Thomas Parnell | DeepAI

DeepAI AI Chat

Featured Co-authors

Luca Benini
202 publications
Martin Jaggi
104 publications
Rachid Guerraoui
62 publications
Michael Kaufmann
34 publications
Abu Sebastian
28 publications
Celestine Mendler-Dünner
17 publications
Evangelos Eleftheriou
12 publications
Nikolas Ioannou
11 publications
Alessandro De Palma
8 publications
Kornilios Kourtis
8 publications
Diego Didona
8 publications

∙ 04/20/2022

Search-based Methods for Multi-Cloud Configuration

Multi-cloud computing has become increasingly popular with enterprises l...

0 Małgorzata Łazuka, et al. ∙

∙ 11/16/2020

Towards a General Framework for ML-based Self-tuning Databases

Machine learning (ML) methods have recently emerged as an effective way ...

0 Thomas Schmied, et al. ∙

∙ 06/17/2020

MixBoost: A Heterogeneous Boosting Machine

Modern gradient boosting software frameworks, such as XGBoost and LightG...

0 Thomas Parnell, et al. ∙

∙ 06/12/2020

Differentially Private Stochastic Coordinate Descent

In this paper we tackle the challenge of making the stochastic coordinat...

0 Georgios Damaskinos, et al. ∙

∙ 11/18/2019

SySCD: A System-Aware Parallel Coordinate Descent Algorithm

In this paper we propose a novel parallel stochastic coordinate descent ...

0 Nikolas Ioannou, et al. ∙

∙ 10/15/2019

Breadth-first, Depth-next Training of Random Forests

In this paper we analyze, evaluate, and improve the performance of train...

0 Andreea Anghel, et al. ∙

∙ 09/16/2019

Learning to Tune XGBoost with XGBoost

In this short paper we investigate whether meta-learning techniques can ...

0 Johanna Sommer, et al. ∙

∙ 09/16/2019

Weighted Sampling for Combined Model Selection and Hyperparameter Tuning

The combined algorithm selection and hyperparameter tuning (CASH) proble...

0 Dimitrios Sarigiannis, et al. ∙

∙ 09/11/2019

Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle

Distributed machine learning training is one of the most common and impo...

0 Michael Kaufmann, et al. ∙

∙ 06/08/2019

5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory

In-memory computing is an emerging computing paradigm that could enable ...

0 Martino Dazzi, et al. ∙

∙ 03/22/2019

Sampling Acquisition Functions for Batch Bayesian Optimization

This paper presents Acquisition Thompson Sampling (ATS), a novel algorit...

0 Alessandro De Palma, et al. ∙

∙ 11/06/2018

Elastic CoCoA: Scaling In to Improve Convergence

In this paper we experimentally analyze the convergence behavior of CoCo...

0 Michael Kaufmann, et al. ∙

∙ 11/05/2018

Parallel training of linear models without compromising convergence

In this paper we analyze, evaluate, and improve the performance of train...

0 Nikolas Ioannou, et al. ∙

∙ 09/12/2018

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Gradient boosted decision trees (GBDTs) have seen widespread adoption in...

0 Andreea Anghel, et al. ∙

∙ 03/16/2018

Snap Machine Learning

We describe an efficient, scalable machine learning library that enables...

0 Celestine Dünner, et al. ∙

∙ 11/20/2017

Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration

The amount of unstructured text-based data is growing every day. Queryin...

0 Kubilay Atasu, et al. ∙

∙ 08/17/2017

Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems

We propose a generic algorithmic building block to accelerate training o...

0 Celestine Dünner, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world