Paper tackles supervision bottleneck in machine learning.
problem Difficulty in generating supervision signals for learning models.
method Describes several learning paradigms to alleviate the supervision bottleneck.
result Illustrates the benefit of these paradigms in inducing semantic representations from text.
Develops a framework for fair semi-supervised learning.
problem Balancing fairness and accuracy in semi-supervised learning.
method Formulates a framework as an optimization problem, incorporating classifier loss, label propagation loss, and fairness constraints.
result Achieves fair semi-supervised learning with better accuracy-fairness trade-off than fair supervised learning.
New algorithm for safer machine learning with different testing and training distributions.
problem Challenges in modern machine learning where training and testing distributions differ.
method First-order optimization algorithm for superquantile-based learning.
result Promising numerical results show the approach's effectiveness.
Quantum machine learning can't achieve polylogarithmic runtimes, even with quantum data access.
problem Bounding the minimum number of samples required for supervised quantum learning.
method Statistical learning theory and quantum machine learning algorithms.
result Quantum machine learning algorithms for supervised learning have at most polynomial speedups over classical algorithms.
Paper proposes an algorithm to recover full supervision from weakly labeled data.
problem Machine learning requires expensive data annotation, motivating the use of weak supervision.
method The paper introduces a disambiguation principle and an empirical disambiguation algorithm for partial labelling.
result The algorithm achieves exponential convergence rates under learnability assumptions.
Proposes SSFair to improve fairness in machine learning using unlabeled data.
problem Fairness issues in machine learning systems due to lack of labeled data.
method Semi-supervised learning with neural networks to leverage unlabeled data.
result Improves fairness of decision-making processes without requiring labeled data.
Is all of machine learning supervised to some degree? The field of machine learning has traditionally been categorized pedagogically into supervised vs unsupervised learning; where supervised learning has typically referred to learning from labeled data, while unsupervised learning has typically referred to learning …
Study compares semi-supervised learning methods for anomaly detection in hydraulic systems.
problem Challenges in anomaly detection for hydraulic systems due to scarce labeled data.
method Compared various semi-supervised learning methods including traditional models and deep neural networks.
result Customized extreme learning machine (HELM) model achieved state-of-the-art performance.
S4 learns new self-supervision automatically, improving accuracy with less human effort.
problem Lack of direct supervision in machine learning.
method Combines deep learning and probabilistic logic to automatically generate and verify new self-supervision.
result S4 can automatically propose accurate self-supervision, matching supervised methods with less human effort.
Prediction and explanation are key objects in supervised machine learning, where predictive models are known as black boxes and explanatory models are known as glass boxes. Explanation provides the necessary and sufficient information to interpret the model output in terms of the model input. It includes assessments of…
Abstract: Surveying connections between ML and Control Theory.
problem Addressing the intersection of Machine Learning and Control Theory.
method Develops connections through reinforcement learning, supervised learning, deep learning, and stochastic gradient descent.
result Machine Learning and Control Theory are interconnected, with ML solving large control problems and Control Theory providing tools for ML.
Proposes a constrained labeling method for weakly supervised learning.
problem Combining weak supervision signals while navigating misleading correlations.
method Randomized constrained labeling within a defined space.
result Randomized constrained labeling converges after few iterations and outperforms other methods.
Machine learning uncovers hidden patterns in Calabi-Yau hypersurfaces.
problem Identifying and clustering Calabi-Yau hypersurfaces from weighted-P4s.
method Supervised and unsupervised machine learning techniques.
result High accuracy in predicting topological parameters and identifying hypersurfaces.
This paper examines sources of uncertainty in machine learning from a statistical perspective.
problem Quantifying uncertainty in supervised machine learning models.
method A conceptual, basic science approach examining aleatoric and epistemic uncertainty.
result Sources of uncertainty are diverse and cannot always be decomposed into aleatoric and epistemic.
Improves data labeling efficiency in machine learning.
problem Efficiency in data labeling for machine learning models.
method Weakly supervised learning, active labeling, stochastic gradient descent.
result Derives a new algorithm for active labeling that scales better with input dimension.
Machine learning applied to algebraic geometry for physics problems.
problem Reformulating algebraic geometry problems as tensor mappings for machine learning.
method Supervised and unsupervised machine learning techniques applied to algebraic geometry problems.
result Machine learning provides insights into the structure of algebraic geometry data.
Bayesian inference corrects bias in supervised learning datasets.
problem Correcting bias in supervised machine learning datasets.
method Bayesian inference framework to adjust posterior distribution.
result Improved model fitting to original dataset.
Overview of SML techniques with banking applications.
problem Credit risk modeling in banking.
method Tree-based ensemble algorithms, Feedforward NNs, hyper-parameter optimization, machine learning interpretability.
result Comparison of ML algorithm features and their application in banking.
Semi-automatic data annotation helps experts label unlabeled samples based on feature space projection.
problem Laborious manual data annotation for machine learning.
method Interactive semi-automatic approach using feature space projection and semi-supervised learning.
result Reduces user annotation effort and improves classification accuracy.
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we pr…
In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract …
This article is an overview of supervised machine learning problems for regression and classification. Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds. Implicit re…
Cyclic Boosting offers detailed prediction understanding for machine learning models.
problem Complex machine learning models are often black boxes, making individual predictions hard to understand.
method Cyclic Boosting is a novel machine learning algorithm that provides detailed understanding of predictions.
result Cyclic Boosting enables detailed understanding of how predictions are made, even for complex models.
