Cover Image

Training Neural Networks at Any Scale

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pethick, Thomas, Antonakopoulos, Kimon, Silveti-Falls, Antonio, Vankadara, Leena Chennuru, Cevher, Volkan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Training Deep Learning Models with Norm-Constrained LMOs
by: Pethick, Thomas, et al.
Published: (2025)

Generalized Gradient Norm Clipping & Non-Euclidean $(L_0,L_1)$-Smoothness
by: Pethick, Thomas, et al.
Published: (2025)

μP$^2$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
by: Haas, Moritz, et al.
Published: (2024)

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
by: Oikonomidis, Konstantinos, et al.
Published: (2026)

Improving SAM Requires Rethinking its Optimization Formulation
by: Xie, Wanyun, et al.
Published: (2024)

Stable Nonconvex-Nonconcave Training via Linear Interpolation
by: Pethick, Thomas, et al.
Published: (2023)

Explaining Kernel Clustering via Decision Trees
by: Fleissner, Maximilian, et al.
Published: (2024)

SAMPa: Sharpness-aware Minimization Parallelized
by: Xie, Wanyun, et al.
Published: (2024)

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization
by: Vankadara, Leena Chennuru, et al.
Published: (2026)

On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling
by: Haas, Moritz, et al.
Published: (2025)

On the Generalization of Stochastic Gradient Descent with Momentum
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)

On the Role of Batch Size in Stochastic Conditional Gradient Methods
by: Islamov, Rustem, et al.
Published: (2026)

Optimistic Dual Averaging Unifies Modern Optimizers
by: Pethick, Thomas, et al.
Published: (2026)

Adaptive Conditional Gradient Descent
by: Khademi, Abbas, et al.
Published: (2025)

Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees
by: Wu, Yongtao, et al.
Published: (2025)

Self-Compatibility: Evaluating Causal Discovery without Ground Truth
by: Faller, Philipp M., et al.
Published: (2023)

Layer-wise Quantization for Quantized Optimistic Dual Averaging
by: Nguyen, Anh Duc, et al.
Published: (2025)

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
by: Liu, Fanghui, et al.
Published: (2024)

Boosted Stochastic Frank-Wolfe for Constrained Nonconvex Optimization
by: Nandhan, Navil, et al.
Published: (2026)

MaD-Mix: Multi-Modal Data Mixtures via Latent Space Coupling for Vision-Language Model Training
by: Xie, Wanyun, et al.
Published: (2026)

Universal Gradient Methods for Stochastic Convex Optimization
by: Rodomanov, Anton, et al.
Published: (2024)

Efficient Large Language Model Inference with Neural Block Linearization
by: Erdogan, Mete, et al.
Published: (2025)

Adversarial Training for Defense Against Label Poisoning Attacks
by: Bal, Melis Ilayda, et al.
Published: (2025)

IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic
by: Viel, Stefano, et al.
Published: (2025)

Imitation Learning in Discounted Linear MDPs without exploration assumptions
by: Viano, Luca, et al.
Published: (2024)

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
by: Xie, Wanyun, et al.
Published: (2025)

Generalization of Scaled Deep ResNets in the Mean-Field Regime
by: Chen, Yihang, et al.
Published: (2024)

Multilinear Operator Networks
by: Cheng, Yixin, et al.
Published: (2024)

MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection
by: Afzal, Arshia, et al.
Published: (2025)

Adversarial Training Should Be Cast as a Non-Zero-Sum Game
by: Robey, Alexander, et al.
Published: (2023)

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
by: Sheebaelhamd, Ziyad, et al.
Published: (2026)

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution
by: Barla, Adam, et al.
Published: (2026)

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
by: Chen, Yihang, et al.
Published: (2024)

Easy Data Unlearning Bench
by: Rinberg, Roy, et al.
Published: (2026)

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining
by: Bal, Melis Ilayda, et al.
Published: (2025)

Matching Multiple Experts: On the Exploitability of Multi-Agent Imitation Learning
by: Bergerault, Antoine, et al.
Published: (2026)

Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness
by: Agafonov, Artem, et al.
Published: (2023)

Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning
by: Freihaut, Till, et al.
Published: (2025)

Best of Both Worlds: Regret Minimization versus Minimax Play
by: Müller, Adrian, et al.
Published: (2025)

Accelerating Spectral Clustering under Fairness Constraints
by: Tonin, Francesco, et al.
Published: (2025)