NeurIPS 2025PastOptimization
OPT 2025: Optimization for Machine Learning
NeurIPS 2025 Workshop
- Submission deadline
- Sep 3, 2025, 12:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (132)
Fetched from OpenReview (v2) on 2026-06-10.
\textsc{LeonArDBO}: Fast and Prior-Driven Bayesian Optimization without Surrogate Modeling
A Monte Carlo Approach to Nonsmooth Convex Optimization via Proximal Splitting Algorithms
A Non-Convex Method for Polynomial Manifold Learning
A Simplified Analysis of SGD for Linear Regression with Weight Averaging
A stochastic Lagrangian-based method for nonconvex empirical risk minimization with nonlinear constraints
A Theoretical Analysis for CUR Decomposition based Active Learning and Feature Selection
A Unified Noise-Curvature View of Loss of Trainability
Achieving First-Order Statistical Improvements in Data-Driven Optimization
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
Adaptive acceleration without strong convexity priors or restarts
Algorithm design and sharper bounds for improving bandits
Aligning Distributionally Robust Optimization with Practical Deep Learning Needs
Aligning Theory with Practice for Muon-type Optimizers: A Layer-wise Framework
Analysis of Schedule Free Non-Convex Optimization
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
Asymptotic and Finite-Time Guarantees for Langevin-Based Temperature Annealing in InfoNCE
Atlas – Rethinking Optimizer Design for Stability and Speed
Augmented Normalization: Differentiating the Generalized Geometric Median
Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs
Balanced Locality-Sensitive Hashing for Online Data Selection
BatchNorm Layers have an Outsized Effect on Adversarial Robustness
Benefits of Learning Rate Annealing for Tuning-Robustness in Stochastic Optimization
Block-Diagonal K-FAC: A Trade-off Between Curvature Information and Resource Efficiency
Can SGD Handle Heavy-Tailed Noise?
Can We Estimate The Entropy Of Arbitrary Distributions Known Up To A Normalization Constant?
Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games
Central Limit Theorems for Asynchronous Averaged Q-Learning
Chebyshev Moment Regularization (CMR): Condition-Number Control with Moment Shaping
Communication Efficient LLM Pre-training with SparseLoCo
Connecting Membership Inference Privacy and Generalization through Instance-Wise Measurements
Convergence for Discrete Parameter Update Schemes
Convex Neural Networks For Robust ASR Language Detection
Curriculum-Learning PIELMs for Hemodynamic Flows
Data Generation without Function Estimation
Data Geometry Determines Generalization Below the Edge-of-Stability
Data Source Adaptive Online Learning under Heteroscedastic Noise
Data-Aware Training Quality Monitoring and Certification for Deep Learning
Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
Distributionally Robust Nash Equilibria via Variational Inequalities
Distributionally Robust Optimization via Diffusion Ambiguity Modeling
Domain-Aware Scaling Laws Uncover Data Synergy
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
DSGD-AC: controlled consensus errors improve generalization in decentralized training
EDIT: Early Diffusion Inference Termination for dLLMs Based on Dynamics of Training Gradients
Efficient Algorithms for Combinatorial-Bandits with Monotonicity
Efficient Training of CNN Ensembles via Feature-Prioritized Boosting
EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes
Empirical-Bayes XTFC for Inverse Parameter Estimation
Entropy Meets Importance: A Unified Head Importance–Entropy Score for Stable and Efficient Transformer Pruning
Error Feedback for Muon and Friends
Evolution of the Spectral Dimension of Transformer Activations
Exploring Magnitude Preservation and Rotation Modulation in Diffusion Transformers
Extending $\mu$P: Spectral Conditions for Feature Learning Across Optimizers
FairPO: Fair Preference Optimization for Multi-Label Learning
Fast decentralized gradient tracking for federated learning with local updates
Faster Gradient Methods for Highly-smooth Stochastic Bilevel Optimization
Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update
Feature Learning as a Virtual Covariance Learning
FineAMP: Optimization-Based Automatic Mixed Precision Quantization for Efficient Diffusion Model Inference
First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
Flat Minima and Generalization: Insights from Stochastic Convex Optimization
Foundations of Top-$k$ Decoding for Language Models
From Emergence to Intention: A Statistical Inductive Bias for Tractable Optimization in Multi-Agent Coordination
Gradient Descent’s Last Iterate is Often (slightly) Suboptimal
Graph-theoretic perspectives on splitting methods for sparse optimal transport
Grassmannian Optimization Drives Generationlization in Overparameterized DNN
Hessian Spectrum is Constant Across Minimizers in Regularized Deep Scalar Factorization
Hessian-Dependent Sample Complexity in Zeroth-Order Stochastic Optimization: Nonconvex Support Sampling Is Necessary for Optimality
High-dimensional isotropic scaling dynamics of Muon and SGD
HiSo: Efficient Federated Zeroth-Order Optimization via Hessian-Informed Acceleration and Scalar-Only Communication
How Does Layer Normalization Improve Deep $Q$-learning?
