I
I
Call for Open Problems
We invite proposals for talks that outline a key open problem in sampling and probabilistic inference in any area of the natural sciences.
Selected speakers will:
Present a 15-minute talk at the workshop.
Be invited to contribute to a post-workshop publication on open problems in sampling.
This is a unique opportunity to shape the dialogue between natural sciences and machine learning.
You can submit your work via OpenReview, selecting the Open Problems track for your submission.
Submission Guidelines
What are relevant domains?
Relevant domains include (but are not limited to):
- Biology & Genomics: Protein folding, gene regulatory networks, single-cell data analysis
- Physics & Cosmology: New physics searches at the LHC, cosmological parameter inference, quantum field theory
- Chemistry & Materials Science: Molecular design, catalyst discovery
- Neuroscience: Brain network modeling, neural decoding
- Earth & Climate Science: Climate model uncertainty quantification, extreme weather prediction
What are relevant domains?
Relevant domains include (but are not limited to):
- Biology & Genomics: Protein folding, gene regulatory networks, single-cell data analysis
- Physics & Cosmology: New physics searches at the LHC, cosmological parameter inference, quantum field theory
- Chemistry & Materials Science: Molecular design, catalyst discovery
- Neuroscience: Brain network modeling, neural decoding
- Earth & Climate Science: Climate model uncertainty quantification, extreme weather prediction
What are relevant domains?
Relevant domains include (but are not limited to):
- Biology & Genomics: Protein folding, gene regulatory networks, single-cell data analysis
- Physics & Cosmology: New physics searches at the LHC, cosmological parameter inference, quantum field theory
- Chemistry & Materials Science: Molecular design, catalyst discovery
- Neuroscience: Brain network modeling, neural decoding
- Earth & Climate Science: Climate model uncertainty quantification, extreme weather prediction
What makes a strong proposal?
- The Scientific Context: What is the fundamental scientific question you are trying to answer? Why is it important?
- Current Approaches: What are the current state-of-the-art methods being used to address this problem?
- Key Computational Bottlenecks: Where do current methods fall short? What are the primary computational or inferential challenges (e.g., high dimensionality, complex posterior geometries, expensive likelihood evaluations)?
- Opportunities for Progress: How could advancement in probabilistic inference and sampling techniques from the machine learning community help overcome these bottlenecks and accelerate scientific discovery?
What makes a strong proposal?
- The Scientific Context: What is the fundamental scientific question you are trying to answer? Why is it important?
- Current Approaches: What are the current state-of-the-art methods being used to address this problem?
- Key Computational Bottlenecks: Where do current methods fall short? What are the primary computational or inferential challenges (e.g., high dimensionality, complex posterior geometries, expensive likelihood evaluations)?
- Opportunities for Progress: How could advancement in probabilistic inference and sampling techniques from the machine learning community help overcome these bottlenecks and accelerate scientific discovery?
What makes a strong proposal?
- The Scientific Context: What is the fundamental scientific question you are trying to answer? Why is it important?
- Current Approaches: What are the current state-of-the-art methods being used to address this problem?
- Key Computational Bottlenecks: Where do current methods fall short? What are the primary computational or inferential challenges (e.g., high dimensionality, complex posterior geometries, expensive likelihood evaluations)?
- Opportunities for Progress: How could advancement in probabilistic inference and sampling techniques from the machine learning community help overcome these bottlenecks and accelerate scientific discovery?
Who should submit?
We strongly encourage submissions from domain experts in the natural sciences who are eager to engage with the machine learning community. We are particularly interested in problems that present novel challenges for existing ML methods.
Who should submit?
We strongly encourage submissions from domain experts in the natural sciences who are eager to engage with the machine learning community. We are particularly interested in problems that present novel challenges for existing ML methods.
Who should submit?
We strongly encourage submissions from domain experts in the natural sciences who are eager to engage with the machine learning community. We are particularly interested in problems that present novel challenges for existing ML methods.
What is the submission format?
- Length: 2 pages maximum (NeurIPS style format). An appendix of any length may be included, but reviewers are not required to read it.
- Format: PDF only. Please use the official NeurIPS 2025 template.
- Review Process: Submissions will be reviewed by the workshop's program committee. Selected proposals will be invited for 15-minute talks during the workshop.
What is the submission format?
- Length: 2 pages maximum (NeurIPS style format). An appendix of any length may be included, but reviewers are not required to read it.
- Format: PDF only. Please use the official NeurIPS 2025 template.
- Review Process: Submissions will be reviewed by the workshop's program committee. Selected proposals will be invited for 15-minute talks during the workshop.
What is the submission format?
- Length: 2 pages maximum (NeurIPS style format). An appendix of any length may be included, but reviewers are not required to read it.
- Format: PDF only. Please use the official NeurIPS 2025 template.
- Review Process: Submissions will be reviewed by the workshop's program committee. Selected proposals will be invited for 15-minute talks during the workshop.
Important Dates
Submission Portal Open: 22 July, 2025: OpenReview
Submission Deadline: 22 August, 2025
Acceptance Notification: 22 September, 2025
Workshop Date & Location: 6 or 7 December, 2025