Fair Hierarchical Clustering
Data Skeptic28 Mar 2022

Fair Hierarchical Clustering

Building a fair machine learning model has become a critical consideration in today's world. In this episode, we speak with Anshuman Chabra, a Ph.D. candidate in Computer Networks. Chhabra joins us to discuss his research on building fair machine learning models and why it is important. Find out how he modeled the problem and the result found.

Click here to access additional show notes on our webiste!

Thanks to our sponsor!
https://astrato.io

Astrato is a modern BI and analytics platform built for the Snowflake Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(601)

Student Spotlight: Aaron Payne, Data Analyst

Student Spotlight: Aaron Payne, Data Analyst

Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They...

1 Mai 25min

The Future is Agentic in Recommender Systems

The Future is Agentic in Recommender Systems

Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness mat...

25 Apr 49min

Book Ratings and Recommendations

Book Ratings and Recommendations

Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often mat...

27 Mar 39min

Disentanglement and Interpretability in Recommender Systems

Disentanglement and Interpretability in Recommender Systems

Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommender systems, finding that while disentanglement strongly corre...

10 Mar 30min

Collective Altruism in Recommender Systems

Collective Altruism in Recommender Systems

Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research r...

27 Feb 54min

Niche vs Mainstream

Niche vs Mainstream

Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and niche algorithms. His research shows spec...

18 Feb 34min

Healthy Friction in Job Recommender Systems

Healthy Friction in Job Recommender Systems

In this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable multi-stakeholder recommender systems for job recruitment. Roa...

2 Feb 26min

Fairness in PCA-Based Recommenders

Fairness in PCA-Based Recommenders

In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enter...

26 Jan 49min

Populært innen Vitenskap

fastlegen
jss
tingenes-tilstand
forskningno
rekommandert
rss-paradigmepodden
villmarksliv
sinnsyn
rss-zahid-ali-hjelper-deg
vett-og-vitenskap-med-gaute-einevoll
kvinnehelsepodden
rss-inn-til-kjernen-med-sunniva-rose
nordnorsk-historie
nevropodden
tidlose-historier
liberal-halvtime
fjellsportpodden
rss-overskuddsliv
grunnstoffene
rss-rekommandert