arXiv Machine Learning
Jul 14
Manifold Constrained Tabular Deep Neural Networks
arXiv:2607. 09710v1 Announce Type: new Abstract: Tabular classification is often governed by local, condition-triggered rules rather than smooth global patterns.
DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
arXiv:2605. 30456v2 Announce Type: replace Abstract: Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches.
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arXiv Machine Learning
Jun 15
Neural Slack Variables for Shape Constraints
arXiv:2606. 13803v1 Announce Type: new Abstract: Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications.
arXiv Machine Learning
Jul 7
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
arXiv:2603. 07606v2 Announce Type: replace Abstract: Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust.
arXiv Machine Learning
Jun 5
DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables
arXiv:2606. 05247v1 Announce Type: new Abstract: Enforcing nonlinear inequality constraints in neural networks remains challenging, especially when the output is subject to many coupled constraints.
arXiv Machine Learning
Jul 1
ShardNet: Training Neural Controllers with Hard, Non-Convex Constraints
arXiv:2606. 30935v1 Announce Type: cross Abstract: While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints.
arXiv Machine Learning
Aug 11
Input convex neural networks as surrogates in mathematical optimisation
arXiv:2608. 09707v1 Announce Type: cross Abstract: Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research.
arXiv AI
Jun 2
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
arXiv:2605. 04193v2 Announce Type: replace Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings.
arXiv AI
1d ago
Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration
arXiv:2608. 14569v1 Announce Type: new Abstract: Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental limitation persistent constraint violations occur under distribution shifts even when the model reports high confidence.
arXiv Machine Learning
Aug 4
Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions
arXiv:2602. 06737v2 Announce Type: replace Abstract: We present a generalized framework for the range verification of neural networks featuring non-linear activation functions.
arXiv AI
Jul 24
Differentiable Logic Programming to Mitigate Reasoning Shortcuts in Neurosymbolic Systems
arXiv:2607. 21185v1 Announce Type: new Abstract: Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has shown they are susceptible to shortcut reasoning behaviors.
arXiv Machine Learning
Aug 11
Generalized Convexity and Smoothness via Conjugate Duality: Optimization Theory for Deep Neural Networks
arXiv:2608. 09523v1 Announce Type: new Abstract: Deep neural network (DNN) training with stochastic gradient descent (SGD) and its variants achieves strong empirical performance, yet classical optimization theory does not fully explain this success.
arXiv Machine Learning
Aug 4
Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection
arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.