Derives stability-based generalization bounds for nonlinear least squares depending on trained Jacobian geometry rather than parameter count.
Title resolution pending
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 2
dataset 1
citation-polarity summary
representative citing papers
Generative optimization of quantum embedding circuits improves supervised classification on some datasets, with derived bounds showing performance saturation governed by Wasserstein distance of the classical input data.
All tested counterfactual explanation methods struggle to generate valid counterfactuals on incomplete inputs, though robust variants outperform non-robust ones.
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
LoH adds a learnable choice operator to propositional logic, compiles formulas to differentiable graphs via fuzzy logic, subsumes prior NeSy models, and supports discretization to Boolean functions via the Gödel trick.
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
Venn-Abers predictors are extended to unbounded regression via conformal prediction, producing point regressors that modestly improve efficiency over standard methods for large datasets.
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
citing papers explorer
-
Generalization in Nonlinear Least Squares via Learned Feature Geometry
Derives stability-based generalization bounds for nonlinear least squares depending on trained Jacobian geometry rather than parameter count.
-
Generative Quantum Data Embeddings for Supervised Learning
Generative optimization of quantum embedding circuits improves supervised classification on some datasets, with derived bounds showing performance saturation governed by Wasserstein distance of the classical input data.
-
Evaluating Counterfactual Explanation Methods on Incomplete Inputs
All tested counterfactual explanation methods struggle to generate valid counterfactuals on incomplete inputs, though robust variants outperform non-robust ones.
-
From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
-
Logic of Hypotheses: from Zero to Full Knowledge in Neurosymbolic Integration
LoH adds a learnable choice operator to propositional logic, compiles formulas to differentiable graphs via fuzzy logic, subsumes prior NeSy models, and supports discretization to Boolean functions via the Gödel trick.
-
A Composite Activation Function for Learning Stable Binary Representations
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
-
Inductive Venn-Abers and related regressors
Venn-Abers predictors are extended to unbounded regression via conformal prediction, producing point regressors that modestly improve efficiency over standard methods for large datasets.
-
CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
- Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification