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.
VOLARE provides standardized realized volatility measures from financial data.
A new VWAP execution method using transformer and signature features.
Enhances portfolio construction with tailored regime forecasts for individual assets.
A universal LSTM model outperforms asset-specific models in forecasting stock volatilities.
LEMs extend transformer-based architectures for complex execution problems.
Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a s…
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline c…
We consider the problem of finding a model-free upper bound on the price of an American put given the prices of a family of European puts on the same underlying asset. Specifically we assume that the American put must be exercised at either or and that we know the prices of all vanilla European puts with th…
Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermediate computations are the same, we propose a pipeline-aware approach to hyperparameter tuning. Our approach optimizes both the design and exe…
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML…
Study proposes a statistical testing framework for evaluating clustering pipelines.
Investigates fairness in pipeline models where individuals may drop out.
Improved asset pricing using uncertainty-adjusted sorting in machine learning models.
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …
Automates supervised learning pipeline design with matrix and tensor factorization.
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …
New model optimizes oil product distribution via pipelines.
Pipeline decomposes portfolio optimization problems into smaller, solvable subproblems.
Paper proposes a statistical test for feature selection pipelines using selective inference.
Pipelined Backpropagation trains large models without batches efficiently.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
Modyn automates continuous ML model training on growing datasets.
TODS automates time series outlier detection with customizable pipelines.
Hungarian text processing improved with efficient, accurate NLP pipelines.
A rigorous ML pipeline for binary classification in biomedical studies, focusing on pancreatic cancer.
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization. Unfortunately, to preserve the statistical efficiency of sequential training, existing PP techniques sacrifice hardware efficien…
Pipeline for comparing trading algorithms in finance and crypto.
RL pipeline simplifies knot diagrams, including very hard unknots.
This paper explores how LLMs can improve pipeline-based conversational agents.
Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing. Yet, it is generally well accepted that machine learning applications require not only model building, but also data preprocessing. In other words, p…
Pipeline selects best RL policy from limited data.
Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchica…
Machine learning tasks entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous or uninformative outputs, the pipeline may fail or produce incorrect results. Inferring the root cause of failures and unexpected behavior…
New portfolio optimization method considers both asset-specific and systemic risks for financial networks.
Paper proposes EEIPU, a memoization-aware BO algorithm to reduce hyperparameter tuning costs.
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical…
Paper introduces Modular Jets for diagnosing model decompositions in pipelines.
Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…
To solve a machine learning problem, one typically needs to perform data preprocessing, modeling, and hyperparameter tuning, which is known as model selection and hyperparameter optimization.The goal of automated machine learning (AutoML) is to design methods that can automatically perform model selection and hyperpara…
Predicts asset return distributions using LSTM and quantile regression.
New Roman pipeline detects astronomical transients.
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on marmoset brains uses two-photon microscopy fluorescence images of axonal projections to collect the neuron connectivity from defined brain r…
The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters. These parameters are frequently set according to the local goal o…
Pipeline integrates cross-sectional and longitudinal multi-omics data for IBD research.
Study on AutoML robustness with dirty data.
Automatic machine learning (AutoML) is an area of research aimed at automating machine learning (ML) activities that currently require human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architectu…
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data…