Cover Image

Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chaudhry, Faris, Monod, Anthea, Yano, Keisuke
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Feature Starvation as Geometric Instability in Sparse Autoencoders
by: Chaudhry, Faris, et al.
Published: (2026)

The Geometry of Projection Heads: Conditioning, Invariance, and Collapse
by: Chaudhry, Faris
Published: (2026)

Tropical Gradient Descent
by: Talbut, Roan, et al.
Published: (2024)

Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
by: Maros, Marie, et al.
Published: (2022)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
by: Ruan, Feng
Published: (2024)

A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
by: Alfano, Carlo, et al.
Published: (2023)

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
by: Yang, Zhuoran, et al.
Published: (2020)

Asymptotic and Finite-Time Guarantees for Langevin-Based Temperature Annealing in InfoNCE
by: Chaudhry, Faris
Published: (2026)

Ensemble-Conditional Gaussian Processes (Ens-CGP): Representation, Geometry, and Inference
by: Ravela, Sai, et al.
Published: (2026)

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
by: Liang, Tengyuan
Published: (2022)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)

Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
by: Guo, Zijian, et al.
Published: (2025)

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
by: Armacki, Aleksandar, et al.
Published: (2023)

High-dimensional Limit of SGD for Diagonal Linear Networks
by: Malaxechebarría, Begoña García, et al.
Published: (2026)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation
by: Zarifis, Nikos, et al.
Published: (2025)

On the Sample Complexity of Set Membership Estimation for Linear Systems with Disturbances Bounded by Convex Sets
by: Xu, Haonan, et al.
Published: (2024)

Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
by: Agrawalla, Bhavya, et al.
Published: (2023)

Stochastic Optimization with Optimal Importance Sampling
by: Aolaritei, Liviu, et al.
Published: (2025)

Federated Optimization of Smooth Loss Functions
by: Jadbabaie, Ali, et al.
Published: (2022)

Joint Learning of Linear Dynamical Systems under Smoothness Constraints
by: Tyagi, Hemant
Published: (2024)

Robust Assortment Optimization from Observational Data
by: Lu, Miao, et al.
Published: (2026)

Certified Multi-Fidelity Zeroth-Order Optimization
by: de Montbrun, Étienne, et al.
Published: (2023)

Convergence of coordinate ascent variational inference for log-concave measures via optimal transport
by: Arnese, Manuel, et al.
Published: (2024)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
by: Gao, Yihang, et al.
Published: (2025)

Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)

Approximating Persistent Homology for Large Datasets
by: Cao, Yueqi, et al.
Published: (2022)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
by: Zhu, Linglingzhi, et al.
Published: (2025)

The Star Geometry of Critic-Based Regularizer Learning
by: Leong, Oscar, et al.
Published: (2024)

Selecting the Best Optimizing System
by: Si, Nian, et al.
Published: (2022)

Causal Invariance Learning via Efficient Nonconvex Optimization
by: Wang, Zhenyu, et al.
Published: (2024)

Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality
by: Chen, Siyu, et al.
Published: (2024)

Convergence rate of random scan Coordinate Ascent Variational Inference under log-concavity
by: Lavenant, Hugo, et al.
Published: (2024)

Markov Kernels, Distances and Optimal Control: A Parable of Linear Quadratic Non-Gaussian Distribution Steering
by: Teter, Alexis M. H., et al.
Published: (2025)

Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
by: Luo, Yuetian, et al.
Published: (2022)

A Spectral Framework for Closed-Form Relative Density Estimation
by: Bach, Francis
Published: (2026)

Risk reversal for least squares estimators under nested convex constraints
by: Al-Ghattas, Omar
Published: (2026)

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
by: Vukovic, Manojlo, et al.
Published: (2026)

Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems
by: Liao, Aaron R., et al.
Published: (2026)

Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements
by: Meng, Xiang, et al.
Published: (2026)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
by: Roy, Somjit, et al.
Published: (2026)