Study shows leaving some hyperparameters at default can outperform tuning them.
problem Importance of tuning hyperparameters in machine learning performance.
method Non-inferiority test and tuning risk methodology.
result Leaving some hyperparameters at default can outperform tuning them.
New method for tuning Graphical Lasso hyperparameters.
problem Tuning hyperparameters of Graphical Lasso.
method Bilevel optimization with first-order method.
result Derivation of Graphical Lasso Jacobian.
Mango automates hyperparameter tuning for large-scale ML training.
problem Manual hyperparameter tuning is tedious and inefficient for large-scale machine learning.
method Parallel hyperparameter tuning with intelligent search strategies and flexible abstractions.
result Mango achieves comparable performance to Hyperopt while supporting distributed computing.
Efficiently tunes hyperparameters with dynamic accuracy method.
problem Optimizing machine learning hyperparameters with inexact evaluations.
method Dynamic accuracy derivative-free optimization for hyperparameter tuning.
result Demonstrates robust and efficient hyperparameter tuning compared to fixed accuracy methods.
HASSO improves SO algorithms by dynamically tuning hyperparameters.
problem Inefficiency of hyperparameter tuning for SO algorithms.
method HASSO is a self-adjusting SO algorithm that dynamically tunes its own hyperparameters.
result HASSO enhances the performance of various SO algorithms across different test problems.
Differentially private hyperparameter tuning improves privacy in machine learning.
problem Hyperparameter tuning leaks private information through selected configurations.
method Local Bayesian optimization using Gaussian Process surrogate for private gradient approximation.
result DP-GIBO converges to locally optimal hyperparameters with polynomial dimensional dependence.
New framework for data-driven hyperparameter tuning with structured loss.
problem Statistical foundations for multi-dimensional hyperparameter tuning remain limited.
method General framework using real algebraic geometry for semi-algebraic function classes.
result First general guarantees for multi-dimensional hyperparameter tuning.
New method FedEx accelerates federated hyperparameter tuning.
problem Federated hyperparameter tuning challenges in distributed learning.
method FedEx method connecting to weight-sharing, adapted for federated optimization.
result FedEx outperforms natural baselines on various benchmarks.
Paper defines hyperparameter importance for efficient tuning.
problem Computational inefficiency in tuning all hyperparameters.
method Defines hyperparameter importance via subsampling procedures.
result Proposed importance consistent with full data under weak conditions.
Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks. Current practices for fine-tuning typically involve selecting an ad-hoc choice of hyperparameters and keeping them fixed to values normally used for training from scratch. This paper re-examines several comm…
New method for hyperparameter tuning in sparse matrix factorization.
problem Hyperparameter tuning in sparse matrix factorization.
method Numerical method based on evaluating the zero point of normalization factor in sparse matrix prior.
result Our method outperforms existing algorithms in ground-truth sparse matrix reconstruction.
A cost-efficient method for hyperparameter tuning using multi-fidelity Bayesian optimization.
problem Expensive hyperparameter tuning with limited knowledge transfer methods.
method Amortized Auto-Tuning (AT2) framework for multi-task, multi-fidelity Bayesian optimization.
result AT2 leads to the best hyperparameter recommendation and is more cost-efficient.
This paper tackles hyperparameter tuning for large-scale kernel ridge regression.
problem Hyperparameter tuning is crucial but often left to users, hindering efficiency and usability.
method Proposes a complexity regularization criterion based on a data-dependent penalty for efficient optimization.
result Demonstrates the benefit of the proposed approach through extensive empirical evaluation.
Paper proposes EEIPU, a memoization-aware BO algorithm to reduce hyperparameter tuning costs.
problem High costs in GPU-days for training and fine-tuning language models.
method Memoization-aware Bayesian Optimization (EEIPU) algorithm in tandem with pipeline caching.
result EEIPU produces 103% more hyperparameter candidates and 108% more validation metric improvement.
Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermediate computations are the same, we propose a pipeline-aware approach to hyperparameter tuning. Our approach optimizes both the design and exe…
Sherpa automates hyperparameter tuning for machine learning models.
problem Hyperparameter optimization for computationally expensive models.
method Powerful and interchangeable algorithms, interactive dashboard.
result Automates tedious aspects of model tuning.
In this paper we develop a Bayesian optimization based hyperparameter tuning framework inspired by statistical learning theory for classifiers. We utilize two key facts from PAC learning theory; the generalization bound will be higher for a small subset of data compared to the whole, and the highest accuracy for a smal…
New method improves hyperparameter tuning efficiency across similar tasks.
problem Mismatch between evaluations in current and previous tasks.
method Nested drop-out and auto-relevance determination for learning basis functions of increasing complexity.
result Improves sample efficiency in hyperparameter tuning across different data regimes.
Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quality. For hyperparameter tuning, machine learning algorithms are complex black-boxes. This creates a class of challenging optimization problem…
New algorithm tunes SGMCMC hyperparameters for scalable Bayesian inference.
problem Tuning hyperparameters for SGMCMC is challenging due to lack of principled methods.
method Proposes a bandit-based algorithm using Stein discrepancies to tune hyperparameters.
result The method effectively tunes SGMCMC hyperparameters for various applications.
Modern supervised machine learning algorithms involve hyperparameters that have to be set before running them. Options for setting hyperparameters are default values from the software package, manual configuration by the user or configuring them for optimal predictive performance by a tuning procedure. The goal of this…
OTSL improves structure learning accuracy with out-of-sample and resampling strategies.
problem Determining optimal hyperparameters for structure learning algorithms.
method Out-of-sample Tuning for Structure Learning (OTSL) using resampling strategies.
result Improves graphical accuracy of structure learning algorithms.
