208 citations · 504 across the 30 of their papers we have counts for
31 papers
SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents
Muhammad Shihab Rashid, Christian Bock, Yuan Zhuang +10
Coding agents powered by large language models have shown impressive capabilities in software engineering tasks, but evaluating their performance across diverse programming languag…
Hyperband-based Bayesian Optimization for Black-box Prompt Selection
Lennart Schneider, Martin Wistuba, Aaron Klein +3
Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks, especially in black-box settings where models are only accessible via…
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles +1
Recent Continual Learning (CL) methods have combined pretrained Transformers with prompt tuning, a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of pro…
Continual Learning with Low Rank Adaptation
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles +1
Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest. However, they struggle to retain that…
Renate: A Library for Real-World Continual Learning
Martin Wistuba, Martin Ferianc, Lukas Balles +2
Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indicati…
Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt +2
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperfo…
Scaling Laws for Hyperparameter Optimization
Arlind Kadra, Maciej Janowski, Martin Wistuba +1
Hyperparameter optimization is an important subfield of machine learning that focuses on tuning the hyperparameters of a chosen algorithm to achieve peak performance. Recently, the…
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
Ondrej Bohdal, Lukas Balles, Martin Wistuba +3
Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly…
Continual Learning with Transformers for Image Classification
Beyza Ermis, Giovanni Zappella, Martin Wistuba +2
In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without fo…
Memory Efficient Continual Learning with Transformers
Beyza Ermis, Giovanni Zappella, Martin Wistuba +2
In many real-world scenarios, data to train machine learning models becomes available over time. Unfortunately, these models struggle to continually learn new concepts without forg…
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Martin Wistuba, Arlind Kadra, Josif Grabocka
Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods. However, existing methods su…
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…
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi +3
Neural Architecture Search (NAS) methods have been growing in popularity. These techniques have been fundamental to automate and speed up the time consuming and error-prone process…
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Martin Wistuba, Josif Grabocka
Hyperparameter optimization (HPO) is a central pillar in the automation of machine learning solutions and is mainly performed via Bayesian optimization, where a parametric surrogat…
Learning to Rank Learning Curves
Martin Wistuba, Tejaswini Pedapati
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many dif…
How can AI Automate End-to-End Data Science?
Charu Aggarwal, Djallel Bouneffouf, Horst Samulowitz +9
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the r…
XferNAS: Transfer Neural Architecture Search
Martin Wistuba
The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computa…
A Survey on Neural Architecture Search
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architectu…
Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit
Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz +1
We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms. The historical observations can contain…
Inductive Transfer for Neural Architecture Optimization
Martin Wistuba, Tejaswini Pedapati
The recent advent of automated neural network architecture search led to several methods that outperform state-of-the-art human-designed architectures. However, these approaches ar…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…
Adversarial Robustness Toolbox v1.0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran +9
Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision…
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data
Martin Wistuba, Ambrish Rawat
We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians.…
Automated Image Data Preprocessing with Deep Reinforcement Learning
Tran Ngoc Minh, Mathieu Sinn, Hoang Thanh Lam +1
Data preparation, i.e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task. For…