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.
Pipeline decomposes portfolio optimization problems into smaller, solvable subproblems.
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…
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…
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.
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 …
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
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…
Clapping reduces memory usage in distributed optimization by reusing data samples.
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…
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…
OpTorch optimizes deep learning for resource-limited environments.
Paper proposes EEIPU, a memoization-aware BO algorithm to reduce hyperparameter tuning costs.
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…
We investigate the theoretical limits of pipeline parallel learning of deep learning architectures, a distributed setup in which the computation is distributed per layer instead of per example. For smooth convex and non-convex objective functions, we provide matching lower and upper complexity bounds and show that a na…
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
A rigorous ML pipeline for binary classification in biomedical studies, focusing on pancreatic cancer.
AVATAR uses a surrogate model to quickly evaluate ML pipelines, saving time and resources.
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuni…
GAMA is a user-friendly AutoML system for machine learning pipeline optimization.
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We pr…
Neural networks struggle with TSP beyond small instances, requiring new approaches.
New approach speeds up optimization with repeated gradient steps on same batch.
Analog method solves portfolio optimization problems faster and more efficiently.
Study proposes a statistical testing framework for evaluating clustering pipelines.
Investigates fairness in pipeline models where individuals may drop out.
Study improves confidence measures in medical imaging pipelines by addressing bias.
Automates supervised learning pipeline design with matrix and tensor factorization.
In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification mod…
Paper proposes a statistical test for feature selection pipelines using selective inference.
Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one…
New approach identifies and explains errors in machine learning pipelines.
Consider a device that is connected to an edge processor via a communication channel. The device holds local data that is to be offloaded to the edge processor so as to train a machine learning model, e.g., for regression or classification. Transmission of the data to the learning processor, as well as training based o…
Paper introduces probabilistic approach to CO layers in ML.
Pipelined Backpropagation trains large models without batches efficiently.
The paper proposes a ML workflow for B2B sales prediction.
As machine learning is applied more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" - brittle, ad hoc ML systems. To address thes…
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.
LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.
SRO optimizes decisions against worst-case sampler induced by generative models.
Optimizes calibration error estimators for better classifier trustworthiness.
Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current predicti…
We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search. Neither prior domain knowledge about the data nor feature preprocessing is needed. Using…
A new penalty-free method optimizes portfolios without quantum annealing penalties.
Estimates mode from partial feedback, improving AI learning pipelines.