Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical a…
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17 results for “epistemology”
The paper clarifies conditions for using benchmark scores in machine learning.
problem Using benchmark scores to draw scientific inferences about learning problems.
method Developing conditions of construct validity inspired by psychological measurement theory.
result Clarifies conditions under which benchmark scores support diverse scientific claims.
Paper bridges AI/ML and causal modeling to reduce bias.
problem Difficulty in combining methods from different assumptions.
method Integrates system dynamics and structural equation modeling.
result Unified mathematical framework for AI/ML and causal modeling.
Machine learning's data-centric philosophy conflicts with natural sciences' standards.
problem Conflict between machine learning's ontology and epistemology and natural sciences' practices.
method Identifying and analyzing contexts where ML can be beneficial or harmful in natural sciences.
result ML can enhance trustworthiness in causal inference but introduces biases in emulation and labeling.
In a context where most published articles are devoted to the development of "new methods", comparison studies are generally appreciated by readers but surprisingly given poor consideration by many scientific journals. In connection with recent articles on over-optimism and epistemology published in Bioinformatics, thi…
We define the information threshold in Bayesian decision-making.
problem Understanding the optimal amount of information for reliable classification.
method Defining the information threshold as the point of maximum curvature in the prior vs. posterior curve.
result At the information threshold, additional evidence does not significantly improve posterior probability.
This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…
Interdisciplinarity in Socio-economics, mathematical analysis and predictability of complex systemsphysics.soc-ph
In this essay, I attempt to provide supporting evidence as well as some balance for the thesis on `Transforming socio-economics with a new epistemology' presented by Hollingworth and Mueller (2008). First, I review a personal highlight of my own scientific path that illustrates the power of interdisciplinarity as well …
NECO detects out-of-distribution data using neural collapse properties.
problem Detecting out-of-distribution data in machine learning models.
method NECO leverages neural collapse geometric properties to identify OOD data.
result NECO achieves state-of-the-art results on OOD detection tasks.
The paper introduces metrics to objectively evaluate interpretability methods.
problem Lack of objective evaluation metrics for interpretability methods.
method Proposes a set of metrics to evaluate interpretability methods along simplicity and broadness.
result Validated metrics on different benchmark tasks and showed their utility in method selection.
The concept of progress has characterized human society from millennia. However, this concept is elusive and too often given for certain. The goal of this paper is to suggest a general definition of human progress that satisfies, whenever possible the conditions of independence, generality, epistemological applicabilit…
The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology, articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we account for our apparent ability to make decisions on the basis only of what is relevant to an ongoing situation without having explicitly to consider all that is not …
Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behaviour, and time. This work can be seen not only as a collection of architectural proposals, but also as the beginnings of a theory of machine k…
Defines Learning Analytics' foundational structure and scope.
problem Lack of theoretical foundation in Learning Analytics.
method Proposes an axiomatic theory based on psychological learning and LA methodology.
result Clarifies the epistemological stance of Learning Analytics and its limitations.
Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applicationsstat.OT
The book examines statistical issues with fat-tailed distributions and proposes remedies.
problem Misapplication of conventional statistical techniques to fat-tailed distributions.
method Investigates the limitations of traditional asymptotics and proposes remedies.
result Traditional statistical techniques often fail when applied to fat-tailed distributions.
This dissertation tackles challenges in reliable machine learning measurement.
problem Challenges in reproducibility, scalability, and uncertainty quantification in machine learning.
method Develops criteria for meaningful metrics and methodologies for scalable, reliable measurement.
result Provides methods for evaluating generative-AI systems and quantifying memorization.
New approach links machine learning reliability to epistemic uncertainty.
problem Characterize and quantify reliability of machine learning predictions.
method Extend JTB theory to neural networks, linking prediction reliability to support characteristics.
result Demonstrates reliability for individual predictions and identifies regions of uncertainty.