35 citations · 55 across the 7 of their papers we have counts for
9 papers
Zero-Shot AutoML with Pretrained Models
Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa +3
Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, par…
Improving Hyperparameter Optimization by Planning Ahead
Hadi S. Jomaa, Jonas Falkner, Lars Schmidt-Thieme
Hyperparameter optimization (HPO) is generally treated as a bi-level optimization problem that involves fitting a (probabilistic) surrogate model to a set of observed hyperparamete…
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Sebastian Pineda Arango, Hadi S. Jomaa, Martin Wistuba +1
Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evalu…
Hyperparameter Optimization with Differentiable Metafeatures
Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
Metafeatures, or dataset characteristics, have been shown to improve the performance of hyperparameter optimization (HPO). Conventionally, metafeatures are precomputed and used to…
Do We Really Need Deep Learning Models for Time Series Forecasting?
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed +2
Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and…
Handwritten Amharic Character Recognition Using a Convolutional Neural Network
Mesay Samuel Gondere, Lars Schmidt-Thieme, Abiot Sinamo Boltena +1
Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant i…
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme
Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparame…
In Hindsight: A Smooth Reward for Steady Exploration
Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme
In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation as an update, in an attempt to estimate the actio…
Dataset2Vec: Learning Dataset Meta-Features
Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven appr…