HyperPALoRA: Parameter-Efficient Pareto Hypernetworks via Preference-Based Diverse Low-Rank Adaptations
Hyperparameter-Free Auto-Scaled Gradient Normalization via Global Standard Deviation Dynamics
Implicit Bias of Per-sample Adam on Separable Data: Departure from the Full-batch Regime
Implicit Bias of Polyak and Line-Search Step Sizes on Linear Classification with Separable Data
Incentivizing Permissionless Distributed Learning of LLMs
Larger Datasets Can Be Repeated More: A Theoretical Analysis of Multi-Epoch Scaling in Linear Regression
Learning by solving differential equations
Lipschitz Optimization via Weighted Sampling Based on Expected Potential Maximizers Reduction
LOTION: Smoothing the Optimization Landscape for Quantized Training
M+Adam: Stable Low-Precision Training with Combined Adam--Madam Updates
Multi-Timescale Gradient Sliding for Distributed Optimization
Muon Optimizes Under Spectral Norm Constraints
New Optimization Methods for Very Large Scale SVMs
On Optimizing Large Scale Multi-Class Logistic Regression
On Riemannian Gradient Descent Algorithm using gradient averaging
On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
On the Finite-Sample Bias of Minimizing Expected Wasserstein Loss Between Empirical Distributions
On the Limits of Momentum in Decentralized and Federated Optimization
On the Potential of the Four-Point Model for Studying the Role of Optimization in Robustness to Spurious Correlations
On the Rollout-Training Mismatch in Modern RL Systems
One-Sided Matrix Completion from Ultra-Sparse Samples
OptiBridge: Multi-Scale Multi-Shift Bridging for Conditioning Optimization Landscapes
Optimal Implicit Bias in Linear Regression
Optimized Statistical Ranking is All You Need for Robust Coreset Selection in Efficient Transformer-Based Spam Detection
OrthoGrad Improves Neural Calibration
Parameter-Agnostic Error Feedback Enhanced With Hessian-Corrected Momentum
Partial Parameter Updates for Efficient Distributed Training
PEARL-Prox: Proximal Algorithm for Resolving Player Drift in Multiplayer Federated Learning
Per-Group Distributionally Robust Optimization (Per-GDRO) with Learnable Ambiguity Set Sizes via Bilevel Optimization
PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts
Policy Gradient Methods Converge Globally in Imperfect-Information Extensive-Form Games
Primal-dual hybrid algorithms for chi-squared regularized Optimal Transport: statistical-computational trade-offs and applications to Wasserstein Barycenters
Projected Compression
Provable Benefit of Sign Descent: A Minimal Model Under Heavy-Tail Class Imbalance
Quantum Non-Linear Bandit Optimization
Quantum Optimal Transport: Regularization and Algorithms
Quasi-Newton Methods for Federated Learning with Error Feedback
Regularizing the Entropy Landscape of Self-Attention: Towards a Soft Inductive Bias in LLMs
Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
Revisiting the Geometrically Decaying Step Size: Linear Convergence for Smooth or Non-Smooth Functions
Sharpness-Aware Minimization with Z-Score Gradient Filtering
Simultaneous Fine-Tuning and Pruning of LLMs
Sparse Adversarial Perturbation-Driven Scalable Coreset Optimization
Spiking Brain Compression: Exploring One-Shot Post-Training Pruning and Quantization for Spiking Neural Networks
Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
Stochastic Neural Tangent Kernel: Revisiting the NTK For SGD
Switching Gradient Methods for Constrained Federated Optimization
The Hebbian Forward-Forward Algorithm
The Hidden Cost of Approximation in Online Mirror Descent
The Limits of large learning rates: A Case Study in Single Index Models
Toward the First Optimization Framework for Low-Rank Adaptation
Towards Characterizing the Complexity of Riemannian Online Convex Optimization
Towards Quantifying the Hessian Structure of Neural Networks
Towards Robust Unroll Generalization in Learned Optimizers
Understanding and Improving Shampoo via Kullback–Leibler Minimization
Weight Decay may matter more than µP for Learning Rate Transfer in Practice
What really matters in matrix-whitening optimizers?
Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning
Why Does Stochastic Gradient Descent Slow Down in Low-Precision Training?
Zero-Infinity GAN: Stable Dynamics and Implicit Bias of Extragradient