Search Results for author:
Found 9 papers, 2 papers with code
Graph Feature Preprocessor: Real-time Extraction of Subgraph-based Features from Transaction Graphs
In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering and fraud patterns in financial transaction graphs in real time.
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets.
Search-based Methods for Multi-Cloud Configuration
Our experiments indicate that (a) many state-of-the-art cloud configuration solutions can be adapted to multi-cloud, with best results obtained for adaptations which utilize the hierarchical structure of the multi-cloud configuration domain, (b) hierarchical methods from AutoML can be used for the multi-cloud configuration task and can outperform state-of-the-art cloud configuration solutions and (c) CB achieves competitive or lower regret relative to other tested algorithms, whilst also identifying configurations that have 65% lower median cost and 20% lower median time in production, compared to choosing a random provider and configuration.
What can multi-cloud configuration learn from AutoML?
Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in.
SnapBoost: A Heterogeneous Boosting Machine
At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean sense.
Breadth-first, Depth-next Training of Random Forests
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures.
Sampling Acquisition Functions for Batch Bayesian Optimization
We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process.
Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms
Gradient boosting decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in many machine learning tasks.
Snap ML: A Hierarchical Framework for Machine Learning
We describe a new software framework for fast training of generalized linear models.
