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Found 12 papers, 10 papers with code
ADGym: Design Choices for Deep Anomaly Detection
Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing.
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation
We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor.
Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
ADBench: Anomaly Detection Benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?
PyGOD: A Python Library for Graph Outlier Detection
PyGOD is an open-source Python library for detecting outliers in graph data.
Uncovering the Source of Machine Bias
We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions.
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution.
COPOD: Copula-Based Outlier Detection
In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
Optimal Sparse Decision Trees
Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's.
