A Taxonomy of Loss Functions for Stochastic Optimal Control
Fuente:
arXiv
Saved in:
| Main Author: | Domingo-Enrich, Carles |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2026)
by: Domingo-Enrich, Carles, et al.
Published: (2026)
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2024)
by: Domingo-Enrich, Carles, et al.
Published: (2024)
Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023)
by: Domingo-Enrich, Carles, et al.
Published: (2023)
Rare Event Analysis via Stochastic Optimal Control
by: Du, Yuanqi, et al.
Published: (2026)
by: Du, Yuanqi, et al.
Published: (2026)
A unified perspective on fine-tuning and sampling with diffusion and flow models
by: Domingo-Enrich, Carles, et al.
Published: (2026)
by: Domingo-Enrich, Carles, et al.
Published: (2026)
A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
by: Claeys, Louis, et al.
Published: (2026)
by: Claeys, Louis, et al.
Published: (2026)
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024)
by: Hua, Mengjian, et al.
Published: (2024)
On Policy Stochasticity in Mutual Information Optimal Control of Linear Systems
by: Enami, Shoju, et al.
Published: (2025)
by: Enami, Shoju, et al.
Published: (2025)
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
by: Han, Yinbin, et al.
Published: (2024)
by: Han, Yinbin, et al.
Published: (2024)
A Convex Loss Function for Set Prediction with Optimal Trade-offs Between Size and Conditional Coverage
by: Bach, Francis
Published: (2025)
by: Bach, Francis
Published: (2025)
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
by: Hoischen, Nicolas, et al.
Published: (2024)
by: Hoischen, Nicolas, et al.
Published: (2024)
MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
by: Zhu, Yuchen, et al.
Published: (2025)
by: Zhu, Yuchen, et al.
Published: (2025)
Optimal Rates for Robust Stochastic Convex Optimization
by: Gao, Changyu, et al.
Published: (2024)
by: Gao, Changyu, et al.
Published: (2024)
Optimal Algorithms for Stochastic Complementary Composite Minimization
by: d'Aspremont, Alexandre, et al.
Published: (2022)
by: d'Aspremont, Alexandre, et al.
Published: (2022)
Stochastic Approximation with Block Coordinate Optimal Stepsizes
by: Jiang, Tao, et al.
Published: (2025)
by: Jiang, Tao, et al.
Published: (2025)
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
by: Im, Hyungki, et al.
Published: (2025)
by: Im, Hyungki, et al.
Published: (2025)
Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
by: Ji, Yao, et al.
Published: (2026)
by: Ji, Yao, et al.
Published: (2026)
A Novel Loss Function-based Support Vector Machine for Binary Classification
by: Li, Yan, et al.
Published: (2024)
by: Li, Yan, et al.
Published: (2024)
Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise
by: Wang, Menglian, et al.
Published: (2026)
by: Wang, Menglian, et al.
Published: (2026)
Optimal Asynchronous Stochastic Nonconvex Optimization under Heavy-Tailed Noise
by: Wu, Yidong, et al.
Published: (2026)
by: Wu, Yidong, et al.
Published: (2026)
Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
by: Shi, Qiankun, et al.
Published: (2025)
by: Shi, Qiankun, et al.
Published: (2025)
General Loss Functions Lead to (Approximate) Interpolation in High Dimensions
by: Lai, Kuo-Wei, et al.
Published: (2023)
by: Lai, Kuo-Wei, et al.
Published: (2023)
Functional Central Limit Theorem for Stochastic Gradient Descent
by: Flamand, Kessang, et al.
Published: (2026)
by: Flamand, Kessang, et al.
Published: (2026)
Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
by: Zheng, Yuping, et al.
Published: (2025)
by: Zheng, Yuping, et al.
Published: (2025)
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
by: Gong, Xiaochuan, et al.
Published: (2024)
by: Gong, Xiaochuan, et al.
Published: (2024)
SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
by: Chu, Tianshu, et al.
Published: (2024)
by: Chu, Tianshu, et al.
Published: (2024)
A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives
by: Nguyen, Ti Ti, et al.
Published: (2025)
by: Nguyen, Ti Ti, et al.
Published: (2025)
Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming
by: Lu, Haihao, et al.
Published: (2021)
by: Lu, Haihao, et al.
Published: (2021)
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization
by: Kornowski, Guy, et al.
Published: (2023)
by: Kornowski, Guy, et al.
Published: (2023)
Lower Bounds on Adversarial Robustness for Multiclass Classification with General Loss Functions
by: Trillos, Camilo Andrés García, et al.
Published: (2025)
by: Trillos, Camilo Andrés García, et al.
Published: (2025)
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
by: Wang, Bokun, et al.
Published: (2023)
by: Wang, Bokun, et al.
Published: (2023)
An Optimal Control Approach To Transformer Training
by: Akman, Kağan, et al.
Published: (2026)
by: Akman, Kağan, et al.
Published: (2026)
More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)
by: Partohaghighi, Mohammad, et al.
Published: (2025)
First-order methods for Stochastic Variational Inequality problems with Function Constraints
by: Boob, Digvijay, et al.
Published: (2023)
by: Boob, Digvijay, et al.
Published: (2023)
Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
by: Liu, Zijian, et al.
Published: (2024)
by: Liu, Zijian, et al.
Published: (2024)
Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $α$-PL
by: Masiha, Saeed, et al.
Published: (2024)
by: Masiha, Saeed, et al.
Published: (2024)
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion
by: Cutkosky, Ashok, et al.
Published: (2023)
by: Cutkosky, Ashok, et al.
Published: (2023)
Mean-Field Generalisation Bounds for Learning Controls in Stochastic Environments
by: Baros, Boris, et al.
Published: (2025)
by: Baros, Boris, et al.
Published: (2025)
Recent Developments in Machine Learning Methods for Stochastic Control and Games
by: Hu, Ruimeng, et al.
Published: (2023)
by: Hu, Ruimeng, et al.
Published: (2023)
A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization
by: Zhu, Yuchen, et al.
Published: (2024)
by: Zhu, Yuchen, et al.
Published: (2024)
Similar Items
-
Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2026) -
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2024) -
Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023) -
Rare Event Analysis via Stochastic Optimal Control
by: Du, Yuanqi, et al.
Published: (2026) -
A unified perspective on fine-tuning and sampling with diffusion and flow models
by: Domingo-Enrich, Carles, et al.
Published: (2026)