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Data Skeptic

Data Skeptic

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.


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Fast and Frugal Time Series Forecasting

Fotios Petropoulos, Professor of Management Science at the University of Bath in The U.K., joins us today to talk about his work "Fast and Frugal Time Series Forecasting."

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Causal Inference in Educational Systems

Manie Tadayon, a PhD graduate from the ECE department at University of California, Los Angeles, joins us today to talk about his work ?Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach.?

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Boosted Embeddings for Time Series

Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work ?Boosted Embeddings for Time Series Forecasting.?

Works Mentioned
Boosted Embeddings for Time Series Forecasting
by Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr 

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Change Point Detection in Continuous Integration Systems

David Daly, Performance Engineer at MongoDB, joins us today to discuss "The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System".

Works Mentioned
The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System
by David Daly, William Brown, Henrik Ingo, Jim O?Leary, David BradfordSocial Media

David's Website
David's Twitter

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Applying k-Nearest Neighbors to Time Series

Samya Tajmouati, a PhD student in Data Science at the University of Science of Kenitra, Morocco, joins us today to discuss her work Applying K-Nearest Neighbors to Time Series Forecasting: Two New Approaches.

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Ultra Long Time Series

Dr. Feng Li, (@f3ngli) is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He joins us today to discuss his work Distributed ARIMA Models for Ultra-long Time Series.

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Angus Dempster, PhD Student at Monash University in Australia, comes on today to talk about MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification, a fast deterministic transform for time series classification. MINIROCKET reformulates ROCKET, gaining a 75x improvement on larger datasets with essentially the same performance. In this episode, we talk about the insights that realized this speedup as well as use cases.

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ARiMA is not Sufficient

Chongshou Li, Associate Professor at Southwest Jiaotong University in China, joins us today to talk about his work Why are the ARIMA and SARIMA not Sufficient.

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Comp Engine

Ben Fulcher, Senior Lecturer at the School of Physics at the University of Sydney in Australia, comes on today to talk about his project Comp Engine.

Follow Ben on Twitter: @bendfulcher
For posts about time series analysis : @comptimeseries

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Detecting Ransomware

Nitin Pundir, PhD candidate at University Florida and works at the Florida Institute for Cybersecurity Research, comes on today to talk about his work ?RanStop: A Hardware-assisted Runtime Crypto-Ransomware Detection Technique.?

FICS Research Lab - 

LinkedIn -

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GANs in Finance

Florian Eckerli, a recent graduate of Zurich University of Applied Sciences, comes on the show today to discuss his work Generative Adversarial Networks in Finance: An Overview.

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Predicting Urban Land Use

Today on the show we have Daniel Omeiza, a doctoral student in the computer science department of the University of Oxford, who joins us to talk about his work Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa.

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Opportunities for Skillful Weather Prediction

Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more from the Barnes Research Group on their site.

Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost!

In this interview, we explore the use of machine learning to help identify certain conditions under which the weather system has entered an unusually predictable position in it?s normally chaotic state space.

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Predicting Stock Prices

Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction.

We discuss the paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks.

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Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting.

Works Mentioned:
N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting
By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio

Social Media


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Translation Automation

Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness.

Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future. 

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Time Series at the Beach

Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work ?Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.?

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Automatic Identification of Outlier Galaxy Images

Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images.

Follow Lio on Twitter @shamir_lior

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Do We Need Deep Learning in Time Series

Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work ?Do We Really Need Deep Learning Models for Time Series Forecasting??

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Detecting Drift

Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time.

Check out Sam's IBM statistics/ML blog at:  
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Darts Library for Time Series

Julien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts. 

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Forecasting Principles and Practice

Welcome to Timeseries! Today?s episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices.

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Prequisites for Time Series

Today's experimental episode uses sound to describe some basic ideas from time series.

This episode includes lag, seasonality, trend, noise, heteroskedasticity, decomposition, smoothing, feature engineering, and deep learning.


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Orders of Magnitude

Today?s show in two parts. First, Linhda joins us to review the episodes from Data Skeptic: Pilot Season and give her feedback on each of the topics.

Second, we introduce our new segment ?Orders of Magnitude?. It?s a statistical game show in which participants must identify the true statistic hidden in a list of statistics which are off by at least an order of magnitude. Claudia and Vanessa join as our first contestants.  Below are the sources of our questions.


Bird Statistics

Birds in the US since 2000 Causes of Bird Mortality

Amounts of Data

Our statistics come from this post

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They're Coming for Our Jobs

AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Unless progress in AI inexplicably halts, the tasks done by humans vs. machines will continue to evolve. Today?s episode is a speculative conversation about what the future may hold.

Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us today! Kyle and Celestia discuss whether or not her jobs as a caricature artist or as an academic editor are under threat from AI automation.

Mentions The legendary Dr. Jorge Pérez and his work studying unicorns Supernormal stimulus International Society of Caricature Artists Two Heads Studios
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Pandemic Machine Learning Pitfalls

Today on the show Derek Driggs, a PhD Student at the University of Cambridge. He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.

