Online machine learns from signals robustly using consensus optimization.
problem Signal classification in online settings.
method Reproducing kernel Hilbert space with consensus optimization.
result Robust signal classification achieved.
On-device research index
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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.
169,051 papers · 148 categories
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48 results for “Online machine learning”
Proposes standards for evaluating online machine learning methods in evolving data streams.
problem Difficulty in evaluating online machine learning methods under realistic conditions.
method Proposes comprehensive evaluation standards, performance measures, and evaluation strategies.
result Provides a new Python framework (float) for modular integration of libraries and custom code.
Paper proposes AdaBoost-assisted ELM for efficient online sequential classification.
problem Efficient online sequential classification with improved accuracy and stability.
method Utilizes AdaBoost for cost-sensitive learning and forgetting mechanism for stability.
result Achieves 94.41% accuracy on MNIST dataset with reduced standard deviation.
Bayesian optimization improves machine learning system tuning with online and offline experiments.
problem Limited simultaneous experiments in complex policy spaces.
method Augment online field experiments with an offline simulator and apply multi-task Bayesian optimization.
result Substantial gains from including biased offline data in live machine learning systems.
iPrescribe offers fast online offer recommendations using deep learning.
problem Online offer recommendation in real-time.
method Ensemble of deep learning and machine learning algorithms, optimized streaming technology stack, and efficient LSTM deployment.
result 90th percentile recommendation latency of 38 milliseconds.
Reduces false positives in classifying rare online platforms.
problem Challenges in accurately identifying rare online platforms with ML.
method Calibrated probabilities and ensembles to reduce bias.
result Significantly reduces false positives in rare event detection.
Online learning improves big data accuracy quickly.
problem Heterogeneity in big data analysis.
method Online machine learning for big data.
result Online learning converges quickly to batch accuracy.
An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promisescs.LG
This chapter explores Meta-Learning algorithms to solve online data challenges in machine learning.
problem Lack of sufficient samples per class and limited distributed data in online learning.
method Investigates Meta-Learning (MTL) algorithms to address these challenges.
result MTL algorithms can learn to learn and handle unseen classes and online data effectively.
Simplifies machine learning validation using kNN and conditional probability algorithms.
problem Validating machine learning models in practical applications.
method Reformulated regression and classification problems using kNN and conditional probability algorithms.
result Online capability and reduced memory usage compared to kNN.
OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination wit…
New machine learning algorithms inspired by ecological principles.
problem Improving machine learning performance.
method Inspired by ecological dynamics, developed new online SVM algorithms.
result New algorithms outperform traditional methods on the MNIST dataset.
This research improves online learning by correcting for target shift in machine learning.
problem Online learning struggles with distributional shift, especially in target values.
method Derives closed-form expressions for online and offline learning, and target correction.
result Online kernel-based learning can learn the same predictor as offline learning with target correction.
Paper tackles online adaptation to changing label distributions.
problem Adapting machine learning models to changing label distributions in real-world settings.
method Leverages novel analysis to show estimation of expected test loss is possible without true labels. Proposes adaptation algorithms inspired by classical online learning techniques.
result Empirically verified that OGD is particularly effective and robust to various label shift scenarios.
Paper tackles online budgeted learning for feature acquisition in machine learning.
problem Finding optimal feature values to acquire from each instance in a data stream under budget constraints.
method Introduces a general framework for online budgeted learning, proposing two feature value acquisition policies: random and adaptive.
result Adaptive policies outperform random policies for most budget limitations and datasets, achieving near-optimal results in some cases.
Machine learning experiments often mislead due to unmet assumptions.
problem Machine learning experiments with pooled data may not meet necessary assumptions for unbiased causal effect estimation.
method Analysis of assumptions required for unbiased causal effect estimation in machine learning experiments.
result Practical applications of A/B-tests with machine learning models may not yield unbiased estimates of causal effect.
Optimal ski rental strategies with machine learning predictions.
problem Minimizing ski rental costs with uncertain future days.
method Derive optimal randomized algorithms using machine learning predictions.
result Class of optimal algorithms with minimized competitive ratio.
The paper proposes calibration to improve algorithm performance using machine learning predictions.
problem Improving real-world performance of online algorithms with machine learning predictions.
method Calibration as a tool to bridge the gap between prediction uncertainty and algorithm design.
result Calibrated advice leads to more effective guidance in high-variance settings and significant performance improvements in real-world data.
The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical …
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, porta…
BOA improves financial forecasting by combining expert models.
problem Challenges in choosing between multiple machine learning models for financial forecasting.
method Online aggregation of expert models using Bernstein Online Aggregation (BOA) procedure.
result BOA leads to better portfolio performance, higher Sharpe Ratio, and lower shortfall.
A new framework for real-time multi-speaker diarization without prior registration.
problem Real-time multi-speaker diarization without prior registration and pretraining.
method Semi-supervised and self-supervised learning methods applied to a new benchmark.
result Robust performance in the online MiniVox framework.
Improved online classification with accurate predictions.
problem Online classification challenges with limited data.
method Designing an online learner that uses predictions to reduce regret.
result Expected regret is better than worst-case analysis, especially with accurate predictions.
Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systemseess.SY
Paper proposes online optimization for uncertain systems using machine learning and DRO.
problem Optimization of uncertain dynamical systems with distributional uncertainty.
method Combines machine learning with Distributional Robust Optimization (DRO) to handle uncertainty.
result Online solutions with probabilistic regret bounds for uncertain systems.
Paper proposes a machine learning method to predict sale efficacy.
problem Determining the efficacy of online sales from discounts alone.
method Machine learning-based heuristic using Support Vector Machine.
result Predicts sale efficacy with 91.11% accuracy.
