Machine Learning
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Optimizing Epochal Evolutionary Search: Population-Size Dependent Theory
Machine Learning - Tập 45 - Trang 77-114 - 2001
Epochal dynamics, in which long periods of stasis in an evolving population are punctuated by a sudden burst of change, is a common behavior in both natural and artificial evolutionary processes. We analyze the population dynamics for a class of fitness functions that exhibit epochal behavior using a mathematical framework developed recently, which incorporates techniques from the fields of mathem...... hiện toàn bộ
Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication
Machine Learning - Tập 109 - Trang 1727-1747 - 2020
Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-ag...... hiện toàn bộ
Wasserstein-based fairness interpretability framework for machine learning models
Machine Learning - Tập 111 - Trang 3307-3357 - 2022
The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of predictors, we take into account favora...... hiện toàn bộ
A unified probabilistic framework for robust manifold learning and embedding
Machine Learning - - 2017
Training a reciprocal-sigmoid classifier by feature scaling-space
Machine Learning - Tập 65 - Trang 273-308 - 2006
This paper presents a reciprocal-sigmoid model for pattern classification. This proposed classifier can be considered as a Φ-machine since it preserves the theoretical advantage of linear machines where the weight parameters can be estimated in a single step. The model can also be considered as an approximation to logistic regression under the framework of Generalized Linear Models. While inheriti...... hiện toàn bộ
Integrating Quantitative and Qualitative Discovery: The ABACUS System
Machine Learning - Tập 1 - Trang 367-401 - 1986
Most research on inductive learning has been concerned with qualitative learning that induces conceptual, logic-style descriptions from the given facts. In contrast, quantitative learning deals with discovering numerical laws characterizing empirical data. This research attempts to integrate both types of learning by combining newly developed heuristics for formulating equations with the previousl...... hiện toàn bộ
Asymptotic accuracy of Bayes estimation for latent variables with redundancy
Machine Learning - Tập 102 - Trang 1-28 - 2015
Hierarchical parametric models consisting of observable and latent variables are widely used for unsupervised learning tasks. For example, a mixture model is a representative hierarchical model for clustering. From the statistical point of view, the models can be regular or singular due to the distribution of data. In the regular case, the models have the identifiability; there is one-to-one relat...... hiện toàn bộ
Learning (predictive) risk scores in the presence of censoring due to interventions
Machine Learning - Tập 102 - Trang 323-348 - 2015
A large and diverse set of measurements are regularly collected during a patient’s hospital stay to monitor their health status. Tools for integrating these measurements into severity scores, that accurately track changes in illness severity, can improve clinicians’ ability to provide timely interventions. Existing approaches for creating such scores either (1) rely on experts to fully specify the...... hiện toàn bộ
Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations
Machine Learning - Tập 112 - Trang 2263-2296 - 2022
In this paper, we define a novel inverse reinforcement learning (IRL) problem where the demonstrations are multi-intention, i.e., collected from multi-intention experts, unlabeled, i.e., without intention labels, and partially overlapping, i.e., shared between multiple intentions. In the presence of overlapping demonstrations, current IRL methods, developed to handle multi-intention and unlabeled ...... hiện toàn bộ
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