Papers Hyperparameter Optimization
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Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences
Reliable and robust user identification and authentication are important and often necessary requirements for many digital services. It becomes paramount in social virtual reality (VR) to ensure trust, specifically in di…
Hyperparameter OptimizationUser IdentificationDynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations
Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For smaller data sets, an exhaustive search i…
Hyperparameter OptimizationThe Curse of Unrolling: Rate of Differentiating Through Optimization
Computing the Jacobian of the solution of an optimization problem is a central problem in machine learning, with applications in hyperparameter optimization, meta-learning, optimization as a layer, and dataset distillati…
Dataset DistillationHyperparameter OptimizationMeta-LearningRolling Shutter CorrectionScalable Gaussian Process Hyperparameter Optimization via Coverage Regularization
Gaussian processes (GPs) are Bayesian non-parametric models popular in a variety of applications due to their accuracy and native uncertainty quantification (UQ). Tuning GP hyperparameters is critical to ensure the valid…
Conformal PredictionGaussian ProcessesHyperparameter OptimizationPrediction+1BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual learning, and reinforcement learning. Co…
Bilevel OptimizationContinual LearningDeep LearningHyperparameter Optimization+2T3VIP: Transformation-based 3D Video Prediction
For autonomous skill acquisition, robots have to learn about the physical rules governing the 3D world dynamics from their own past experience to predict and reason about plausible future outcomes. To this end, we propos…
Hyperparameter OptimizationPredictionVideo PredictionSimple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models
Recent trends towards training ever-larger language models have substantially improved machine learning performance across linguistic tasks. However, the huge cost of training larger models can make tuning them prohibiti…
Hyperparameter OptimizationMachine TranslationNatural Language UnderstandingTranslationMulti-objective hyperparameter optimization with performance uncertainty
The performance of any Machine Learning (ML) algorithm is impacted by the choice of its hyperparameters. As training and evaluating a ML algorithm is usually expensive, the hyperparameter optimization (HPO) method needs …
GPRHyperparameter OptimizationBlack-box optimization for integer-variable problems using Ising machines and factorization machines
Black-box optimization has potential in numerous applications such as hyperparameter optimization in machine learning and optimization in design of experiments. Ising machines are useful for binary optimization problems …
Hyperparameter OptimizationAn Empirical Study on the Usage of Automated Machine Learning Tools
The popularity of automated machine learning (AutoML) tools in different domains has increased over the past few years. Machine learning (ML) practitioners use AutoML tools to automate and optimize the process of feature…
AutoMLFeature EngineeringHyperparameter OptimizationManagementTask Selection for AutoML System Evaluation
Our goal is to assess if AutoML system changes - i.e., to the search space or hyperparameter optimization - will improve the final model's performance on production tasks. However, we cannot test the changes on productio…
AutoMLHyperparameter OptimizationA Globally Convergent Gradient-based Bilevel Hyperparameter Optimization Method
Hyperparameter optimization in machine learning is often achieved using naive techniques that only lead to an approximate set of hyperparameters. Although techniques such as Bayesian optimization perform an intelligent s…
Bayesian OptimizationBilevel OptimizationHyperparameter OptimizationHyperparameter Optimization for Unsupervised Outlier Detection
Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels? In this work, we address this challenging hyperparameter optimization for unsup…
Hyperparameter OptimizationMeta-LearningOutlier DetectionThe Value of Out-of-Distribution Data
We expect the generalization error to improve with more samples from a similar task, and to deteriorate with more samples from an out-of-distribution (OOD) task. In this work, we show a counter-intuitive phenomenon: the …
Data AugmentationHyperparameter OptimizationHyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), incl…
Generative Adversarial NetworkHyperparameter OptimizationACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization
Deploying machine learning models requires high model quality and needs to comply with application constraints. That motivates hyperparameter optimization (HPO) to tune model configurations under deployment constraints. …
FairnessHyperparameter OptimizationHPO: We won't get fooled again
Hyperparameter optimization (HPO) is a well-studied research field. However, the effects and interactions of the components in an HPO pipeline are not yet well investigated. Then, we ask ourselves: can the landscape of H…
Hyperparameter OptimizationHPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little prog…
Hyperparameter OptimizationOpen Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critic…
Hyperparameter OptimizationTransfer LearningGradient-based Bi-level Optimization for Deep Learning: A Survey
Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta-knowledge extraction. Bi-level optimization embeds one pro…
Deep LearningHyperparameter OptimizationSurvey