| 09:00 |
09:15 |
Introduction and Opening |
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| 09:15 |
10:00 |
Keynote 1: James Zou (Stanford U) - Data scaling laws and data attribution for generative AI
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| 10:00 |
10:35 |
Invited Talk 1: Abdulmotaleb El Saddik (U Ottawa) |
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| 10:35 |
11:00 |
Coffee break / networking break |
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| 11:00 |
12:00 |
Panel 1: Data 2024: What are the important research questions for the DMLR community in light of foundation models?
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| 12:00 |
1:00 |
Lunch Break |
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| 1:00 |
2:00 |
Poster session |
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| 2:00 |
2:35 |
Announcements |
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- DataPerf & Dynabench |
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- Challenge results |
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- AI for Good |
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- Update on DMLR Journal |
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- Croissant data format |
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- Common Crawl |
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| 2:35 |
3:00 |
Coffee break / networking break |
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| 3:00 |
4:00 |
Panel 2: Generative AI for Good: What are the savior applications of generative AI and what data needs do they have?
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| 4:00 |
4:45 |
Keynote 2: Baharan Mirzasoleiman (UCLA) - Efficient Pre-training on Massive Datasets: A Data Perspective
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| 4:45 |
5:20 |
Invited Talk 2: Chelsea Finn (Stanford U) - Rethinking Supervision for Machine Learning Models
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| 5:20 |
5:35 |
Concluding Remarks |
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| 7:00 |
9:00 |
Post-workshop social event |
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