Saved in:
| Main Author: | Daneshmand, Hadi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.19931 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data Generation without Function Estimation
by: Daneshmand, Hadi, et al.
Published: (2025)
by: Daneshmand, Hadi, et al.
Published: (2025)
Convergence of empirical subgradients for optimal transport-based objectives
by: Le, Tam
Published: (2026)
by: Le, Tam
Published: (2026)
Provably Convergent Federated Trilevel Learning
by: Jiao, Yang, et al.
Published: (2023)
by: Jiao, Yang, et al.
Published: (2023)
Provably Efficient Exploration in Policy Optimization
by: Cai, Qi, et al.
Published: (2019)
by: Cai, Qi, et al.
Published: (2019)
Sinkhorn algorithms and linear programming solvers for optimal partial transport problems
by: Bai, Yikun
Published: (2024)
by: Bai, Yikun
Published: (2024)
Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
by: Seyedi, Iman, et al.
Published: (2025)
by: Seyedi, Iman, et al.
Published: (2025)
Group-blind optimal transport to group parity and its constrained variants
by: Zhou, Quan, et al.
Published: (2023)
by: Zhou, Quan, et al.
Published: (2023)
Proximal optimal transport divergences
by: Baptista, Ricardo, et al.
Published: (2025)
by: Baptista, Ricardo, et al.
Published: (2025)
MGDA Converges under Generalized Smoothness, Provably
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
Learning Provably Improves the Convergence of Gradient Descent
by: Song, Qingyu, et al.
Published: (2025)
by: Song, Qingyu, et al.
Published: (2025)
Muon is Provably Faster with Momentum Variance Reduction
by: Qian, Xun, et al.
Published: (2025)
by: Qian, Xun, et al.
Published: (2025)
Provable Mixed-Noise Learning with Flow-Matching
by: Hagemann, Paul, et al.
Published: (2025)
by: Hagemann, Paul, et al.
Published: (2025)
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
by: Deng, Yuyang, et al.
Published: (2024)
by: Deng, Yuyang, et al.
Published: (2024)
Provable Adaptivity of Adam under Non-uniform Smoothness
by: Wang, Bohan, et al.
Published: (2022)
by: Wang, Bohan, et al.
Published: (2022)
SpectraLDS: Provable Distillation for Linear Dynamical Systems
by: Shah, Devan, et al.
Published: (2025)
by: Shah, Devan, et al.
Published: (2025)
Provably data-driven projection method for quadratic programming
by: Nguyen, Anh Tuan, et al.
Published: (2025)
by: Nguyen, Anh Tuan, et al.
Published: (2025)
On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
by: Sahu, Sharan, et al.
Published: (2026)
by: Sahu, Sharan, et al.
Published: (2026)
Provable Exactness for Asymmetric Low-Rank SDP Learning
by: Hu, Enliang
Published: (2018)
by: Hu, Enliang
Published: (2018)
An inexact Bregman proximal point method and its acceleration version for unbalanced optimal transport
by: Chen, Xiang, et al.
Published: (2024)
by: Chen, Xiang, et al.
Published: (2024)
Provably-Stable Neural Network-Based Control of Nonlinear Systems
by: Li, Anran, et al.
Published: (2025)
by: Li, Anran, et al.
Published: (2025)
Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
by: Chae, Jiseok, et al.
Published: (2024)
by: Chae, Jiseok, et al.
Published: (2024)
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods
by: Tao, Yuanzhe, et al.
Published: (2024)
by: Tao, Yuanzhe, et al.
Published: (2024)
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning
by: Zhang, Thomas T., et al.
Published: (2025)
by: Zhang, Thomas T., et al.
Published: (2025)
Momentum Benefits Non-IID Federated Learning Simply and Provably
by: Cheng, Ziheng, et al.
Published: (2023)
by: Cheng, Ziheng, et al.
Published: (2023)
Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
by: Chu, Ya-Chi, et al.
Published: (2025)
by: Chu, Ya-Chi, et al.
Published: (2025)
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
by: Baptista, Ricardo, et al.
Published: (2024)
by: Baptista, Ricardo, et al.
Published: (2024)
Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling
by: Li, Junyi, et al.
Published: (2024)
by: Li, Junyi, et al.
Published: (2024)
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)
by: Tucat, Matteo, et al.
Published: (2024)
First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
by: Shulgin, Egor, et al.
Published: (2025)
by: Shulgin, Egor, et al.
Published: (2025)
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach
by: Wang, Yao, et al.
Published: (2025)
by: Wang, Yao, et al.
Published: (2025)
A Provably Convergent and Practical Algorithm for Gromov--Wasserstein Optimal Transport
by: Liang, Ling, et al.
Published: (2026)
by: Liang, Ling, et al.
Published: (2026)
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
by: Palenzuela, Karlo, et al.
Published: (2025)
by: Palenzuela, Karlo, et al.
Published: (2025)
A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization
by: Chu, Tianshu, et al.
Published: (2025)
by: Chu, Tianshu, et al.
Published: (2025)
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
by: Jacot, Arthur, et al.
Published: (2024)
by: Jacot, Arthur, et al.
Published: (2024)
Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
by: Qiu, Shuang, et al.
Published: (2024)
by: Qiu, Shuang, et al.
Published: (2024)
Provably Convergent Decentralized Optimization over Directed Graphs under Generalized Smoothness
by: Bo, Yanan, et al.
Published: (2026)
by: Bo, Yanan, et al.
Published: (2026)
Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts
by: Liao, Fangshuo, et al.
Published: (2025)
by: Liao, Fangshuo, et al.
Published: (2025)
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
by: Vaswani, Sharan, et al.
Published: (2025)
by: Vaswani, Sharan, et al.
Published: (2025)
Provable Accelerated Convergence of Nesterov's Momentum for Deep ReLU Neural Networks
by: Liao, Fangshuo, et al.
Published: (2023)
by: Liao, Fangshuo, et al.
Published: (2023)
Linear attention is (maybe) all you need (to understand transformer optimization)
by: Ahn, Kwangjun, et al.
Published: (2023)
by: Ahn, Kwangjun, et al.
Published: (2023)
Similar Items
-
Data Generation without Function Estimation
by: Daneshmand, Hadi, et al.
Published: (2025) -
Convergence of empirical subgradients for optimal transport-based objectives
by: Le, Tam
Published: (2026) -
Provably Convergent Federated Trilevel Learning
by: Jiao, Yang, et al.
Published: (2023) -
Provably Efficient Exploration in Policy Optimization
by: Cai, Qi, et al.
Published: (2019) -
Sinkhorn algorithms and linear programming solvers for optimal partial transport problems
by: Bai, Yikun
Published: (2024)