Louis Ohl
@louisohl.bsky.social
Postdoc @ Linköping University, STIMA division
oshillou.github.io
oshillou.github.io
On December 2nd, I’ll be at the ELLIS UnConference presenting at the Poster Session my paper "Discriminative ordering through ensemble consensus" [UAI2025]. Looking forward to interesting talks in Copenhagen!
Paper: openreview.net/forum?id=xjN...
Paper: openreview.net/forum?id=xjN...
October 27, 2025 at 8:33 AM
On December 2nd, I’ll be at the ELLIS UnConference presenting at the Poster Session my paper "Discriminative ordering through ensemble consensus" [UAI2025]. Looking forward to interesting talks in Copenhagen!
Paper: openreview.net/forum?id=xjN...
Paper: openreview.net/forum?id=xjN...
Reposted by Louis Ohl
« Can you train a standard classifier without labels ? » was the question we tried to investigate in this survey. Very happy to see @louisohl.bsky.social ‘s final PhD projet published in ACM CSUR!
Great news: our "Tutorial on discriminative clustering and mutual information" got upgraded from preprint to ACM Computing Surveys publication! (with @pamattei.bsky.social and Frédéric Precioso).
It is available on open access here: dl.acm.org/doi/10.1145/...
It is available on open access here: dl.acm.org/doi/10.1145/...
A Tutorial on Discriminative Clustering and Mutual Information | ACM Computing Surveys
To cluster data is to separate samples into distinctive groups that should ideally
have some cohesive properties. Today, numerous clustering algorithms exist, and their
differences lie essentially in ...
dl.acm.org
September 15, 2025 at 4:17 PM
« Can you train a standard classifier without labels ? » was the question we tried to investigate in this survey. Very happy to see @louisohl.bsky.social ‘s final PhD projet published in ACM CSUR!
Great news: our "Tutorial on discriminative clustering and mutual information" got upgraded from preprint to ACM Computing Surveys publication! (with @pamattei.bsky.social and Frédéric Precioso).
It is available on open access here: dl.acm.org/doi/10.1145/...
It is available on open access here: dl.acm.org/doi/10.1145/...
A Tutorial on Discriminative Clustering and Mutual Information | ACM Computing Surveys
To cluster data is to separate samples into distinctive groups that should ideally
have some cohesive properties. Today, numerous clustering algorithms exist, and their
differences lie essentially in ...
dl.acm.org
September 11, 2025 at 11:44 AM
Great news: our "Tutorial on discriminative clustering and mutual information" got upgraded from preprint to ACM Computing Surveys publication! (with @pamattei.bsky.social and Frédéric Precioso).
It is available on open access here: dl.acm.org/doi/10.1145/...
It is available on open access here: dl.acm.org/doi/10.1145/...
What a pleasure #UAI2025 has been! Great researchers, great talks. I'm looking forward to coming another time!
Thanks a lot for the organisation :-)
Thanks a lot for the organisation :-)
July 28, 2025 at 9:20 AM
What a pleasure #UAI2025 has been! Great researchers, great talks. I'm looking forward to coming another time!
Thanks a lot for the organisation :-)
Thanks a lot for the organisation :-)
Reposted by Louis Ohl
EurIPS is coming! 📣 Mark your calendar for Dec. 2-7, 2025 in Copenhagen 📅
EurIPS is a community-organized conference where you can present accepted NeurIPS 2025 papers, endorsed by @neuripsconf.bsky.social and @nordicair.bsky.social and is co-developed by @ellis.eu
eurips.cc
EurIPS is a community-organized conference where you can present accepted NeurIPS 2025 papers, endorsed by @neuripsconf.bsky.social and @nordicair.bsky.social and is co-developed by @ellis.eu
eurips.cc
July 16, 2025 at 10:01 PM
EurIPS is coming! 📣 Mark your calendar for Dec. 2-7, 2025 in Copenhagen 📅
EurIPS is a community-organized conference where you can present accepted NeurIPS 2025 papers, endorsed by @neuripsconf.bsky.social and @nordicair.bsky.social and is co-developed by @ellis.eu
eurips.cc
EurIPS is a community-organized conference where you can present accepted NeurIPS 2025 papers, endorsed by @neuripsconf.bsky.social and @nordicair.bsky.social and is co-developed by @ellis.eu
eurips.cc
New preprint!
