Saved in:
| Main Authors: | Zozoulenko, Nikita, Cass, Thomas, Gonon, Lukas |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
by: Zozoulenko, Nikita, et al.
Published: (2024)
by: Zozoulenko, Nikita, et al.
Published: (2024)
Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026)
by: Zozoulenko, Nikita, et al.
Published: (2026)
Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026)
by: Mehmood, Muhammed Ali, et al.
Published: (2026)
Random Controlled Differential Equations
by: Piatti, Francesco, et al.
Published: (2025)
by: Piatti, Francesco, et al.
Published: (2025)
Distributional Adversarial Attacks and Training in Deep Hedging
by: He, Guangyi, et al.
Published: (2025)
by: He, Guangyi, et al.
Published: (2025)
Universal randomised signatures for generative time series modelling
by: Biagini, Francesca, et al.
Published: (2024)
by: Biagini, Francesca, et al.
Published: (2024)
Reservoir kernels and Volterra series
by: Gonon, Lukas, et al.
Published: (2022)
by: Gonon, Lukas, et al.
Published: (2022)
Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs
by: Gonon, Lukas, et al.
Published: (2023)
by: Gonon, Lukas, et al.
Published: (2023)
Feedback-driven recurrent quantum neural network universality
by: Gonon, Lukas, et al.
Published: (2025)
by: Gonon, Lukas, et al.
Published: (2025)
Fast Deep Hedging with Second-Order Optimization
by: Mueller, Konrad, et al.
Published: (2024)
by: Mueller, Konrad, et al.
Published: (2024)
Lecture notes on rough paths and applications to machine learning
by: Cass, Thomas, et al.
Published: (2024)
by: Cass, Thomas, et al.
Published: (2024)
Gaussian Match-and-Copy: A Minimalist Benchmark for Studying Transformer Induction
by: Gonon, Antoine, et al.
Published: (2026)
by: Gonon, Antoine, et al.
Published: (2026)
Computing Systemic Risk Measures with Graph Neural Networks
by: Gonon, Lukas, et al.
Published: (2024)
by: Gonon, Lukas, et al.
Published: (2024)
All Random Features Representations are Equivalent
by: Sernau, Luke, et al.
Published: (2024)
by: Sernau, Luke, et al.
Published: (2024)
Symmetry-Aware Bayesian Optimization via Max Kernels
by: Bardou, Anthony, et al.
Published: (2025)
by: Bardou, Anthony, et al.
Published: (2025)
Non-Vacuous Generalization Bounds: Can Rescaling Invariances Help?
by: Rouchouse, Damien, et al.
Published: (2025)
by: Rouchouse, Damien, et al.
Published: (2025)
A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations
by: Gonon, Antoine, et al.
Published: (2024)
by: Gonon, Antoine, et al.
Published: (2024)
Insights on Muon from Simple Quadratics
by: Gonon, Antoine, et al.
Published: (2026)
by: Gonon, Antoine, et al.
Published: (2026)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)
by: Gonon, Lukas, et al.
Published: (2024)
Fast Inference with Kronecker-Sparse Matrices
by: Gonon, Antoine, et al.
Published: (2024)
by: Gonon, Antoine, et al.
Published: (2024)
Numerical Schemes for Signature Kernels
by: Cass, Thomas, et al.
Published: (2025)
by: Cass, Thomas, et al.
Published: (2025)
SWING: Unlocking Implicit Graph Representations for Graph Random Features
by: Manenti, Alessandro, et al.
Published: (2026)
by: Manenti, Alessandro, et al.
Published: (2026)
A path-norm toolkit for modern networks: consequences, promises and challenges
by: Gonon, Antoine, et al.
Published: (2023)
by: Gonon, Antoine, et al.
Published: (2023)
Support Before Frequency in Discrete Diffusion
by: Müller, Adrian, et al.
Published: (2026)
by: Müller, Adrian, et al.
Published: (2026)
Teach Old SAEs New Domain Tricks with Boosting
by: Koriagin, Nikita, et al.
Published: (2025)
by: Koriagin, Nikita, et al.
Published: (2025)
Mechanistic Permutability: Match Features Across Layers
by: Balagansky, Nikita, et al.
Published: (2024)
by: Balagansky, Nikita, et al.
Published: (2024)
Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
by: Blesch, Kristin, et al.
Published: (2025)
by: Blesch, Kristin, et al.
Published: (2025)
Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
by: Schuchardt, Jan, et al.
Published: (2026)
by: Schuchardt, Jan, et al.
Published: (2026)
Contextualized Messages Boost Graph Representations
by: Lim, Brian Godwin, et al.
Published: (2024)
by: Lim, Brian Godwin, et al.
Published: (2024)
Manifold Random Features
by: Parashar, Ananya, et al.
Published: (2026)
by: Parashar, Ananya, et al.
Published: (2026)
RUMBoost: Gradient Boosted Random Utility Models
by: Salvadé, Nicolas, et al.
Published: (2024)
by: Salvadé, Nicolas, et al.
Published: (2024)
Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
by: Sukhorukov, Nikita, et al.
Published: (2025)
by: Sukhorukov, Nikita, et al.
Published: (2025)
On the Impact of Performative Risk Minimization for Binary Random Variables
by: Tsoy, Nikita, et al.
Published: (2025)
by: Tsoy, Nikita, et al.
Published: (2025)
Fast Calculation of Feature Contributions in Boosting Trees
by: Jiang, Zhongli, et al.
Published: (2024)
by: Jiang, Zhongli, et al.
Published: (2024)
Invertible Kernel PCA with Random Fourier Features
by: Gedon, Daniel, et al.
Published: (2023)
by: Gedon, Daniel, et al.
Published: (2023)
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
by: Alkin, Benedikt, et al.
Published: (2024)
by: Alkin, Benedikt, et al.
Published: (2024)
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
by: da Cunha, Arthur, et al.
Published: (2024)
by: da Cunha, Arthur, et al.
Published: (2024)
General Graph Random Features
by: Reid, Isaac, et al.
Published: (2023)
by: Reid, Isaac, et al.
Published: (2023)
Stein Random Feature Regression
by: Warren, Houston, et al.
Published: (2024)
by: Warren, Houston, et al.
Published: (2024)
HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
by: Bhedasgaonkar, Nikita, et al.
Published: (2025)
by: Bhedasgaonkar, Nikita, et al.
Published: (2025)
Similar Items
-
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
by: Zozoulenko, Nikita, et al.
Published: (2024) -
Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026) -
Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026) -
Random Controlled Differential Equations
by: Piatti, Francesco, et al.
Published: (2025) -
Distributional Adversarial Attacks and Training in Deep Hedging
by: He, Guangyi, et al.
Published: (2025)