SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Francobaldi, Matteo, Lombardi, Michele, Lodi, Andrea |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SMLE: Safe Machine Learning via Embedded Overapproximation
by: Francobaldi, Matteo, et al.
Published: (2024)
by: Francobaldi, Matteo, et al.
Published: (2024)
An improved column-generation-based matheuristic for learning classification trees
by: Patel, Krunal Kishor, et al.
Published: (2023)
by: Patel, Krunal Kishor, et al.
Published: (2023)
Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
by: Liu, Xin, et al.
Published: (2022)
by: Liu, Xin, et al.
Published: (2022)
Training Safe Neural Networks with Global SDP Bounds
by: Soletskyi, Roman, et al.
Published: (2024)
by: Soletskyi, Roman, et al.
Published: (2024)
On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
by: de Oliveira, Arthur Castello Branco, et al.
Published: (2025)
by: de Oliveira, Arthur Castello Branco, et al.
Published: (2025)
Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
by: Tsukamoto, Hiroyasu, et al.
Published: (2022)
by: Tsukamoto, Hiroyasu, et al.
Published: (2022)
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
by: Chen, Hao, et al.
Published: (2026)
by: Chen, Hao, et al.
Published: (2026)
Provable Acceleration for Diffusion Models under Minimal Assumptions
by: Li, Gen, et al.
Published: (2024)
by: Li, Gen, et al.
Published: (2024)
Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
by: Huang, Yu, et al.
Published: (2025)
by: Huang, Yu, et al.
Published: (2025)
Agentic Transformers Provably Learn to Search via Reinforcement Learning
by: Yang, Tong, et al.
Published: (2026)
by: Yang, Tong, et al.
Published: (2026)
Provably Safe Generative Sampling with Constricting Barrier Functions
by: Gadginmath, Darshan, et al.
Published: (2026)
by: Gadginmath, Darshan, et al.
Published: (2026)
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
by: Li, Zihao, et al.
Published: (2024)
by: Li, Zihao, et al.
Published: (2024)
Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $μ$P Parametrization
by: Chen, Zixiang, et al.
Published: (2025)
by: Chen, Zixiang, et al.
Published: (2025)
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
by: Xiao, Minheng, et al.
Published: (2024)
by: Xiao, Minheng, et al.
Published: (2024)
Provable Offline Reinforcement Learning for Structured Cyclic MDPs
by: Lee, Kyungbok, et al.
Published: (2026)
by: Lee, Kyungbok, et al.
Published: (2026)
Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
by: Kiyani, Elham, et al.
Published: (2025)
by: Kiyani, Elham, et al.
Published: (2025)
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
by: Onorato, Gabriele
Published: (2024)
by: Onorato, Gabriele
Published: (2024)
Applications of 0-1 Neural Networks in Prescription and Prediction
by: Patil, Vrishabh, et al.
Published: (2024)
by: Patil, Vrishabh, et al.
Published: (2024)
Taming Binarized Neural Networks and Mixed-Integer Programs
by: Aspman, Johannes, et al.
Published: (2023)
by: Aspman, Johannes, et al.
Published: (2023)
Feed-Forward Neural Networks as a Mixed-Integer Program
by: Aftabi, Navid, et al.
Published: (2024)
by: Aftabi, Navid, et al.
Published: (2024)
Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
by: Pacheco, Bruno Machado, et al.
Published: (2023)
by: Pacheco, Bruno Machado, et al.
Published: (2023)
Ginger: An Efficient Curvature Approximation with Linear Complexity for General Neural Networks
by: Hao, Yongchang, et al.
Published: (2024)
by: Hao, Yongchang, et al.
Published: (2024)
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)
by: Tucat, Matteo, et al.
Published: (2024)
A Convexity-dependent Two-Phase Training Algorithm for Deep Neural Networks
by: Hrycej, Tomas, et al.
Published: (2025)
by: Hrycej, Tomas, et al.
Published: (2025)
An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks
by: Ke, Zhifa, et al.
Published: (2024)
by: Ke, Zhifa, et al.
Published: (2024)
The Differentiable Feasibility Pump
by: Cacciola, Matteo, et al.
Published: (2024)
by: Cacciola, Matteo, et al.
Published: (2024)
Accelerating Convergence of Score-Based Diffusion Models, Provably
by: Li, Gen, et al.
Published: (2024)
by: Li, Gen, et al.
Published: (2024)
A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks
by: Cayci, Semih
Published: (2024)
by: Cayci, Semih
Published: (2024)
Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
by: Li, Haoyu, et al.
Published: (2026)
by: Li, Haoyu, et al.
Published: (2026)
T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method
by: Zhu, Haoyu, et al.
Published: (2025)
by: Zhu, Haoyu, et al.
Published: (2025)
Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning
by: Li, Hongpei, et al.
Published: (2024)
by: Li, Hongpei, et al.
Published: (2024)
Distributionally Robust Safety Verification of Neural Networks via Worst-Case CVaR
by: Kishida, Masako
Published: (2025)
by: Kishida, Masako
Published: (2025)
Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
by: Islam, MD Shafikul, et al.
Published: (2023)
by: Islam, MD Shafikul, et al.
Published: (2023)
When and How Unlabeled Data Provably Improve In-Context Learning
by: Li, Yingcong, et al.
Published: (2025)
by: Li, Yingcong, et al.
Published: (2025)
How hard is learning to cut? Trade-offs and sample complexity
by: Khalife, Sammy, et al.
Published: (2025)
by: Khalife, Sammy, et al.
Published: (2025)
From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
by: Liu, Jiachang, et al.
Published: (2026)
by: Liu, Jiachang, et al.
Published: (2026)
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
by: Chen, Siyu, et al.
Published: (2024)
by: Chen, Siyu, et al.
Published: (2024)
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
by: Choquette-Choo, Christopher A., et al.
Published: (2023)
by: Choquette-Choo, Christopher A., et al.
Published: (2023)
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
by: Ganesh, Swetha, et al.
Published: (2024)
by: Ganesh, Swetha, et al.
Published: (2024)
Neural Solver Selection for Combinatorial Optimization
by: Gao, Chengrui, et al.
Published: (2024)
by: Gao, Chengrui, et al.
Published: (2024)
Similar Items
-
SMLE: Safe Machine Learning via Embedded Overapproximation
by: Francobaldi, Matteo, et al.
Published: (2024) -
An improved column-generation-based matheuristic for learning classification trees
by: Patel, Krunal Kishor, et al.
Published: (2023) -
Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
by: Liu, Xin, et al.
Published: (2022) -
Training Safe Neural Networks with Global SDP Bounds
by: Soletskyi, Roman, et al.
Published: (2024) -
On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
by: de Oliveira, Arthur Castello Branco, et al.
Published: (2025)