Help us vote for the next theme of Data Skeptic!

Vote here:

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Flesch Kincaid Readability Tests

Given a document in English, how can you estimate the ease with which someone will find they can read it?  Does it require a college-level of reading comprehension or is it something a much younger student could read and understand?

While these questions are useful to ask, they don't admit a simple answer.  One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests.  These are simple calculations which provide you with a rough estimate of the reading ease.

In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective.

For empirical validation of these metrics, the plot below compares English language Wikipedia pages with "Simple English" Wikipedia pages.  The analysis Kyle describes in this episode yields the intuitively pleasing histogram below.  It summarizes the distribution of Flesch reading ease scores for 1000 pages examined from both Wikipedias.


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Fairness Aware Outlier Detection

Today on the show we have Shubhranshu Shekar, a Ph. D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.

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Life May be Rare

Today on the show Dr. Anders Sandburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work ?The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.?

Works Mentioned:

?The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.?by Andrew E Snyder-Beattie, Anders Sandberg, K Eric Drexler, Michael B Bonsall 


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Social Networks

Mayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley.

Works Mentioned
?Knowledge, Graphs, Fundamentals, Techniques and Applications?by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley

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The QAnon Conspiracy

QAnon is a conspiracy theory born in the underbelly of the internet.  While easy to disprove, these cryptic ideas captured the minds of many people and (in part) paved the way to the 2021 storming of the US Capital.

This is a contemporary conspiracy which came into existence and grew in a very digital way.  This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity.

This episode is not so much a debunking of this debunked theory, but rather an exploration of the metadata and origins of this conspiracy.

This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be.  This is the first installment.  In a few weeks, we're going to ask everyone to vote for their favorite theme for our next season.


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Benchmarking Vision on Edge vs Cloud

Karthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper "JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads"

Works Mentioned:
?JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.?

by: Karthick Shankar, Pengcheng Wang, Ran Xu, Ashraf Mahgoub, Somali ChaterjiSocial Media

Karthick Shankar

Somali Chaterji

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Goodhart's Law in Reinforcement Learning

Hal Ashton, a PhD student from the University College of London, joins us today to discuss a recent work Causal Campbell-Goodhart?s law and Reinforcement Learning.

"Only buy honey from a local producer." - Hal Ashton


Works Mentioned:

?Causal Campbell-Goodhart?s law and Reinforcement Learning?by Hal AshtonBook 

?The Book of Why?by Judea PearlPaper

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Video Anomaly Detection

Yuqi Ouyang, in his second year of PhD study at the University of Warwick in England, joins us today to discuss his work ?Video Anomaly Detection by Estimating Likelihood of Representations.?Works Mentioned:

Video Anomaly Detection by Estimating Likelihood of Representations
by: Yuqi Ouyang, Victor Sanchez

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Fault Tolerant Distributed Gradient Descent

Nirupam Gupta, a Computer Science Post Doctoral Researcher at EDFL University in Switzerland, joins us today to discuss his work ?Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.?


Works Mentioned:

Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
by Nirupam Gupta and Nitin H. Vaidya


Conference Details:

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Decentralized Information Gathering

Mikko Lauri, Post Doctoral researcher at the University of Hamburg, Germany, comes on the show today to discuss the work Information Gathering in Decentralized POMDPs by Policy Graph Improvements.

Follow Mikko: @mikko_lauri


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Leaderless Consensus

Balaji Arun, a PhD Student in the Systems of Software Research Group at Virginia Tech, joins us today to discuss his research of distributed systems through the paper ?Taming the Contention in Consensus-based Distributed Systems.? 

Works Mentioned
?Taming the Contention in Consensus-based Distributed Systems? 
by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindran

?Fast Paxos?
by Leslie Lamport

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Automatic Summarization

Maartje ter Hoeve, PhD Student at the University of Amsterdam, joins us today to discuss her research in automated summarization through the paper ?What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.? 

Works Mentioned 
?What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.?
by Maartje der Hoeve, Juilia Kiseleva, and Maarten de Rijke

[email protected]



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Brian Brubach, Assistant Professor in the Computer Science Department at Wellesley College, joins us today to discuss his work ?Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives".

Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives
by Brian Brubach, Aravind Srinivasan, and Shawn Zhao

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Even Cooperative Chess is Hard

Aside from victory questions like ?can black force a checkmate on white in 5 moves?? many novel questions can be asked about a game of chess. Some questions are trivial (e.g. ?How many pieces does white have?") while more computationally challenging questions can contribute interesting results in computational complexity theory.

In this episode, Josh Brunner, Master's student in Theoretical Computer Science at MIT, joins us to discuss his recent paper Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard.

Works Mentioned
Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard
by Josh Brunner, Erik D. Demaine, Dylan Hendrickson, and Juilian Wellman

1x1 Rush Hour With Fixed Blocks is PSPACE Complete
by Josh Brunner, Lily Chung, Erik D. Demaine, Dylan Hendrickson, Adam Hesterberg, Adam Suhl, Avi Zeff

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Consecutive Votes in Paxos

Eil Goldweber, a graduate student at the University of Michigan, comes on today to share his work in applying formal verification to systems and a modification to the Paxos protocol discussed in the paper Significance on Consecutive Ballots in Paxos.

