Efficient Hyperparameter Tuning via Trajectory Invariance Principle
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Bingrui, Wen, Jiaxin, Zhou, Zhanpeng, Zhu, Jun, Chen, Jianfei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
by: Li, Bingrui, et al.
Published: (2024)
by: Li, Bingrui, et al.
Published: (2024)
Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late in Training
by: Zhou, Zhanpeng, et al.
Published: (2024)
by: Zhou, Zhanpeng, et al.
Published: (2024)
Principled Architecture-aware Scaling of Hyperparameters
by: Chen, Wuyang, et al.
Published: (2024)
by: Chen, Wuyang, et al.
Published: (2024)
Efficient Backpropagation with Variance-Controlled Adaptive Sampling
by: Wang, Ziteng, et al.
Published: (2024)
by: Wang, Ziteng, et al.
Published: (2024)
Using Sequential Statistical Tests for Efficient Hyperparameter Tuning
by: Buczak, Philip, et al.
Published: (2021)
by: Buczak, Philip, et al.
Published: (2021)
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
by: Tian, Zichen, et al.
Published: (2026)
by: Tian, Zichen, et al.
Published: (2026)
S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training
by: Hu, Yuezhou, et al.
Published: (2024)
by: Hu, Yuezhou, et al.
Published: (2024)
Causal Learning with the Invariance Principle
by: Montagna, Francesco, et al.
Published: (2026)
by: Montagna, Francesco, et al.
Published: (2026)
Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
by: Lee, Dong Bok, et al.
Published: (2025)
by: Lee, Dong Bok, et al.
Published: (2025)
PLoRA: Efficient LoRA Hyperparameter Tuning for Large Models
by: Yan, Minghao, et al.
Published: (2025)
by: Yan, Minghao, et al.
Published: (2025)
Generative Bayesian Hyperparameter Tuning
by: Lopes, Hedibert, et al.
Published: (2025)
by: Lopes, Hedibert, et al.
Published: (2025)
On the Identifiability of Causal Graphs with the Invariance Principle
by: Montagna, Francesco
Published: (2025)
by: Montagna, Francesco
Published: (2025)
A Comparative Study of Hyperparameter Tuning Methods
by: Dasgupta, Subhasis, et al.
Published: (2024)
by: Dasgupta, Subhasis, et al.
Published: (2024)
Hyperparameter Tuning Through Pessimistic Bilevel Optimization
by: Ustun, Meltem Apaydin, et al.
Published: (2024)
by: Ustun, Meltem Apaydin, et al.
Published: (2024)
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning
by: Wang, Yiran, et al.
Published: (2024)
by: Wang, Yiran, et al.
Published: (2024)
The Condition-Number Principle for Prototype Clustering
by: Li, Romano, et al.
Published: (2026)
by: Li, Romano, et al.
Published: (2026)
Unifying Causal Representation Learning with the Invariance Principle
by: Yao, Dingling, et al.
Published: (2024)
by: Yao, Dingling, et al.
Published: (2024)
From Black-Box Tuning to Guided Optimization via Hyperparameters Interaction Analysis
by: Garouani, Moncef, et al.
Published: (2025)
by: Garouani, Moncef, et al.
Published: (2025)
Revisiting Hyperparameter Tuning with Differential Privacy
by: Ding, Youlong, et al.
Published: (2022)
by: Ding, Youlong, et al.
Published: (2022)
SparseDM: Toward Sparse Efficient Diffusion Models
by: Wang, Kafeng, et al.
Published: (2024)
by: Wang, Kafeng, et al.
Published: (2024)
ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing
by: Wang, Ziteng, et al.
Published: (2024)
by: Wang, Ziteng, et al.
Published: (2024)
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
by: Madhusudhanan, Kiran, et al.
Published: (2024)
by: Madhusudhanan, Kiran, et al.
Published: (2024)
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance
by: Li, Qiang, et al.
Published: (2025)
by: Li, Qiang, et al.
Published: (2025)
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization
by: Xi, Haocheng, et al.
Published: (2024)
by: Xi, Haocheng, et al.
Published: (2024)
Time-Efficient Hybrid Hyperparameter Tuning Approach for Cardiovascular Disease Classification
by: Pathak, Abhay Kumar, et al.
Published: (2024)
by: Pathak, Abhay Kumar, et al.
Published: (2024)
Practical Differentially Private Hyperparameter Tuning with Subsampling
by: Koskela, Antti, et al.
Published: (2023)
by: Koskela, Antti, et al.
Published: (2023)
Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
by: Amad, Harry, et al.
Published: (2026)
by: Amad, Harry, et al.
Published: (2026)
A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
by: Zhou, Yuhao, et al.
Published: (2025)
by: Zhou, Yuhao, et al.
Published: (2025)
The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
by: Machlanski, Damian, et al.
Published: (2023)
by: Machlanski, Damian, et al.
Published: (2023)
Differentially Private Hyperparameter Tuning using Local Bayesian Optimization
by: Sopa, Getoar, et al.
Published: (2025)
by: Sopa, Getoar, et al.
Published: (2025)
Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning
by: Willemsen, Floris-Jan, et al.
Published: (2025)
by: Willemsen, Floris-Jan, et al.
Published: (2025)
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso
by: Pouliquen, Can, et al.
Published: (2023)
by: Pouliquen, Can, et al.
Published: (2023)
PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuning
by: Xu, Beicheng, et al.
Published: (2025)
by: Xu, Beicheng, et al.
Published: (2025)
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
by: Lin, Lequan, et al.
Published: (2024)
by: Lin, Lequan, et al.
Published: (2024)
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness
by: Essofi, Abdelmajid, et al.
Published: (2024)
by: Essofi, Abdelmajid, et al.
Published: (2024)
R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning
by: Chen, Zhuokun, et al.
Published: (2025)
by: Chen, Zhuokun, et al.
Published: (2025)
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
by: Onorato, Gabriele
Published: (2024)
by: Onorato, Gabriele
Published: (2024)
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
by: Zheng, Kaiwen, et al.
Published: (2023)
by: Zheng, Kaiwen, et al.
Published: (2023)
FedPop: Federated Population-based Hyperparameter Tuning
by: Chen, Haokun, et al.
Published: (2023)
by: Chen, Haokun, et al.
Published: (2023)
Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
by: Lin, Luxi, et al.
Published: (2026)
by: Lin, Luxi, et al.
Published: (2026)
Similar Items
-
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
by: Li, Bingrui, et al.
Published: (2024) -
Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late in Training
by: Zhou, Zhanpeng, et al.
Published: (2024) -
Principled Architecture-aware Scaling of Hyperparameters
by: Chen, Wuyang, et al.
Published: (2024) -
Efficient Backpropagation with Variance-Controlled Adaptive Sampling
by: Wang, Ziteng, et al.
Published: (2024) -
Using Sequential Statistical Tests for Efficient Hyperparameter Tuning
by: Buczak, Philip, et al.
Published: (2021)