TreeDOX predicts chaotic systems without hyperparameter tuning.
problem Forecasting chaotic systems requires hyperparameter tuning, limiting adoption.
method TreeDOX uses time delay overembedding and Extra-Trees Regressors.
result TreeDOX achieves state-of-the-art performance on chaotic systems.
Study shows data attribution methods are sensitive to hyperparameters, making tuning costly.
problem Hyperparameter sensitivity in data attribution methods makes tuning impractical.
method Theoretical analysis and lightweight procedure for selecting regularization value without retraining.
result Proposes a lightweight procedure for selecting regularization value without model retraining.
The paper analyzes the statistical cost of tuning kernel hyperparameters in robust regression.
problem Finding the best interpolant from a class of kernels with unknown hyperparameters under adversarial noise.
method Finite-sample guarantees, subsampling guarantee for linear regression, ε-net argument for discretizing kernel parameterizations.
result Hyperparameter optimization increases sample complexity by just a logarithmic factor, compared to known parameters.
Optimizes hyperparameter tuning for models using approximate leave-one-out cross-validation.
problem Finding optimal hyperparameters for regularized models using approximate leave-one-out cross-validation.
method Derive efficient formulas for gradient and hessian of approximate leave-one-out cross-validation, apply second-order optimization.
result Demonstrates the effectiveness of the approach on real-world data sets.
Bayesian optimization outperformed random search in machine learning hyperparameter tuning challenge.
problem Optimizing hyperparameters of machine learning models using derivative-free methods.
method Bayesian optimization vs. random search on real datasets.
result Bayesian optimization significantly outperformed random search in held-out objective functions.
Study assesses hyperparameter tuning for causal inference with DML.
problem Optimizing hyperparameters for causal inference with DML.
method Empirical simulation study using DML approach.
result Hyperparameter tuning crucial for causal estimation with DML.
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially on larger datasets, while the tuned settings do not always significantly outperform the default val…
The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative training process to run for numerous steps to convergence. Traditional tuning algorithms only consider the final performance of hyperparame…
Efficient auto-tuning for DR hyperparameters with BO.
problem Hyperparameter selection in DR for large-scale datasets.
method Bayesian optimization with surrogate model, normalization, subsampling.
result Robust and efficient hyperparameter selection for DR algorithms.
New optimizer improves privacy-protected hyperparameter tuning.
problem No practical methods for differentially private hyperparameter selection.
method Study honest hyperparameter selection under DP, show adaptive optimizers like DPAdam have an advantage.
result DPAdam optimizes hyperparameters more efficiently under DP constraints.
New benchmark protocol evaluates neural network optimizers for efficiency and data shift sensitivity.
problem Benchmarking neural network optimizers with hyperparameter complexity and data shift sensitivity.
method Proposed a new evaluation protocol combining end-to-end and data-addition training efficiency, using bandit hyperparameter tuning and human study validation.
result No clear winner across all tasks, highlighting the complexity of optimizer performance.
Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts. A downside of these models is their strong sensitivity to model hyperparameters, in particular regularizers, which have to be extensively …
Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters …
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples tha…
Amazon SageMaker AMT automates machine learning model tuning.
problem Challenging hyperparameter tuning for complex machine learning systems.
method Gradient-free optimization using random search or Bayesian optimization.
result AMT finds the best hyperparameter configurations for models.
A new framework tunes hyperparameters in real-time for contextual bandits.
problem Optimizing hyperparameters for contextual bandits in real-time.
method CDT (Continuous Dynamic Tuning) framework using Zooming TS algorithm.
result Achieves sublinear regret and performs better than existing methods.
This study analyzes and optimizes hyperparameters for machine learning models.
problem Optimizing hyperparameters for machine learning models to improve performance.
method Experimental analysis of 30 hyperparameters from six machine learning algorithms using R and SPOT.
result A new consensus ranking method for analyzing results from multiple algorithms.
Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinf…
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
problem Optimizing black-box functions with noisy, expensive evaluations and hyperparameter tuning.
method Exploring heuristics to automatically tune acquisition functions in Bayesian Optimization.
result Demonstrates effectiveness of heuristics in automatic Bayesian Optimization.
The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive. More advanced methods like Population Based Training that learn optimal schedule…
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
problem Redundant and time-consuming hyperparameter tuning for SVM classifiers in medical imaging.
method Employed multi-task Bayesian optimization to accelerate hyperparameter search.
result Multi-task Bayesian optimization significantly accelerates hyperparameter search.
Proposes a gradient-based bilevel optimization method for efficient hyperparameter tuning.
problem Efficiently tuning hyperparameters in machine learning models.
method Gradient-based bilevel optimization approach.
result The proposed method is multiple times faster than existing techniques.
A new method reduces hyperparameter tuning evaluations by using sequential tests.
problem Time-consuming hyperparameter tuning in machine learning.
method Sequential Random Search (SQRS) extending regular random search.
result SQRS finds similarly well-performing parameter settings with fewer evaluations.
The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under near-optimal problem-specific hyperparameters, and finding these settings may be prohibitively costly for practitioners. In this work, we arg…
This paper highlights the overlooked role of preprocessing hyperparameters in machine learning model performance.
problem The neglect of preprocessing hyperparameters in machine learning model evaluation.
method Empirical review and illustration of different procedures for generating and evaluating prediction models.
result Users may fail to report or account for preprocessing hyperparameter optimization, leading to exaggerated performance claims.
New method upscales models and transfers hyperparameters efficiently.
problem Efficiently scaling and tuning large neural networks.
method Introduces a general upscaling method and extends μTransfer for hyperparameter tuning. result Effective hyperparameter transfer for upscaled models.