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	<title>Statistics &#8211; Noise</title>
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	<description>The collective thoughts of the interwebz</description>
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		<title>Password reuse is rampant: nearly half of observed user logins are compromised</title>
		<link>https://noise.getoto.net/2025/03/17/password-reuse-is-rampant-nearly-half-of-observed-user-logins-are-compromised/</link>
		
		<dc:creator><![CDATA[Radwa Radwan]]></dc:creator>
		<pubDate>Mon, 17 Mar 2025 13:00:00 +0000</pubDate>
				<category><![CDATA[Account Takeover]]></category>
		<category><![CDATA[authentication]]></category>
		<category><![CDATA[bots]]></category>
		<category><![CDATA[Password-reuse]]></category>
		<category><![CDATA[Security Week]]></category>
		<category><![CDATA[Statistics]]></category>
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					<description><![CDATA[Nearly half of observed login attempts across websites protected by Cloudflare involved leaked credentials. The pervasive issue of password reuse is enabling automated bot attacks on a massive scale.]]></description>
		
		
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		<title>Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments</title>
		<link>https://noise.getoto.net/2024/08/26/improve-your-next-experiment-by-learning-better-proxy-metrics-from-past-experiments/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 26 Aug 2024 15:46:24 +0000</pubDate>
				<category><![CDATA[a-b-testing]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Statistics]]></category>
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					<description><![CDATA[By Aurélien Bibaut, Winston Chou, Simon Ejdemyr, and Nathan KallusWe are excited to share our work on how to learn good proxy metrics from historical experiments at KDD 2024. This work addresses a fundamental question for technology companies and acade...]]></description>
		
		
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		<item>
		<title>Grab Experiment Decision Engine &#8211; a Unified Toolkit for Experimentation</title>
		<link>https://noise.getoto.net/2024/04/09/grab-experiment-decision-engine-a-unified-toolkit-for-experimentation/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 09 Apr 2024 02:22:10 +0000</pubDate>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Econometrics]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[Python Package]]></category>
		<category><![CDATA[Statistics]]></category>
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					<description><![CDATA[Introduction

This article introduces the GrabX Decision Engine, an internal open-source package that offers a comprehensive framework for designing and analysing experiments conducted on online experiment platforms. The package encompasses a wide rang...]]></description>
		
		
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		<title>Sequential Testing Keeps the World Streaming Netflix Part 2: Counting Processes</title>
		<link>https://noise.getoto.net/2024/03/18/sequential-testing-keeps-the-world-streaming-netflix-part-2-counting-processes/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 18 Mar 2024 12:46:46 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[devops]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[software-development]]></category>
		<category><![CDATA[Statistics]]></category>
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					<description><![CDATA[Michael Lindon, Chris Sanden, Vache Shirikian, Yanjun Liu, Minal Mishra, Martin TingleyHave you ever encountered a bug while streaming Netflix? Did your title stop unexpectedly, or not start at all? In the first installment of this blog series on seque...]]></description>
		
		
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		<title>Sequential A/B Testing Keeps the World Streaming Netflix
Part 1: Continuous Data</title>
		<link>https://noise.getoto.net/2024/02/13/sequential-a-b-testing-keeps-the-world-streaming-netflixpart-1-continuous-data/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 13 Feb 2024 19:10:28 +0000</pubDate>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[experimentation]]></category>
		<category><![CDATA[software-development]]></category>
		<category><![CDATA[software-testing]]></category>
		<category><![CDATA[Statistics]]></category>
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					<description><![CDATA[Michael Lindon, Chris Sanden, Vache Shirikian, Yanjun Liu, Minal Mishra, Martin Tingley1. Spot the DifferenceCan you spot any difference between the two data streams below? Each observation is the time interval between a Netflix member hitting the play...]]></description>
		
		
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		<title>Building a Hyper Self-Service, Distributed Tracing and Feedback System for Rule &#038; Machine Learning (ML) Predictions</title>
		<link>https://noise.getoto.net/2021/05/24/building-a-hyper-self-service-distributed-tracing-and-feedback-system-for-rule-machine-learning-ml-predictions/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Mon, 24 May 2021 00:11:20 +0000</pubDate>
				<category><![CDATA[distributed-tracing]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Statistics]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/building-hyper-self-service-distributed-tracing-feedback-system</guid>

					<description><![CDATA[Introduction

In Grab, the Trust, Identity, Safety, and Security (TISS) is a team of software engineers and AI developers working on fraud detection, login identity check, safety issues, etc. There are many TISS services, like grab-fraud, grab-safety, ...]]></description>
		
		
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