Introducing "A tutorial on discriminative clustering and mutual information"
With @pamattei.bsky.social and Frédéric Precioso.
arxiv.org/abs/2505.04484
This preprint covers the history of discriminative clustering with pedagogical intents.
Introducing "A tutorial on discriminative clustering and mutual information"
With @pamattei.bsky.social and Frédéric Precioso.
arxiv.org/abs/2505.04484
This preprint covers the history of discriminative clustering with pedagogical intents.
A Tutorial on Discriminative Clustering and Mutual Information
To cluster data is to separate samples into distinctive groups that should ideally have some cohesive properties. Today, numerous clustering algorithms exist, and their differences lie essentially in ...
arxiv.org
May 12, 2025 at 8:19 AM
New preprint!
Introducing "A tutorial on discriminative clustering and mutual information"
With @pamattei.bsky.social and Frédéric Precioso.
arxiv.org/abs/2505.04484
This preprint covers the history of discriminative clustering with pedagogical intents.
Introducing "A tutorial on discriminative clustering and mutual information"
With @pamattei.bsky.social and Frédéric Precioso.
arxiv.org/abs/2505.04484
This preprint covers the history of discriminative clustering with pedagogical intents.
I'm glad to announce that our paper titled "Discriminative ordering through ensemble consensus" was accepted for a poster presentation at #uai2025.
In collaboration with Fredrik Lindsten.
Preprint: arxiv.org/abs/2505.04464
How to compare sets of very different clustering algorithms? (1/5)
In collaboration with Fredrik Lindsten.
Preprint: arxiv.org/abs/2505.04464
How to compare sets of very different clustering algorithms? (1/5)
Discriminative Ordering Through Ensemble Consensus
Evaluating the performance of clustering models is a challenging task where the outcome depends on the definition of what constitutes a cluster. Due to this design, current existing metrics rarely han...
arxiv.org
May 9, 2025 at 6:40 AM
I'm glad to announce that our paper titled "Discriminative ordering through ensemble consensus" was accepted for a poster presentation at #uai2025.
In collaboration with Fredrik Lindsten.
Preprint: arxiv.org/abs/2505.04464
How to compare sets of very different clustering algorithms? (1/5)
In collaboration with Fredrik Lindsten.
Preprint: arxiv.org/abs/2505.04464
How to compare sets of very different clustering algorithms? (1/5)
Had a blast today discussing and tracing the evolution of discriminative clustering methods at the Pioneer Centre for AI @aicentre.dk in Copenhagen. Lots of interesting talks and perspectives; thanks for the invitation!
📸: @jesfrellsen.bsky.social
📸: @jesfrellsen.bsky.social
February 21, 2025 at 11:29 PM
Had a blast today discussing and tracing the evolution of discriminative clustering methods at the Pioneer Centre for AI @aicentre.dk in Copenhagen. Lots of interesting talks and perspectives; thanks for the invitation!
📸: @jesfrellsen.bsky.social
📸: @jesfrellsen.bsky.social
Happy to announce that we released a new version of GemClus: v1.1.0.
It now includes:
- Compatibility with latest numpy
- A novel gemini based on the chi2 divergence
- An improved introductory documentation for anyone new to the concept
Check it out: gemini-clustering.github.io
It now includes:
- Compatibility with latest numpy
- A novel gemini based on the chi2 divergence
- An improved introductory documentation for anyone new to the concept
Check it out: gemini-clustering.github.io
Welcome to GemClus documentation! — gemclus 1.1.0 documentation
gemini-clustering.github.io
February 13, 2025 at 2:47 PM
Happy to announce that we released a new version of GemClus: v1.1.0.
It now includes:
- Compatibility with latest numpy
- A novel gemini based on the chi2 divergence
- An improved introductory documentation for anyone new to the concept
Check it out: gemini-clustering.github.io
It now includes:
- Compatibility with latest numpy
- A novel gemini based on the chi2 divergence
- An improved introductory documentation for anyone new to the concept
Check it out: gemini-clustering.github.io