LMEMs for post-hoc analysis of HPO Benchmarking
Fuente:
arXiv
Saved in:
| Main Authors: | Geburek, Anton, Mallik, Neeratyoy, Stoll, Danny, Bouthillier, Xavier, Hutter, Frank |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
by: Watanabe, Shuhei, et al.
Published: (2024)
by: Watanabe, Shuhei, et al.
Published: (2024)
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
by: Carstensen, Timur, et al.
Published: (2025)
by: Carstensen, Timur, et al.
Published: (2025)
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
by: Rakotoarison, Herilalaina, et al.
Published: (2024)
by: Rakotoarison, Herilalaina, et al.
Published: (2024)
When is Warmstarting Effective for Scaling Language Models?
by: Mallik, Neeratyoy, et al.
Published: (2026)
by: Mallik, Neeratyoy, et al.
Published: (2026)
Warmstarting for Scaling Language Models
by: Mallik, Neeratyoy, et al.
Published: (2024)
by: Mallik, Neeratyoy, et al.
Published: (2024)
Multi-objective Hyperparameter Optimization in the Age of Deep Learning
by: Basu, Soham, et al.
Published: (2025)
by: Basu, Soham, et al.
Published: (2025)
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
by: Benjamins, Carolin, et al.
Published: (2025)
by: Benjamins, Carolin, et al.
Published: (2025)
Revisiting the robustness of post-hoc interpretability methods
by: Wei, Jiawen, et al.
Published: (2024)
by: Wei, Jiawen, et al.
Published: (2024)
Model-agnostic post-hoc explainability for recommender systems
by: Arévalo, Irina, et al.
Published: (2025)
by: Arévalo, Irina, et al.
Published: (2025)
HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime
by: Sana, Mohamed, et al.
Published: (2026)
by: Sana, Mohamed, et al.
Published: (2026)
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification
by: Jiang, Jiantong, et al.
Published: (2024)
by: Jiang, Jiantong, et al.
Published: (2024)
Towards Benchmarking Foundation Models for Tabular Data With Text
by: Mráz, Martin, et al.
Published: (2025)
by: Mráz, Martin, et al.
Published: (2025)
Fast Optimizer Benchmark
by: Blauth, Simon, et al.
Published: (2024)
by: Blauth, Simon, et al.
Published: (2024)
Introducing Milabench: Benchmarking Accelerators for AI
by: Delaunay, Pierre, et al.
Published: (2024)
by: Delaunay, Pierre, et al.
Published: (2024)
Training data membership inference via Gaussian process meta-modeling: a post-hoc analysis approach
by: Huang, Yongchao, et al.
Published: (2025)
by: Huang, Yongchao, et al.
Published: (2025)
In defence of post-hoc explanations in medical AI
by: Hatherley, Joshua, et al.
Published: (2025)
by: Hatherley, Joshua, et al.
Published: (2025)
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
by: Watanabe, Shuhei, et al.
Published: (2022)
by: Watanabe, Shuhei, et al.
Published: (2022)
Evaluation of post-hoc interpretability methods in time-series classification
by: Turbé, Hugues, et al.
Published: (2022)
by: Turbé, Hugues, et al.
Published: (2022)
TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
by: Kirscher, Tristan, et al.
Published: (2026)
by: Kirscher, Tristan, et al.
Published: (2026)
A redescription mining framework for post-hoc explaining and relating deep learning models
by: Mihelčić, Matej, et al.
Published: (2025)
by: Mihelčić, Matej, et al.
Published: (2025)
Bayes' Power for Explaining In-Context Learning Generalizations
by: Müller, Samuel, et al.
Published: (2024)
by: Müller, Samuel, et al.
Published: (2024)
A General Framework for User-Guided Bayesian Optimization
by: Hvarfner, Carl, et al.
Published: (2023)
by: Hvarfner, Carl, et al.
Published: (2023)
Early Stopping Tabular In-Context Learning
by: Küken, Jaris, et al.
Published: (2025)
by: Küken, Jaris, et al.
Published: (2025)
Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)
by: Bühler, Magnus, et al.
Published: (2026)
Channel Estimation by Infinite Width Convolutional Networks
by: Mallik, Mohammed, et al.
Published: (2025)
by: Mallik, Mohammed, et al.
Published: (2025)
Auction-Based Online Policy Adaptation for Evolving Objectives
by: Shabadi, Guruprerana, et al.
Published: (2026)
by: Shabadi, Guruprerana, et al.
Published: (2026)
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
by: Hennig, Leona, et al.
Published: (2024)
by: Hennig, Leona, et al.
Published: (2024)
Post-hoc Probabilistic Vision-Language Models
by: Baumann, Anton, et al.
Published: (2024)
by: Baumann, Anton, et al.
Published: (2024)
Efficient Search for Customized Activation Functions with Gradient Descent
by: Strack, Lukas, et al.
Published: (2024)
by: Strack, Lukas, et al.
Published: (2024)
Large Language Models Engineer Too Many Simple Features For Tabular Data
by: Küken, Jaris, et al.
Published: (2024)
by: Küken, Jaris, et al.
Published: (2024)
Don't Waste Your Time: Early Stopping Cross-Validation
by: Bergman, Edward, et al.
Published: (2024)
by: Bergman, Edward, et al.
Published: (2024)
EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
by: Arbel, Michael, et al.
Published: (2025)
by: Arbel, Michael, et al.
Published: (2025)
Agentic NL2SQL to Reduce Computational Costs
by: Jehle, Dominik, et al.
Published: (2025)
by: Jehle, Dominik, et al.
Published: (2025)
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
by: Becktepe, Jannis, et al.
Published: (2024)
by: Becktepe, Jannis, et al.
Published: (2024)
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes
by: Robertson, Jake, et al.
Published: (2024)
by: Robertson, Jake, et al.
Published: (2024)
Tune My Adam, Please!
by: Athanasiadis, Theodoros, et al.
Published: (2025)
by: Athanasiadis, Theodoros, et al.
Published: (2025)
How Usable is Automated Feature Engineering for Tabular Data?
by: Schäfer, Bastian, et al.
Published: (2025)
by: Schäfer, Bastian, et al.
Published: (2025)
Self-Correcting Bayesian Optimization through Bayesian Active Learning
by: Hvarfner, Carl, et al.
Published: (2023)
by: Hvarfner, Carl, et al.
Published: (2023)
nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN
by: Pfefferle, Alexander, et al.
Published: (2025)
by: Pfefferle, Alexander, et al.
Published: (2025)
TabArena: A Living Benchmark for Machine Learning on Tabular Data
by: Erickson, Nick, et al.
Published: (2025)
by: Erickson, Nick, et al.
Published: (2025)
Similar Items
-
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
by: Watanabe, Shuhei, et al.
Published: (2024) -
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
by: Carstensen, Timur, et al.
Published: (2025) -
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
by: Rakotoarison, Herilalaina, et al.
Published: (2024) -
When is Warmstarting Effective for Scaling Language Models?
by: Mallik, Neeratyoy, et al.
Published: (2026) -
Warmstarting for Scaling Language Models
by: Mallik, Neeratyoy, et al.
Published: (2024)