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality*-10pt
1 Introduction
1.1 Why It Is Important to Evaluate the Quality of Explanations?
2 Background and Related Work
2.1 Explainability in Neural Networks
2.2 Evaluating Explanation Quality
3 Definitions
4 Investigating the Relationship Between Model Accuracy and Explanation Quality
4.1 Evaluating Explanation Quality
4.2 Comparing Different Models
4.3 Studying Explanations as a Model Evolves During Training
4.4 Selecting Models Based on Explanation Quality
5 Experimental Evaluation
5.1 Data
5.2 Implementation
5.3 Results - Comparing Different Models
5.4 Results - Studying a Model at Different Iterations During Training
5.5 Using Explanations for Model Selection
6 Conclusion
References
Explainable Multiple Instance Learning with Instance Selection Randomized Trees
1 Introduction
2 Instance Selection Randomized Trees
3 Related Work
4 Experiments
4.1 Private Dataset
4.2 Public Datasets
5 Conclusion
References
Adversarial Representation Learning with Closed-Form Solvers
1 Introduction
2 Prior Work
3 Problem Setting
3.1 Motivating Exact Solvers
4 Approach
4.1 Closed-Form Adversary and Target Predictor
4.2 Optimal Embedding Dimensionality
4.3 Gradient of Closed-Form Solution
5 Experiments
5.1 Fair Classification
5.2 Mitigating Sensitive Information Leakage
5.3 Ablation Study on Mixture of Four Gaussians
6 Concluding Remarks
References
Learning Unbiased Representations via Rényi Minimization
1 Introduction
2 Related Work
3 Problem Statement
3.1 Metrics for Continuous Statistical Dependence
4 Theoretical Properties
4.1 Consistency of the HGR_NN
4.2 Theoretical Comparison Against Simple Adversarial Algorithms
5 Method
6 Experiments
6.1 Synthetic Scenario
6.2 MNIST with Continuous Color Intensity
6.3 Real-World Experiments
7 Conclusion
References
Diversity-Aware k-median: Clustering with Fair Center Representation
1 Introduction
2 Related Work
3 Problem Statement and Complexity
3.1 NP-Hardness
3.2 Fixed-Parameter Intractability
3.3 Hardness of Approximation
4 Approximable Instances
4.1 Non-intersecting Facility Groups
4.2 Two Facility Groups
4.3 The Case i ri & k
5 Proposed Methods
5.1 Local Search
5.2 Relaxing the Representation Constraints
6 Experimental Evaluation
6.1 Results
7 Conclusion
References
Sibling Regression for Generalized Linear Models
1 Introduction
2 Related Work
3 Preliminaries
4 Sibling Regression for Generalized Linear Models
4.1 Extension to GLM
5 Experiments
5.1 Synthetic Experiment
5.2 Discover Life Moth Observations
6 Ethical Impact
7 Conclusion
References
Privacy Amplification via Iteration for Shuffled and Online PNSGD
1 Introduction
2 Related Work
3 Shuffled PNSGD
4 Asymptotic Analysis for When Using Shuffling and Fixed Noises
4.1 Laplace Noise
4.2 Gaussian Noise
5 Multiple Epochs Composition
6 Online Results for Decaying Noises
6.1 Laplace Noise
6.2 Gaussian Noise
7 Conclusion
References
Author Index

Systemvoraussetzungen

Als PDF speichern Als Link merken