Method analyzes hyperparameters using HSIC for better neural network performance.
problem Complex hyperparameter spaces in deep learning.
method Goal-oriented sensitivity analysis using HSIC.
result Robust analysis index quantifies hyperparameters' impact.
A game-theoretic framework identifies influential hyperparameters for neural networks.
problem Understanding which hyperparameters are most important for neural network performance.
method Employing Shapley Effects for global sensitivity analysis and Pareto front sets for identifying effective configurations.
result Reveals which hyperparameters are most influential for different objectives in neural networks.
Develops a method to optimize hyperparameters for subsampling methods.
problem Optimizing hyperparameters for subsampling methods to improve estimator efficiency.
method Careful theoretical analysis leading to an optimal choice of hyperparameters.
result Improves the statistical efficiency of subsampling estimators without extra CPU time.
Global sensitivity analysis improves BNN hyperparameter selection for accurate uncertainty quantification.
problem Difficulties in obtaining accurate uncertainty quantification with Bayesian Neural Networks (BNNs).
method Global sensitivity analysis of BNN performance under varying hyperparameter settings.
result Many hyperparameters interact to affect both predictive accuracy and uncertainty quantification.
New algorithm stabilizes bi-level hyperparameter optimization.
problem Stability issues in bi-level hyperparameter optimization.
method Uses Moreau-Yosida regularization to stabilize convergence.
result Significant improvement in loss values with fixed computation budget.
Transforms game optimization dynamics into frequency domain for precise hyperparameter analysis.
problem Analyzing convergence of hyperparameters in game optimization.
method Frequency-domain framework using High-Resolution Differential Equations (HRDEs) and Laplace transforms.
result Derives precise convergence criteria for the Lookahead algorithm.
This study analyzes quantization in deep learning models using statistical physics methods.
problem The computational resource requirements for large-scale data analysis models.
method Typical case analysis from statistical physics, specifically the replica method.
result Optimal quantization width minimizes error and delays overfitting.
A new survival analysis method eliminates hyperparameter tuning.
problem Survival analysis model hyperparameters are difficult to tune.
method Flexible survival density estimation with importance sampling.
result Matches or outperforms existing methods on real-world datasets.
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.
Many algorithms for data analysis exist, especially for classification problems. To solve a data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper, we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order…
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 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.
This paper evaluates heuristics and hyperparameters in weight-sharing NAS methods.
problem Improving the performance of weight-sharing NAS methods.
method Systematic evaluation of heuristics and hyperparameters in weight-sharing NAS algorithms.
result Some heuristics negatively impact super-net and stand-alone performance correlation.
Transfer learning improves prediction quality in high-dimensional sparse regression.
problem Selecting hyperparameters for Lasso-based transfer learning algorithms.
method Asymptotic analysis using the replica method.
result Ignoring one type of transferred information has minimal impact on performance.
The paper simplifies quickshift hyperparameter tuning for larger images.
problem Understanding and tuning hyperparameters for quickshift image segmentation.
method Theoretical analysis of a modified quickshift algorithm for homogeneous patches with i.i.d. noise.
result A heuristic to scale quickshift hyperparameters based on image size.
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. W…
Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMBO (SMAC, TPE). The objective function of black-box optimization is non-smooth, or time-consuming to evaluate, or in some way noisy. Recent y…
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…
A framework schedules hyperparameters for model-based reinforcement learning, improving performance.
problem Inadequate scheduling of hyperparameters in model-based reinforcement learning.
method Theoretical analysis and AutoMBPO framework to automatically schedule real data ratio and other hyperparameters.
result Training with hyperparameters scheduled by AutoMBPO significantly improves performance.
Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian o…
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.
Statistical analysis of regularization in continual learning tasks.
problem Understanding how regularization affects model performance in sequential learning.
method Derivation of convergence rates, iterative update formula, and optimal hyperparameters for generalized ℓ2-regularization.
result Optimal hyperparameters balance forward and backward knowledge transfer, improving model performance.
The paper improves Gaussian process regression by optimizing hyperparameters.
problem Hyperparameter tuning for Gaussian process regression models.
method Adaptive sparse variational approximations using variational Bayes.
result Minimax optimal rates of convergence for variational posterior.
Hyper addresses the hyperparameter tuning challenge in RL, improving exploration efficiency and robustness.
problem Hyperparameter tuning is a significant challenge in RL, especially for curiosity-based exploration methods.
method Hyper robustly explores by effectively regularizing exploration visits and decoupling exploitation.
result Hyper is provably efficient and robust in various RL environments.
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize shared computing resources while supporting various HyperOpt algorithms. We incorporate conven…
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.
