Imagen de Portada

Matrix Sensing with Kernel Optimal Loss: Robustness and Optimization Landscape

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Song, Xinyuan, Ma, Ziye
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

There is a Singularity in the Loss Landscape
por: Lowell, Mark
Publicado: (2022)

Robust Model-Based Optimization for Challenging Fitness Landscapes
por: Ghaffari, Saba, et al.
Publicado: (2023)

Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
por: Chen, Jiaqing, et al.
Publicado: (2026)

Visualizing Loss Functions as Topological Landscape Profiles
por: Geniesse, Caleb, et al.
Publicado: (2024)

On Symmetric Losses for Robust Policy Optimization with Noisy Preferences
por: Nishimori, Soichiro, et al.
Publicado: (2025)

Model Merging on Loss Landscape: A Geometry Perspective
por: Lu, Juanwu, et al.
Publicado: (2026)

Evaluating Loss Landscapes from a Topology Perspective
por: Xie, Tiankai, et al.
Publicado: (2024)

Early-Warning Signals of Grokking via Loss-Landscape Geometry
por: Xu, Yongzhong
Publicado: (2026)

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
por: Sun, Xudong, et al.
Publicado: (2024)

Fractal Landscapes in Policy Optimization
por: Wang, Tao, et al.
Publicado: (2023)

Identifying Sparsely Active Circuits Through Local Loss Landscape Decomposition
por: Chrisman, Brianna, et al.
Publicado: (2025)

A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models
por: Xie, YuQing, et al.
Publicado: (2025)

Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing
por: Abdennadher, Yesmine, et al.
Publicado: (2026)

Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
por: Yun, Taeyoung, et al.
Publicado: (2026)

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization
por: Lange, Robert Tjarko, et al.
Publicado: (2025)

Kernelized Reinforcement Learning with Order Optimal Regret Bounds
por: Vakili, Sattar, et al.
Publicado: (2023)

The Epochal Sawtooth Phenomenon: Unveiling Training Loss Oscillations in Adam and Other Optimizers
por: Liu, Qi, et al.
Publicado: (2024)

Loss Landscape Degeneracy and Stagewise Development in Transformers
por: Hoogland, Jesse, et al.
Publicado: (2024)

Manifold Approximation leads to Robust Kernel Alignment
por: Islam, Mohammad Tariqul, et al.
Publicado: (2025)

Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
por: Li, Shuyao, et al.
Publicado: (2024)

StructPrune: Structured Global Pruning asymptotics with $\mathcal{O}(\sqrt{N})$ GPU Memory
por: Song, Xinyuan, et al.
Publicado: (2025)

Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models
por: Mahmood, Khawir, et al.
Publicado: (2024)

Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
por: Liu, Yurong, et al.
Publicado: (2024)

Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL
por: You, Jiahao, et al.
Publicado: (2025)

Flat Channels to Infinity in Neural Loss Landscapes
por: Martinelli, Flavio, et al.
Publicado: (2025)

Adapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning
por: Liu, Jingyi, et al.
Publicado: (2026)

KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Bandits
por: Ran, Dezhi, et al.
Publicado: (2025)

Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
por: Barannikov, Serguei, et al.
Publicado: (2020)

Visualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms
por: Liu, Jingyi, et al.
Publicado: (2026)

A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study
por: Liu, Jingyi, et al.
Publicado: (2026)

Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning
por: Zhang, Dingwen, et al.
Publicado: (2024)

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric
por: Harvey, Thomas R.
Publicado: (2025)

Finding One's Bearings in the Hyperparameter Landscape of a Wide-Kernel Convolutional Fault Detector
por: Hudson, Dan, et al.
Publicado: (2024)

Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck Equation
por: Yin, Shuyu, et al.
Publicado: (2024)

PhySense: Sensor Placement Optimization for Accurate Physics Sensing
por: Ma, Yuezhou, et al.
Publicado: (2025)

Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
por: Ahmed, Waqas, et al.
Publicado: (2026)

Matrix Low-Rank Trust Region Policy Optimization
por: Rozada, Sergio, et al.
Publicado: (2024)

Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds
por: Li, Yibang, et al.
Publicado: (2026)

Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
por: Suwandi, Richard Cornelius, et al.
Publicado: (2025)

GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization
por: Khan, Zaid, et al.
Publicado: (2026)