Works Mentioned :
Previous Episode on Paxos

On the Significance on Consecutive Ballots in Paxos by: Eli Goldweber, Nuda Zhang, and Manos Kapritsos

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Visual Illusions Deceiving Neural Networks

Today on the show we have Adrian Martin, a Post-doctoral researcher from the University of Pompeu Fabra in Barcelona, Spain. He comes on the show today to discuss his research from the paper ?Convolutional Neural Networks can be Deceived by Visual Illusions.?

Works Mentioned in Paper:
?Convolutional Neural Networks can be Decieved by Visual Illusions.? by Alexander Gomez-Villa, Adrian Martin, Javier Vazquez-Corral, and Marcelo Bertalmio


Snake Illusions

Alex: @alviur

Adrian: @adriMartin13

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Earthquake Detection with Crowd-sourced Data

Have you ever wanted to hear what an earthquake sounds like? Today on the show we have Omkar Ranadive, Computer Science Masters student at NorthWestern University, who collaborates with Suzan van der Lee, an Earth and Planetary Sciences professor at Northwestern University, on the crowd-sourcing project Earthquake Detective. 

Email Links:
Suzan: [email protected] 
Omkar: [email protected]

Works Mentioned: 

Paper: Applying Machine Learning to Crowd-sourced Data from Earthquake Detective
by Omkar Ranadive, Suzan van der Lee, Vivan Tang, and Kevin Chao
Earthquake Detective:

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Byzantine Fault Tolerant Consensus

Byzantine fault tolerance (BFT) is a desirable property in a distributed computing environment. BFT means the system can survive the loss of nodes and nodes becoming unreliable. There are many different protocols for achieving BFT, though not all options can scale to large network sizes.

Ted Yin joins us to explain BFT, survey the wide variety of protocols, and share details about HotStuff.

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Alpha Fold

Kyle shared some initial reactions to the announcement about Alpha Fold 2's celebrated performance in the CASP14 prediction.  By many accounts, this exciting result means protein folding is now a solved problem.

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Arrow's Impossibility Theorem

Above all, everyone wants voting to be fair. What does fair mean and how can we measure it? Kenneth Arrow posited a simple set of conditions that one would certainly desire in a voting system. For example, unanimity - if everyone picks candidate A, then A should win!

Yet surprisingly, under a few basic assumptions, this theorem demonstrates that no voting system exists which can satisfy all the criteria.

This episode is a discussion about the structure of the proof and some of its implications.

Works Mentioned

A Difficulty in the Concept of Social Welfare by Kenneth J. Arrow   Three Brief Proofs of Arrows Impossibility Theorem by John Geanakoplos   Thank you to our sponsors!   Better Help is much more affordable than traditional offline counseling, and financial aid is available! Get started in less than 24 hours. Data Skeptic listeners get 10% off your first month when you visit:   Let Springboard School of Data jumpstart your data career! With 100% online and remote schooling, supported by a vast network of professional mentors with a tuition-back guarantee, you can't go wrong. Up to twenty $500 scholarships will be awarded to Data Skeptic listeners. Check them out at and enroll using code: DATASK
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Face Mask Sentiment Analysis

As the COVID-19 pandemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask-wearing via Twitter. What does this data tell us about public opinion? How does it vary by demographic? What, if anything, can make people change their minds?

Today we speak to, Neil Yeung and Jonathan Lai, Undergraduate students in the Department of Computer Science at the University of Rochester, and Professor of Computer Science, Jiebo-Luoto to discuss their recent paper. Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.

Works Mentioned

Neil Yeung
[email protected]

Jonathan Lia
[email protected]

Jiebo Luo
[email protected]

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Counting Briberies in Elections

Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper ?On the Robustness of Winners: Counting Briberies in Elections.?

Links Mentioned:

Works Mentioned:
?On the Robustness of Winners: Counting Briberies in Elections.? by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier

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Sybil Attacks on Federated Learning

Clement Fung, a Societal Computing PhD student at Carnegie Mellon University, discusses his research in security of machine learning systems and a defense against targeted sybil-based poisoning called FoolsGold.

Works Mentioned:
The Limitations of Federated Learning in Sybil Settings




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Differential Privacy at the US Census

Simson Garfinkel, Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau, discusses his work modernizing the Census Bureau disclosure avoidance system from private to public disclosure avoidance techniques using differential privacy. Some of the discussion revolves around the topics in the paper Randomness Concerns When Deploying Differential Privacy.  


?Calibrating Noise to Sensitivity in Private Data Analysis? by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith "Issues Encountered Deploying Differential Privacy" by Simson L Garfinkel, John M Abowd, and Sarah Powazek "Randomness Concerns When Deploying Differential Privacy" by Simson L. Garfinkel and Philip Leclerc 

Check out:

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