Sparse Gaussian process hyperparameters optimized using MCMC.
problem Hyperparameter uncertainty leads to biased estimates and underestimation of predictive uncertainty.
method Proposes an MCMC algorithm to sample from the hyperparameter posterior in sparse Gaussian process regression.
result Significantly improves sampling efficiency in the Gaussian likelihood case.
New method predicts and optimizes matrix recovery from noisy measurements.
problem Recovering rank-1 matrices from Gaussian measurements with noise.
method Stochastic prox-linear iterative algorithm with trajectory predictions.
result The method converges linearly with accurate predictions of error.
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 compiles ML algorithm performance benchmarks on OpenML datasets.
problem Finding optimal hyperparameters for ML algorithms efficiently.
method Generated benchmark data for 7 ML algorithms on 39 datasets, fixed hyperparameters before testing.
result Comprehensive dataset of ML algorithm performance sensitivity.
Reshuffling splits improves hyperparameter optimization's generalization performance.
problem Improving peak performance of machine learning models through better hyperparameter optimization.
method Reshuffling splits for every hyperparameter configuration improves generalization performance.
result Reshuffling leads to better generalization performance compared to fixed splits.
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.
Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.
problem Understanding the assumptions and dependencies in Bayesian hierarchical models.
method Demonstrates how canonical distributions and maximum entropy principles can be used to derive marginal priors in hierarchical models.
result Marginal priors in hierarchical models derived from maximum entropy principles have different constraints compared to the original priors.
Adversarial training shows promise as an approach for training models that are robust towards adversarial perturbation. In this paper, we explore some of the practical challenges of adversarial training. We present a sensitivity analysis that illustrates that the effectiveness of adversarial training hinges on the sett…
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.
The paper analyzes how hyperparameters affect SGD with momentum's convergence rate.
problem The role of hyperparameters in SGD with momentum's convergence rate.
method Theoretical analysis using a hyperparameters-dependent stochastic differential equation (hp-dependent SDE).
result The optimal linear rate of convergence depends on both the learning rate and the momentum coefficient.
The paper analyzes convergence rates of bilevel optimization algorithms and introduces a new stochastic algorithm.
problem Nonconvex-strongly-convex bilevel optimization problems in machine learning.
method Comprehensive convergence rate analysis for deterministic bilevel optimization using AID and ITD, and a novel stochastic algorithm stocBiO.
result Theoretical convergence rates for AID and ITD methods, and stocBiO's superior performance.
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.
New BO method optimizes functions efficiently even with unknown hyperparameters.
problem Inaccurate estimation of Gaussian process hyperparameters degrades BO performance.
method Exploits multi-armed bandit and novel training loss function for consistent hyperparameter estimation.
result Sub-linear convergence to global optimum with unknown hyperparameters.
We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compresse…
New method optimizes ML models under poisoned data, improving robustness.
problem Vulnerability of ML models to poisoned data attacks.
method Multiobjective bilevel optimization to consider hyperparameter learning and attack effects.
result Current approaches underestimate model robustness and regularization benefits.
We offer new formulas for European option pricing under tempered stable processes.
problem Pricing European options under tempered stable processes.
method Series expansions for tempered stable densities and European option prices.
result Our formulas are hyperparameter-free and competitive with traditional methods.
Adam's hyperparameters implicitly regularize solutions, penalizing or impeding loss gradients' norms.
problem Implicit regularization in Adam's hyperparameters and training stage.
method Backward error analysis and ODE approximations to study Adam's behavior.
result Adam's implicit regularization depends on hyperparameters and training stage, involving different norms.
An online learning framework for survival analysis with real-time adaptation.
problem Real-time adaptation to dynamic environments and censored data in survival analysis.
method Online Newton Step (ONS) for optimal second order online convex optimization.
result Logarithmic stochastic regret for ONS with adaptive aggregation method.
A simple self-supervised model for tensor RPCA using deep unfolding.
problem Tensor robust principal component analysis (RPCA) challenges in practical applications.
method Deep unfolding with only four hyperparameters.
result Competitive or superior performance compared to supervised methods, even in data-starved scenarios.
The study analyzes convergence of random-walk embeddings in graph theory.
problem Understanding the convergence behavior of random-walk based vertex embeddings.
method Theoretical analysis of convergence in single and double limits of N and L. result Proved convergence of vertex embeddings under weak assumptions and derived concentration bounds.
Improved sampling in generative models using CLDs with a hyperparameter.
problem Improving sampling performance in generative models.
method Extending Critically-damped Langevin Diffusions with a hyperparameter to control noise.
result Derivation of a novel upper bound on Wasserstein sampling error.
Regularisation improves ML classifier stability against poisoning attacks.
problem Poisoning attacks degrade ML algorithms' performance; current attacks ignore hyperparameters.
method Proposed a multiobjective bilevel optimisation problem to consider hyperparameter effects.
result L2 regularisation enhances learning algorithm stability and mitigates poisoning attacks.