Search Results for author:
Found 65 papers, 35 papers with code
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80\% in accuracy and low computational cost.
auto-sktime: Automated Time Series Forecasting
The framework employs Bayesian optimization, to automatically construct pipelines from statistical, machine learning (ML) and deep neural network (DNN) models.
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.
AutoML in Heavily Constrained Applications
In this paper, we propose CAML, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand.
Structure in Reinforcement Learning: A Survey and Open Problems
We amalgamate these diverse methodologies under a unified framework, shedding light on the role of structure in the learning problem, and classify these methods into distinct patterns of incorporating structure.
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance.
Automated Machine Learning for Remaining Useful Life Predictions
Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required.
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years.
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets.
Hyperparameters in Reinforcement Learning and How To Tune Them
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting.
Learning Activation Functions for Sparse Neural Networks
By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15. 53%, 8. 88%, and 6. 33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.
AutoRL Hyperparameter Landscapes
Addressing an important open question on the legitimacy of such dynamic AutoRL approaches, we provide thorough empirical evidence that the hyperparameter landscapes strongly vary over time across representative algorithms from RL literature (DQN, PPO, and SAC) in different kinds of environments (Cartpole, Bipedal Walker, and Hopper) This supports the theory that hyperparameters should be dynamically adjusted during training and shows the potential for more insights on AutoRL problems that can be gained through landscape analyses.
Hyperparameter Optimization
Open-Ended Question Answering
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Hyperparameters in Contextual RL are Highly Situational
Although Reinforcement Learning (RL) has shown impressive results in games and simulation, real-world application of RL suffers from its instability under changing environment conditions and hyperparameters.
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems.
PI is back! Switching Acquisition Functions in Bayesian Optimization
We also compare this to a random schedule and round-robin selection of EI and PI.
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves.
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines.
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
Most approaches for this problem rely on pre-computed dataset meta-features and landmarking performances to capture the salient topology of the datasets and those topologies that the algorithms attend to.
Automated Dynamic Algorithm Configuration
The performance of an algorithm often critically depends on its parameter configuration.
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved.
Efficient Automated Deep Learning for Time Series Forecasting
In contrast to common NAS search spaces, we designed a novel neural architecture search space covering various state-of-the-art architectures, allowing for an efficient macro-search over different DL approaches.
$π$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
Practitioner Motives to Select Hyperparameter Optimization Methods
Advanced programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization, have high sample efficiency in reproducibly finding optimal hyperparameter values of machine learning (ML) models.
Contextualize Me -- The Case for Context in Reinforcement Learning
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents.
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
Searching in the Forest for Local Bayesian Optimization
Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO).
Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models.
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment.
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance.
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications.
Developing Open Source Educational Resources for Machine Learning and Data Science
It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS).
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.
Well-tuned Simple Nets Excel on Tabular Datasets
Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures.
Automatic Risk Adaptation in Distributional Reinforcement Learning
The use of Reinforcement Learning (RL) agents in practical applications requires the consideration of suboptimal outcomes, depending on the familiarity of the agent with its environment.
Distributional Reinforcement Learning
reinforcement-learning
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TempoRL: Learning When to Act
Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment.
Self-Paced Context Evaluation for Contextual Reinforcement Learning
Reinforcement learning (RL) has made a lot of advances for solving a single problem in a given environment; but learning policies that generalize to unseen variations of a problem remains challenging.
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models.
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance.
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural network to use and tuning the hyperparameters of the used training pipeline.
Regularization Cocktails
The regularization of prediction models is arguably the most crucial ingredient that allows Machine Learning solutions to generalize well on unseen data.
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
Neural Model-based Optimization with Right-Censored Observations
When fitting a regression model to predict the distribution of the outcomes, we cannot simply drop these right-censored observations, but need to properly model them.
Prior-guided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts.
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success.
Bayesian Optimization with a Prior for the Optimum
We show that BOPrO is around 6. 67x faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application.
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search.
Learning Heuristic Selection with Dynamic Algorithm Configuration
We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system.
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on parameter tuning.
Best Practices for Scientific Research on Neural Architecture Search
Finding a well-performing architecture is often tedious for both DL practitioners and researchers, leading to tremendous interest in the automation of this task by means of neural architecture search (NAS).
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning.
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
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.
Towards White-box Benchmarks for Algorithm Control
The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on tuned hyperparameter configurations.
Towards Automatically-Tuned Deep Neural Networks
Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks.
The Algorithm Selection Competitions 2015 and 2017
The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance.
Neural Networks for Predicting Algorithm Runtime Distributions
Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance.
Warmstarting of Model-based Algorithm Configuration
The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem.
Pitfalls and Best Practices in Algorithm Configuration
Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning).
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures.
A case study of algorithm selection for the traveling thief problem
Many real-world problems are composed of several interacting components.
ASlib: A Benchmark Library for Algorithm Selection
To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature.
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different types of instances of hard combinatorial problems.
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques.
Solver Scheduling via Answer Set Programming
Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances and is known to depend strongly on algorithm parameters.
