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Found 18 papers, 7 papers with code
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data.
Symbolic Regression is NP-hard
Symbolic regression (SR) is the task of learning a model of data in the form of a mathematical expression.
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
We find that coefficient mutation can help re-discovering the underlying equation by a substantial amount, but only when no noise is added to the target variable.
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications.
On genetic programming representations and fitness functions for interpretable dimensionality reduction
We find that various GP methods can be competitive with state-of-the-art DR algorithms and that they have the potential to produce interpretable DR mappings.
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models.
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning
Our results indicate that adult data can be considered to be a meaningful augmentation of pediatric data for the recognition of emotional facial expression in children, and open up the possibility for other applications of contrastive learning to improve pediatric care by complementing data of children with that of adults.
Conversational Agents: Theory and Applications
In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that generally engage in discussions intended to reach one or several specific goals, often (but not always) within a specific domain.
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations
Since CEs typically prescribe a sparse form of intervention (i. e., only a subset of the features should be changed), we study the effect of addressing robustness separately for the features that are recommended to be changed and those that are not.
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i. e., dependencies between variables, can be key.
The five Is: Key principles for interpretable and safe conversational AI
In this position paper, we present five key principles, namely interpretability, inherent capability to explain, independent data, interactive learning, and inquisitiveness, for the development of conversational AI that, unlike the currently popular black box approaches, is transparent and accountable.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Model Learning with Personalized Interpretability Estimation (ML-PIE)
The latter is estimated by a neural network that is trained concurrently to the evolution using the feedback of the user, which is collected using uncertainty-based active learning.
Genetic Programming is Naturally Suited to Evolve Bagging Ensembles
Experimental comparisons on classification and regression tasks taken and reproduced from prior studies show that our algorithm fares very well against state-of-the-art ensemble and non-ensemble GP algorithms.
Learning a Formula of Interpretability to Learn Interpretable Formulas
We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability.
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
To assess the effects of radiation therapy, treatment plans are typically simulated on phantoms, i. e., virtual surrogates of patient anatomy.
On Explaining Machine Learning Models by Evolving Crucial and Compact Features
In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm.
Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions
We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this.
