A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proposes a reinforcement learning framework for efficient hyper-parameter tuning of stochastic optimization algorithms.
Stage-based hyper-parameter optimization reduces GPU-hours and training time.
Hippo optimizes deep learning hyper-parameters by reducing redundant trials.
This work improves Bayesian Optimization for setting DNN hyper-parameters.
This paper proposes an efficient autoHPO method based on data-to-hyper-parameter mapping.
Two-tier approach optimizes RL hyper-parameters for better agent learning.
This paper explores hyperparameter optimization for machine learning models.
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
A new method selects algorithms and optimizes their hyper-parameters efficiently.
KD technique improves QDNN performance with reduced hyper-parameters.
Generative approach speeds hyperparameter tuning for machine learning models.
The quality of an induced model by a learning algorithm is dependent on the quality of the training data and the hyper-parameters supplied to the learning algorithm. Prior work has shown that improving the quality of the training data (i.e., by removing low quality instances) or tuning the learning algorithm hyper-para…
Paper proposes efficient GP hyper-parameter optimization methods.
BOFFIN TTS optimizes hyper-parameters for new speaker adaptation.
New method improves anomaly detection in acoustic signals.
This paper reviews hyper-parameter optimization methods for deep learning.
GeneCAI optimizes DNN compression hyper-parameters for mobile devices.
A new Randomized-Hyperopt method improves XGBoost hyperparameter tuning.
This paper aims to explore models based on the extreme gradient boosting (XGBoost) approach for business risk classification. Feature selection (FS) algorithms and hyper-parameter optimizations are simultaneously considered during model training. The five most commonly used FS methods including weight by Gini, weight b…
Paper proposes CNN-LSTM model for multi-speaker speech separation.
A framework for auto-tuning hyper-parameters in contextual bandit algorithms.
Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchica…
Study shows how network width affects SGD hyper-parameters and generalization.
This paper introduces an efficient method for optimizing deep learning hyperparameters.
In the regression setting, given a set of hyper-parameters, a model-estimation procedure constructs a model from training data. The optimal hyper-parameters that minimize generalization error of the model are usually unknown. In practice they are often estimated using split-sample validation. Up to now, there is an ope…
Optimal Word2Vec hyper-parameters improve NLP tasks.
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits,…
ABPS improves RL training efficiency by sharing policies and evolving hyper-params.
Paper proposes a new optimizer for faster nonconvex optimization.
Gradient boosting decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in many machine learning tasks. One relative downside to these models is the large number of hyper-parameters that they expose to the end-user. To maximize …
Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization bec…
P3O merges on-policy and off-policy updates without extra hyper-parameters.
Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of machine learning based systems when being installed for a specific task. However, auto…
Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to ac…
Meta-Semi learns to optimize SSL with minimal hyper-parameter tuning.
Unified Bayesian Optimization framework for model selection balancing effectiveness and training efficiency.
Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling and hyper-parameter optimization. Existing solutions attempt to adaptiv…
This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there are three hyper-parameters that have more influence on the best performance ach…
Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge…
This paper proposes an automatic neural network compression method.
A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or pri…
Much recent research has been conducted in the area of Bayesian learning, particularly with regard to the optimization of hyper-parameters via Gaussian process regression. The methodologies rely chiefly on the method of maximizing the expected improvement of a score function with respect to adjustments in the hyper-par…
We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tuning. In hyper-parameter tuning evaluating the black-box function at a point involves training a learning algorithm on a large data-set at a …
The objective of this research is to enhance performance of Stochastic Gradient Descent (SGD) algorithm in text classification. In our research, we proposed using SGD learning with Grid-Search approach to fine-tuning hyper-parameters in order to enhance the performance of SGD classification. We explored different setti…
New metaheuristic combines SGD and SA for hyper-parameter tuning.
PyKEEN 1.0 simplifies KGE model creation and optimization.
We solve a portfolio selection problem with four objectives, finding convex scalarizations for part of the Pareto front.
We present SEBOOST, a technique for boosting the performance of existing stochastic optimization methods. SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions. The method was inspired by the SESOP optimization method for large-scale problems, and has been ada…