Study proposes a machine learning method for bid shading in first-price auctions.
problem Maintaining strategy equilibrium in first-price auctions.
method Machine learning approach to model optimal bid shading.
result Demonstrates superiority and robustness of new approach across various metrics.
This paper explores online learning of dynamics and state using ensemble Kalman filters.
problem Reconstructing dynamics from partial and noisy observations in real-time.
method Ensemble Kalman filter (EnKF) family of algorithms for online learning of dynamics and state.
result Demonstrates the efficiency and accuracy of online learning methods using Lorenz models.
Efficiently tunes hyperparameters for online traffic time series prediction.
problem Online hyperparameter tuning for machine learning models in time series prediction.
method Online hyperparameter optimization algorithm for Kernel Ridge regression.
result Achieves better or similar prediction accuracy with significantly less computation time.
Paper presents a neural network for estimating wavefronts in direction of arrival scenarios.
problem Estimating the number of wavefronts in direction of arrival scenarios.
method Cross-entropy trained multilayer neural network for online adaptation of antenna array imperfections.
result The method outperforms classical model order selection schemes in accuracy, especially at low signal-to-noise-ratios.
Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining, etc. This article aims to provide a comprehensive survey and a structural under…
Boosts weak online learners to strong ones with sublinear regret.
problem Online learning agnostic setting without strong guarantees.
method Reduction to online convex optimization, boosting via marginally-better-than-trivial regret guarantees.
result First agnostic online boosting algorithm with sublinear regret.
New algorithm learns and unlearns from streaming data efficiently.
problem Continuous learning and unlearning from production data streams.
method Translated batch unlearning techniques to online setting using regret, sample complexity, and deletion capacity.
result Achieved logarithmic regret bound of for online unlearning.
Online and stochastic learning has emerged as powerful tool in large scale optimization. In this work, we generalize the Douglas-Rachford splitting (DRs) method for minimizing composite functions to online and stochastic settings (to our best knowledge this is the first time DRs been generalized to sequential version).…
Framework for interactive learning to minimize user experience regressions.
problem Sub-optimal user experiences due to frequent exploration of options.
method Explore-Exploit framework for online learning operators.
result Efficiencies achieved in integrating online learning with run-time services.
Converts GBDT trees to neural networks for online updates.
problem Performance loss in converting GBDT trees to neural networks.
method Converts existing GBDT implementations to neural network architectures, allowing online updates of decision splits.
result Learning bounds for neural network architecture with updated splits.
Online learning improves dengue fever detection with minimal training data.
problem Improving dengue fever detection accuracy with limited training data.
method Online learning approach for incremental learning from patient symptoms and diagnostic investigations.
result The proposed model effectively identifies patients with high likelihood of dengue disease.
Study defenses against data poisoning attacks in online learning.
problem Data poisoning attacks on machine learning models.
method Rigorous study of four standard defenses in both a powerful and a realistic threat model.
result The effectiveness of defenses depends on the ease of the learning problem.
Deep learning detects radical content on social media.
problem Detecting extremist content on social media platforms.
method Employed an LSTM based feed forward neural network to classify radical content.
result Achieved a precision of 85.9% in detecting radical content.
Study on collaborative vs. non-collaborative online and bandit convex optimization.
problem Minimizing average regret in distributed online and bandit convex optimization.
method Analyzes the impact of collaboration in adaptive and zeroth-order feedback settings.
result Collaboration is beneficial in high-dimensional federated online optimization with limited feedback.
Develops Bayesian filtering for online learning and related problems.
problem Sequential machine learning challenges, especially non-stationarity, model misspecification, and high dimensionality.
method Modular adaptive framework, provably robust filter, and sequential parameter updates.
result Improved performance in dynamic, high-dimensional, and misspecified models.
Deep neural networks improve online learning by ensuring convergence to best strategies.
problem Challenges in online learning due to dependencies between observations.
method Lipschitz regularized deep neural networks for online learning.
result Guaranteed convergence to the best prediction strategy.
Study uses online data to identify malicious websites, addressing imbalance.
problem Identifying malicious websites from many more benign ones.
method Integrated resampling approach combining SMOTE and PSO.
result Proposed approach outperforms other resampling methods.
Study shows how online personalization can lead to unfair models due to biased user responses.
problem Fairness issues in online personalization systems due to biased user responses.
method Formulated a regularization-based approach to mitigate biases in machine learning models.
result Demonstrated that online personalization can cause models to learn unfair behavior from biased user responses.
Study predicts purchasing decisions of online food delivery customers.
problem Understanding and predicting consumer purchasing decisions in online food delivery.
method Used machine learning techniques including CART, C4.5, random forest, and rule-based classifiers to predict purchasing decisions.
result C4.5 decision tree model outperformed others with 91.67% accuracy.
OL4EL optimizes edge learning on resource-constrained servers.
problem Resource constraints on edge servers hinder effective distributed machine learning.
method Online Learning for EL (OL4EL) framework using budget-limited multi-armed bandit model.
result OL4EL significantly improves learning performance while conserving resources.
Extends boosting to multiclass online agnostic classification.
problem Online multiclass classification with weak learners.
method Reduces multiclass online agnostic boosting to online convex optimization.
result First boosting algorithm for online agnostic multiclass classification.
Paper proposes a deep learning method for online sequential data with multiple objectives.
problem Dealing with multiple loss functions in online deep learning tasks.
method A novel online deep learning training procedure that can handle various neural network architectures.
result Demonstrated effectiveness on the Neyman-Pearson classification problem.
New method combines machine learning with data assimilation for model error correction.
problem Correcting model errors using sparse and noisy observations.
method Hybrid machine learning and data assimilation methods.
result Tendency correction outperforms resolvent correction in data assimilation experiments.
We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approac…