A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Simplifies machine learning validation using kNN and conditional probability algorithms.
This paper reviews quantum machine learning from NISQ to fault tolerance.
Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum…
Julia accelerates machine learning in various fields with balance of efficiency and simplicity.
We consider a variant of the classic Ski Rental online algorithm with applications to machine learning. In our variant, we allow the skier access to a black-box machine-learning algorithm that provides an estimate of the probability that there will be at most a threshold number of ski-days. We derive a class of optimal…
Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most effic…
Machine learning algorithms can unintentionally discriminate; tools detect and fix this.
Cancer analysis and prediction is the utmost important research field for well-being of humankind. The Cancer data are analyzed and predicted using machine learning algorithms. Most of the researcher claims the accuracy of the predicted results within 99%. However, we show that machine learning algorithms can easily pr…
New algorithms improve machine learning performance with explicit regret bounds.
Proposes MLPSVM for multi-label learning, improving on binary relevance.
Weisfeiler and Leman enhance graph learning for machine learning tasks.
Interactive visualization helps understand complex machine learning models.
This paper explores hyperparameter optimization for machine learning models.
This study examines how learning algorithms affect collective action in machine learning.
We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common m…
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
Machine learning models outperform traditional CAPM in forecasting financial asset prices.
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate to…
Developed mlf-core for deterministic machine learning.
A new method detects changes in machine learning models over time.
Bayesian learning rule unifies and generalizes various machine learning algorithms.
The study examines machine learning classification algorithms and their generalizability using Framingham Heart Study data.
A theorem for debiasing machine learning with finite sample guarantees.
DIGEN benchmark provides synthetic datasets for ML algorithm evaluation.
Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this …
MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduces the MM algorithm framework in general and via three popular example applications: Gaussian mixture regressions, multinomial logistic regre…
Research shows bias in machine learning can be due to algorithmic flaws, not just data.
Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the …
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discr…
New GPU algorithm boosts machine learning with larger datasets.
With increasing competition and pace in the financial markets, robust forecasting methods are becoming more and more valuable to investors. While machine learning algorithms offer a proven way of modeling non-linearities in time series, their advantages against common stochastic models in the domain of financial market…
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
We apply machine learning to the problem of finding numerical Calabi-Yau metrics. Building on Donaldson's algorithm for calculating balanced metrics on Kähler manifolds, we combine conventional curve fitting and machine-learning techniques to numerically approximate Ricci-flat metrics. We show that machine learning is …
Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in …
Sommelier recommends machine learning algorithms for datasets based on scholarly knowledge.
This paper improves parallel belief propagation for scalable machine learning.
AutoML discovers complete machine learning algorithms from basic operations.
In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large number of datasets. MLaut provides a high-level workflow interface to machine algorithm algorithms, implements a l…
Maximal correlation framework improves fairness in machine learning algorithms.
This review covers predictive uncertainty estimation in machine learning.
Paper tackles Byzantine attacks in Federated Learning by clustering and robustifying.
This study analyzes and optimizes hyperparameters for machine learning models.
Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations. These algorithms are compared to a new one that solves a fixed point problem by using deep learning techniques. This new algorithm appears to be competit…
MLJ offers a Julia package for composing machine learning models.
Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge…
Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulner…
Machine learning algorithms typically rely on optimization subroutines and are well-known to provide very effective outcomes for many types of problems. Here, we flip the reliance and ask the reverse question: can machine learning algorithms lead to more effective outcomes for optimization problems? Our goal is to trai…
The paper proposes calibration to improve algorithm performance using machine learning predictions.