Search Results for author: Horst Samulowitz

Found 26 papers, 1 papers with code

Choosing a Classical Planner with Graph Neural Networks

no code implementations25 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.

Enhancing In-context Learning via Linear Probe Calibration

1 code implementation22 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.

In-Context Learning

Matching Table Metadata with Business Glossaries Using Large Language Models

no code implementations8 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.


A Vision for Semantically Enriched Data Science

no code implementations2 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.

Common Sense Reasoning Data Augmentation +1

A Survey on Semantics in Automated Data Science

no code implementations16 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.

BIG-bench Machine Learning Common Sense Reasoning +2

FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning

no code implementations15 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).

Federated Learning

Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance

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.

Hyperparameter Optimization

AutoAI-TS: AutoAI for Time Series Forecasting

no code implementations24 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.

Benchmarking BIG-bench Machine Learning +3

How Much Automation Does a Data Scientist Want?

no code implementations7 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.

AutoML Marketing

Solving Constrained CASH Problems with ADMM

no code implementations17 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.

BIG-bench Machine Learning Fairness

Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit

no code implementations31 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.


An ADMM Based Framework for AutoML Pipeline Configuration

no code implementations1 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.

AutoML Binary Classification

Automating Predictive Modeling Process using Reinforcement Learning

no code implementations2 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.

Decision Making Decision Making Under Uncertainty +3

Neurology-as-a-Service for the Developing World

no code implementations16 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.

EEG Feature Engineering

Feature Engineering for Predictive Modeling using Reinforcement Learning

no code implementations21 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.

Automated Feature Engineering Efficient Exploration +3

An effective algorithm for hyperparameter optimization of neural networks

no code implementations23 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.

Hyperparameter Optimization

Automating Feature Engineering

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.

Automated Feature Engineering Feature Engineering

Selecting Near-Optimal Learners via Incremental Data Allocation

no code implementations31 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.

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