Saved in:
| Main Authors: | Wang, Ruigang, Dvijotham, Krishnamurthy, Manchester, Ian R. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.01344 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Norm-Bounded Low-Rank Adaptation
by: Wang, Ruigang, et al.
Published: (2025)
by: Wang, Ruigang, et al.
Published: (2025)
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
by: Barbara, Nicholas H., et al.
Published: (2024)
by: Barbara, Nicholas H., et al.
Published: (2024)
R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
by: Barbara, Nicholas H., et al.
Published: (2025)
by: Barbara, Nicholas H., et al.
Published: (2025)
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
by: Pauli, Patricia, et al.
Published: (2024)
by: Pauli, Patricia, et al.
Published: (2024)
Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
by: Zhang, Yurui, et al.
Published: (2025)
by: Zhang, Yurui, et al.
Published: (2025)
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
by: Pauli, Patricia, et al.
Published: (2023)
by: Pauli, Patricia, et al.
Published: (2023)
Learning Stable and Passive Neural Differential Equations
by: Cheng, Jing, et al.
Published: (2024)
by: Cheng, Jing, et al.
Published: (2024)
Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
by: Shi, Kanghong, et al.
Published: (2025)
by: Shi, Kanghong, et al.
Published: (2025)
React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
by: Barbara, Nicholas H., et al.
Published: (2025)
by: Barbara, Nicholas H., et al.
Published: (2025)
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
by: Barbara, Nicholas H., et al.
Published: (2023)
by: Barbara, Nicholas H., et al.
Published: (2023)
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition
by: Bai, Yunyan, et al.
Published: (2024)
by: Bai, Yunyan, et al.
Published: (2024)
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
by: Chen, Lesi, et al.
Published: (2023)
by: Chen, Lesi, et al.
Published: (2023)
From Sublinear to Linear: Local Convergence in Finite-Width Networks via Locally Polyak-Lojasiewicz Regions
by: Aich, Agnideep, et al.
Published: (2025)
by: Aich, Agnideep, et al.
Published: (2025)
Achieving the Tightest Relaxation of Sigmoids for Formal Verification
by: Chevalier, Samuel, et al.
Published: (2024)
by: Chevalier, Samuel, et al.
Published: (2024)
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
by: Kar, Avik, et al.
Published: (2026)
by: Kar, Avik, et al.
Published: (2026)
A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
by: Xu, Ziqing, et al.
Published: (2025)
by: Xu, Ziqing, et al.
Published: (2025)
Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality
by: Kassing, Sebastian, et al.
Published: (2024)
by: Kassing, Sebastian, et al.
Published: (2024)
Provably Bounding Neural Network Preimages
by: Kotha, Suhas, et al.
Published: (2023)
by: Kotha, Suhas, et al.
Published: (2023)
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning
by: Bose, Avinandan, et al.
Published: (2025)
by: Bose, Avinandan, et al.
Published: (2025)
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
by: Xia, Lu, et al.
Published: (2023)
by: Xia, Lu, et al.
Published: (2023)
Poincaré Inequality for Local Log-Polyak-Lojasiewicz Measures : Non-asymptotic Analysis in Low-temperature Regime
by: Gong, Yun, et al.
Published: (2025)
by: Gong, Yun, et al.
Published: (2025)
Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
by: de Oliveira, Arthur C. B., et al.
Published: (2026)
by: de Oliveira, Arthur C. B., et al.
Published: (2026)
Training-Free Imitation Learning with Closed-Form Diffusion Policies
by: Mishra, Raghav, et al.
Published: (2026)
by: Mishra, Raghav, et al.
Published: (2026)
Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)
by: Eiras, Francisco, et al.
Published: (2023)
Large Language Models Can Verbatim Reproduce Long Malicious Sequences
by: Lin, Sharon, et al.
Published: (2025)
by: Lin, Sharon, et al.
Published: (2025)
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
by: Brown, Gavin, et al.
Published: (2024)
by: Brown, Gavin, et al.
Published: (2024)
Verified Neural Compressed Sensing
by: Bunel, Rudy, et al.
Published: (2024)
by: Bunel, Rudy, et al.
Published: (2024)
Beyond Lipschitz Continuity and Monotonicity: Fractal and Chaotic Activation Functions in Echo State Networks
by: Chipera, Rae, et al.
Published: (2025)
by: Chipera, Rae, et al.
Published: (2025)
Bi-Lipschitz Autoencoder With Injectivity Guarantee
by: Zhan, Qipeng, et al.
Published: (2026)
by: Zhan, Qipeng, et al.
Published: (2026)
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
by: Liu, Dechuan, et al.
Published: (2026)
by: Liu, Dechuan, et al.
Published: (2026)
Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models
by: Kim, Kyuyoung, et al.
Published: (2024)
by: Kim, Kyuyoung, et al.
Published: (2024)
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
by: Martin, Andrea, et al.
Published: (2025)
by: Martin, Andrea, et al.
Published: (2025)
Expressive Losses for Verified Robustness via Convex Combinations
by: De Palma, Alessandro, et al.
Published: (2023)
by: De Palma, Alessandro, et al.
Published: (2023)
Learning to Receive Help: Intervention-Aware Concept Embedding Models
by: Zarlenga, Mateo Espinosa, et al.
Published: (2023)
by: Zarlenga, Mateo Espinosa, et al.
Published: (2023)
Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion
by: Shpilevskiy, Frederick, et al.
Published: (2025)
by: Shpilevskiy, Frederick, et al.
Published: (2025)
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
by: Dvijotham, Krishnamurthy, et al.
Published: (2024)
by: Dvijotham, Krishnamurthy, et al.
Published: (2024)
Ruppert-Polyak averaging for Stochastic Order Oracle
by: Smirnov, V. N., et al.
Published: (2024)
by: Smirnov, V. N., et al.
Published: (2024)
FSW-GNN: A Bi-Lipschitz WL-Equivalent Graph Neural Network
by: Sverdlov, Yonatan, et al.
Published: (2024)
by: Sverdlov, Yonatan, et al.
Published: (2024)
Learning Stable and Robust Linear Parameter-Varying State-Space Models
by: Verhoek, Chris, et al.
Published: (2023)
by: Verhoek, Chris, et al.
Published: (2023)
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)
Similar Items
-
Norm-Bounded Low-Rank Adaptation
by: Wang, Ruigang, et al.
Published: (2025) -
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
by: Barbara, Nicholas H., et al.
Published: (2024) -
R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
by: Barbara, Nicholas H., et al.
Published: (2025) -
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
by: Pauli, Patricia, et al.
Published: (2024) -
Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
by: Zhang, Yurui, et al.
Published: (2025)