hyperparameter tuning
The process of systematically adjusting the parameters that govern the training of machine learning models to optimize their performance.
- A Clean Slate for Offline Reinforcement Learning
- A Clean Slate for Offline Reinforcement Learning
- A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models
- AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining
- Beyond Benign Overfitting in Nadaraya-Watson Interpolators
- COGNAC: Cooperative Graph-based Networked Agent Challenges for Multi-Agent Reinforcement Learning
- Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
- Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
- Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
- Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size
- Exploring the Design Space of Diffusion Bridge Models
- FastJAM: a Fast Joint Alignment Model for Images
- Flux4D: Flow-based Unsupervised 4D Reconstruction
- Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization
- Influence Guided Context Selection for Effective Retrieval-Augmented Generation
- LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
- Learning with Statistical Equality Constraints
- MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
- Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
- Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
- Non-Asymptotic Analysis Of Data Augmentation For Precision Matrix Estimation
- Private Hyperparameter Tuning with Ex-Post Guarantee
- Problem-Parameter-Free Decentralized Bilevel Optimization
- Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
- Scalable and adaptive prediction bands with kernel sum-of-squares
- Scaling Diffusion Transformers Efficiently via $\mu$P
- Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
- Stab-SGD: Noise-Adaptivity in Smooth Optimization with Stability Ratios
- Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining
- The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model
- The Primacy of Magnitude in Low-Rank Adaptation
- Training the Untrainable: Introducing Inductive Bias via Representational Alignment
- ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data