Towards a Prediction of Machine Learning Training Time to Support
Continuous Learning Systems Development
- URL: http://arxiv.org/abs/2309.11226v1
- Date: Wed, 20 Sep 2023 11:35:03 GMT
- Title: Towards a Prediction of Machine Learning Training Time to Support
Continuous Learning Systems Development
- Authors: Francesca Marzi, Giordano d'Aloisio, Antinisca Di Marco, and Giovanni
Stilo
- Abstract summary: We present an empirical study of the Full.
Time Complexity (FPTC) approach by Zheng et al.
We study the formulations proposed for the Logistic Regression and Random Forest classifiers.
We observe how, from the conducted study, the prediction of training time is strictly related to the context.
- Score: 5.207307163958806
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The problem of predicting the training time of machine learning (ML) models
has become extremely relevant in the scientific community. Being able to
predict a priori the training time of an ML model would enable the automatic
selection of the best model both in terms of energy efficiency and in terms of
performance in the context of, for instance, MLOps architectures. In this
paper, we present the work we are conducting towards this direction. In
particular, we present an extensive empirical study of the Full Parameter Time
Complexity (FPTC) approach by Zheng et al., which is, to the best of our
knowledge, the only approach formalizing the training time of ML models as a
function of both dataset's and model's parameters. We study the formulations
proposed for the Logistic Regression and Random Forest classifiers, and we
highlight the main strengths and weaknesses of the approach. Finally, we
observe how, from the conducted study, the prediction of training time is
strictly related to the context (i.e., the involved dataset) and how the FPTC
approach is not generalizable.
Related papers
- Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences [6.067007470552307]
We propose a methodology for finding sequences of machine learning models that are stable across retraining iterations.
We develop a mixed-integer optimization formulation that is guaranteed to recover optimal models.
Our method shows stronger stability than greedily trained models with a small, controllable sacrifice in predictive power.
arXiv Detail & Related papers (2024-03-28T22:45:38Z) - Machine Unlearning of Pre-trained Large Language Models [17.40601262379265]
This study investigates the concept of the right to be forgotten' within the context of large language models (LLMs)
We explore machine unlearning as a pivotal solution, with a focus on pre-trained models.
arXiv Detail & Related papers (2024-02-23T07:43:26Z) - Federated Learning with Projected Trajectory Regularization [65.6266768678291]
Federated learning enables joint training of machine learning models from distributed clients without sharing their local data.
One key challenge in federated learning is to handle non-identically distributed data across the clients.
We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
arXiv Detail & Related papers (2023-12-22T02:12:08Z) - PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [71.63186089279218]
This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.
On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.
On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
arXiv Detail & Related papers (2023-09-13T17:55:11Z) - Generalization Properties of Retrieval-based Models [50.35325326050263]
Retrieval-based machine learning methods have enjoyed success on a wide range of problems.
Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored.
We present a formal treatment of retrieval-based models to characterize their generalization ability.
arXiv Detail & Related papers (2022-10-06T00:33:01Z) - Learning continuous models for continuous physics [94.42705784823997]
We develop a test based on numerical analysis theory to validate machine learning models for science and engineering applications.
Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.
arXiv Detail & Related papers (2022-02-17T07:56:46Z) - Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary
Time-Series [20.958959332978726]
SAF integrates a self-adaptation stage prior to forecasting based on backcasting'
Our method enables efficient adaptation of encoded representations to evolving distributions, leading to superior generalization.
On synthetic and real-world datasets in domains where time-series data are known to be notoriously non-stationary, such as healthcare and finance, we demonstrate a significant benefit of SAF.
arXiv Detail & Related papers (2022-02-04T21:54:10Z) - Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
We consider the impact of the training set and its structure on the quality of the long-term prediction.
We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
arXiv Detail & Related papers (2021-12-15T20:09:20Z) - On Effective Scheduling of Model-based Reinforcement Learning [53.027698625496015]
We propose a framework named AutoMBPO to automatically schedule the real data ratio.
In this paper, we first theoretically analyze the role of real data in policy training, which suggests that gradually increasing the ratio of real data yields better performance.
arXiv Detail & Related papers (2021-11-16T15:24:59Z) - A Meta-learning Approach to Reservoir Computing: Time Series Prediction
with Limited Data [0.0]
We present a data-driven approach to automatically extract an appropriate model structure from experimentally observed processes.
We demonstrate our approach on a simple benchmark problem, where it beats the state of the art meta-learning techniques.
arXiv Detail & Related papers (2021-10-07T18:23:14Z) - Introduction to Rare-Event Predictive Modeling for Inferential
Statisticians -- A Hands-On Application in the Prediction of Breakthrough
Patents [0.0]
We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance.
We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility.
We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
arXiv Detail & Related papers (2020-03-30T13:06:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.