Search Results for author: Sihem Amer-Yahia

Found 7 papers, 2 papers with code

Eliciting Worker Preference for Task Completion

no code implementations10 Jan 2018 Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia

In this work, we believe that asking workers to indicate their preferences explicitly improve their experience in task completion and hence, the quality of their contributions.

Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls

no code implementations10 Sep 2019 Jose F. Rodrigues-Jr, Gabriel Spadon, Bruno Brandoli, Sihem Amer-Yahia

Our results demonstrate significant improvements in automated medical prognosis, as measured with Recall@k. We summarize our experience as a set of relevant insights for the design of Deep Learning architectures.

Trajectory Prediction

DropLeaf: a precision farming smartphone application for measuring pesticide spraying methods

no code implementations31 Aug 2020 Bruno Brandoli, Gabriel Spadon, Travis Esau, Patrick Hennessy, Andre C. P. L. Carvalho, Jose F. Rodrigues-Jr, Sihem Amer-Yahia

Pesticide application has been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades.

Guided Exploration of Data Summaries

no code implementations27 May 2022 Brit Youngmann, Sihem Amer-Yahia, Aurélien Personnaz

We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility.

Data Summarization

On Efficient Approximate Queries over Machine Learning Models

1 code implementation6 Jun 2022 Dujian Ding, Sihem Amer-Yahia, Laks VS Lakshmanan

Alternatively, under the Core Set Closure assumption, we develop two algorithms: CSC that efficiently returns high quality answers with high probability and minimal oracle usage, and CSE, which extends it to more general settings.

BIG-bench Machine Learning

A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs

1 code implementation20 Mar 2024 Yun Wang, Chrysanthi Kosyfaki, Sihem Amer-Yahia, Reynold Cheng

Experiments on real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling in terms of accuracy and time efficiency.

Graph Sampling

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