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Causal Bandits @ CLeaR 2024 | Part 2 | CausalBanditsPodcast.com

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Manage episode 447288112 series 3526805
Konten disediakan oleh Alex Molak. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Alex Molak atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

Send us a text

Which models work best for causal discovery and double machine learning?
In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California.
What you'll learn:
- Which causal discovery models perform best with their default hyperparameters?
- How to tune your double machine learning model?
- Does putting your paper on ArXiv early increase its chances of being accepted at a conference?
- How to deal with causal representation learning with multiple latent interventions?
Time codes:
00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery
08:52 Oliver Schacht - Hyperparameter Tuning for DML
14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance
18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
=============================
🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery.
https://www.youtube.com/@CausalPython/?sub_confirmation=1
✅ Stay Connected With Me.
👉Twitter (X): https://twitter.com/AleksanderMolak
👉Linkedin: https://www.linkedin.com/in/aleksandermolak/
👉Facebook: https://www.facebook.com/CausalPython
👉Instagram: https://www.instagram.com/alex.molak/
👉Tiktok: https://www.tiktok.com/@alex.molak
👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/
✅ For Business Inquiries: hello@causalpython.io
=============================
✅ About Causal Python with Alex Molak.
Welcome to my official YouTube channel, Causal Python, with Alex Molak. Dive into the fascinating world of Causal AI, unraveling the complexities of Causal Inference and Discovery with Python.
My content simplifies these intricate topics, making them accessible whether you’re starting or advancing your knowledge. Here, I explore the intersections of causality, AI, machine learning, optimization, and decision-making, all through Python’s versatile capabilities.
This is also the home of The Causal Bandits Podcast. For more insightful discussions, check out my Causal Bandit Podcast Website.
============

Support the show

Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4

  continue reading

27 episode

Artwork
iconBagikan
 
Manage episode 447288112 series 3526805
Konten disediakan oleh Alex Molak. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Alex Molak atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

Send us a text

Which models work best for causal discovery and double machine learning?
In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California.
What you'll learn:
- Which causal discovery models perform best with their default hyperparameters?
- How to tune your double machine learning model?
- Does putting your paper on ArXiv early increase its chances of being accepted at a conference?
- How to deal with causal representation learning with multiple latent interventions?
Time codes:
00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery
08:52 Oliver Schacht - Hyperparameter Tuning for DML
14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance
18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
=============================
🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery.
https://www.youtube.com/@CausalPython/?sub_confirmation=1
✅ Stay Connected With Me.
👉Twitter (X): https://twitter.com/AleksanderMolak
👉Linkedin: https://www.linkedin.com/in/aleksandermolak/
👉Facebook: https://www.facebook.com/CausalPython
👉Instagram: https://www.instagram.com/alex.molak/
👉Tiktok: https://www.tiktok.com/@alex.molak
👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/
✅ For Business Inquiries: hello@causalpython.io
=============================
✅ About Causal Python with Alex Molak.
Welcome to my official YouTube channel, Causal Python, with Alex Molak. Dive into the fascinating world of Causal AI, unraveling the complexities of Causal Inference and Discovery with Python.
My content simplifies these intricate topics, making them accessible whether you’re starting or advancing your knowledge. Here, I explore the intersections of causality, AI, machine learning, optimization, and decision-making, all through Python’s versatile capabilities.
This is also the home of The Causal Bandits Podcast. For more insightful discussions, check out my Causal Bandit Podcast Website.
============

Support the show

Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com
Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4

  continue reading

27 episode

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