Search Results for author: Nesime Tatbul

Found 10 papers, 3 papers with code

Class-Weighted Evaluation Metrics for Imbalanced Data Classification

no code implementations12 Oct 2020 Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task.

Classification General Classification

Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series

1 code implementation10 Oct 2020 Vincent Jacob, Fei Song, Arnaud Stiegler, Bijan Rad, Yanlei Diao, Nesime Tatbul

Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many experimental research domains.

Anomaly Detection Time Series

LISA: Towards Learned DNA Sequence Search

no code implementations10 Oct 2019 Darryl Ho, Jialin Ding, Sanchit Misra, Nesime Tatbul, Vikram Nathan, Vasimuddin Md, Tim Kraska

Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.

The Three Pillars of Machine Programming

no code implementations20 Mar 2018 Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B. Tenenbaum, Tim Mattson

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research.

BIG-bench Machine Learning

Precision and Recall for Time Series

4 code implementations NeurIPS 2018 Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich

Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time.

Anomaly Detection General Classification +1

Precision and Recall for Range-Based Anomaly Detection

no code implementations9 Jan 2018 Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik

Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point.

Anomaly Detection

Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection

no code implementations9 Jan 2018 Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik

This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.

Anomaly Detection BIG-bench Machine Learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.