no code implementations • 25 Jan 2024 • Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, Michael Katz
Thus, the ability to predict their performance on a given problem is of great importance.
1 code implementation • 22 Jan 2024 • Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
However, applying ICL in real cases does not scale with the number of samples, and lacks robustness to different prompt templates and demonstration permutations.
no code implementations • ICLR 2023 • Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
no code implementations • 8 Sep 2023 • Elita Lobo, Oktie Hassanzadeh, Nhan Pham, Nandana Mihindukulasooriya, Dharmashankar Subramanian, Horst Samulowitz
The resulting matching enables the use of an available or curated business glossary for retrieval and analysis without or before requesting access to the data contents.
no code implementations • 9 Jul 2023 • Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
no code implementations • 2 Mar 2023 • Udayan Khurana, Kavitha Srinivas, Sainyam Galhotra, Horst Samulowitz
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection.
no code implementations • 16 May 2022 • Udayan Khurana, Kavitha Srinivas, Horst Samulowitz
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models.
no code implementations • 16 Feb 2022 • Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
no code implementations • 15 Dec 2021 • Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
no code implementations • ICML Workshop AutoML 2021 • Parikshit Ram, Alexander G. Gray, Horst Samulowitz
The tradeoffs in the excess risk incurred from data-driven learning of a single model has been studied by decomposing the excess risk into approximation, estimation and optimization errors.
no code implementations • 24 Feb 2021 • Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculin, Petros Zerfos
We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset.
no code implementations • 7 Jan 2021 • Dakuo Wang, Q. Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, Lisa Amini
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
no code implementations • 17 Jun 2020 • Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi, Dakuo Wang, Djallel Bouneffouf, Greg Bramble, Horst Samulowitz, Alexander G. Gray
The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.
no code implementations • 22 Oct 2019 • Charu Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander Gray
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
no code implementations • 5 Sep 2019 • Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, Alexander Gray
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
no code implementations • 31 May 2019 • Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistub
We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms.
no code implementations • 1 May 2019 • Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.
no code implementations • 2 Mar 2019 • Udayan Khurana, Horst Samulowitz
Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction.
no code implementations • 17 Jan 2019 • Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
no code implementations • 16 Nov 2017 • Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels
In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.
no code implementations • 21 Sep 2017 • Udayan Khurana, Horst Samulowitz, Deepak Turaga
It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target.
no code implementations • 23 May 2017 • Gonzalo Diaz, Achille Fokoue, Giacomo Nannicini, Horst Samulowitz
This paper addresses the problem of choosing appropriate parameters for the NN by formulating it as a box-constrained mathematical optimization problem, and applying a derivative-free optimization tool that automatically and effectively searches the parameter space.
no code implementations • IJCAI 2017 2017 • Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B. Khalil, Deepak Turaga
Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space.
no code implementations • NIPS 2016 2016 • Udayan Khurana, Fatemeh Nargesian, Horst Samulowitz, Elias Khalil, Deepak Turaga
Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model.
no code implementations • ICDMW 2016 2016 • Udayan Khurana, Deepak Turaga, Horst Samulowitz, Srinivasan Parthasrathy
In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised learning.
no code implementations • 31 Dec 2015 • Ashish Sabharwal, Horst Samulowitz, Gerald Tesauro
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers.