Search Results for author:
Found 26 papers, 1 papers with code
Choosing a Classical Planner with Graph Neural Networks
Thus, the ability to predict their performance on a given problem is of great importance.
Enhancing In-context Learning via Linear Probe Calibration
However, applying ICL in real cases does not scale with the number of samples, and lacks robustness to different prompt templates and demonstration permutations.
Single-shot General Hyper-parameter Optimization for Federated Learning
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Matching Table Metadata with Business Glossaries Using Large Language Models
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.
LakeBench: Benchmarks for Data Discovery over Data Lakes
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
A Vision for Semantically Enriched Data Science
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.
A Survey on Semantics in Automated Data Science
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models.
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance
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.
AutoAI-TS: AutoAI for Time Series Forecasting
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.
How Much Automation Does a Data Scientist Want?
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
Solving Constrained CASH Problems with ADMM
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.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit
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
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.
Automating Predictive Modeling Process using Reinforcement Learning
Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
Neurology-as-a-Service for the Developing World
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.
Feature Engineering for Predictive Modeling using Reinforcement Learning
It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target.
An effective algorithm for hyperparameter optimization of neural networks
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.
Learning Feature Engineering for Classification
Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space.
Automating Feature Engineering
Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model.
Cognito: Automated Feature Engineering for Supervised Learning
In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised learning.
Selecting Near-Optimal Learners via Incremental Data Allocation
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers.