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…
Enhances machine learning interpretability using category theory.
problem Improving machine learning interpretability and social implementation.
method Develops a categorical framework for structured understanding of supervised learning.
result Introduces the Gauss-Markov Adjunction for clarifying residuals and parameters.
Machine learning in high-energy physics faces challenges from nuisance parameters, which are reviewed and techniques to mitigate their impact are discussed.
problem Impact of nuisance parameters on machine learning performance in high-energy physics.
method Review and discussion of techniques including nuisance-parameterized models, modified or adversary losses, semi-supervised learning, and inference-aware techniques.
result Various methods to reduce the impact of nuisance parameters and improve model performance in high-energy physics.
Bayesian approach to selecting data for machine learning.
problem Iterative data selection in machine learning algorithms.
method Embedding data selection into decision theory and deriving Bayes-optimal criteria.
result Mitigates confirmation bias in data selection.
DSL uses supervised learning to optimize portfolios, improving stability and performance.
problem Optimizing robust portfolios in financial markets.
method DSL reframes portfolio construction as a supervised learning problem, using cross-entropy loss and optimizing Sharpe or Sortino ratios. Deep Ensemble methods are employed to reduce variance.
result DSL outperforms traditional and machine learning methods, achieving higher median returns and more stable risk-adjusted performance.
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using on…
Survey of self-supervised image representation learning methods.
problem Learning meaningful image representations without labeled data.
method Comprehensive review and taxonomy of self-supervised methods.
result Quality of self-supervised representations is comparable to supervised learning.
QCML improves bond similarity learning in illiquid markets.
problem Improving similarity learning for illiquid corporate bonds.
method Quantum Cognition Machine Learning (QCML) for supervised distance metric learning.
result QCML outperforms classical tree-based models in high-yield markets.
Semi-supervised learning uses unlabeled data to improve fairness without sacrificing accuracy.
problem Discrimination in machine learning models.
method Pseudo labeling, re-sampling, ensemble learning.
result Unlabeled data can help achieve a better balance between accuracy and fairness.
WRENCH benchmarks weak supervision datasets for machine learning.
problem Lack of standardized evaluation for weak supervision datasets.
method Developed a comprehensive benchmark platform WRENCH.
result Demonstrated the efficacy of WRENCH as a benchmark platform.
Extends RF proximities to all supervised distance-based machine learning contexts.
problem Limited utility of RF proximities in various machine learning tasks.
method Introduces generalized Proximity Forest (PF) model and variant for regression.
result Demonstrates unique advantages over RF and k-nearest neighbors models.
Survey on making machine learning models more understandable.
problem Making predictions from machine learning models transparent and understandable for humans.
method Conducts a survey on explainable supervised machine learning approaches.
result Classifies explainable SML approaches and discusses future directions.
MEC improves efficiency and robustness in semi-supervised inference.
problem Efficient inference with limited labeled data and robust uncertainty quantification.
method Machine-Learning-Assisted Generalized Entropy Calibration (MEC) using cross-fitted, calibration-weighted PPI.
result MEC achieves semiparametric efficiency bounds under weaker assumptions and provides near-nominal coverage.
AAT separates robust and non-robust features without supervision.
problem Adversarial vulnerability and accuracy reduction in machine learning models.
method Adversarial Asymmetric Training (AAT) algorithm.
result Preserves accuracy and achieves better disentanglement than previous methods.
Novel approach to contextual bandits using self-supervised learning.
problem Exploiting rich data representations in computer vision without explicit labels.
method Combining contextual bandit objective with self-supervision objective.
result Substantial gains in cumulative reward on computer vision datasets.
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting…
Quantum machine learning offers advantages for broader learning tasks.
problem Demonstrate QML advantage over classical methods for general learning tasks.
method Construct a new family of supervised learning tasks and prove their hardness.
result Prove provable advantage of QML based on general quantum computational advantages.
Weisfeiler and Leman enhance graph learning for machine learning tasks.
problem Learning from graph data in machine learning.
method Weisfeiler and Leman algorithm applied to graph and node representation learning.
result The algorithm improves graph and node representation learning in machine learning.
In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that prespecified target region. For example, one might be interested in the clusteri…
A new method detects anomalous sounds using self-supervised learning.
problem Detecting anomalous sounds in noisy data.
method Flow-based self-supervised density estimation using Normalizing Flows.
result The method improves anomaly detection performance significantly.
The paper proposes a method to ensure fairness in machine learning models.
problem Ensuring fairness in machine learning models powered by supervised learning.
method Optimal affine transport and Wasserstein-2 barycenter to characterize the Pareto frontier between prediction error and statistical disparity.
result The proposed method effectively balances prediction accuracy and fairness, as demonstrated by numerical simulations.
A new method for weakly supervised learning that improves model accuracy.
problem Training machine learning models with precise labels is expensive; weak supervision provides a low-cost alternative.
method Data consistent weak supervision algorithm that searches over classifiers to find plausible labelings, considering features of the training data and estimating labels for low/no coverage data.
result Empirically, the method significantly outperforms state-of-the-art weak supervision methods on text and image classification tasks.
The paper investigates how supervised learning and self-play improve sample efficiency in teaching AI to communicate.
problem Improving sample efficiency in training AI to use natural language.
method Investigates the relationship between supervised learning and self-play, introduces supervised self-play (S2P).
result First training agents via supervised learning followed by self-play outperforms self-play followed by supervised learning.
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…
PLRM synthesizes labels from mismatched sources for better training sets.
problem Creating labeled training sets is a major challenge in machine learning.
method PLRM uses probabilistic modeling to synthesize labels from indirect supervision sources with different output spaces.
result PLRM outperforms baselines by 2%-9% on various tasks.