qcappart

Associate professor at @polymtl • Ph.D. from @UCLouvain_be • Researcher on artificial intelligence, combinatorial optimization, and machine learning

[Alexandre Lacoste @alex_lacoste_]

We’re really excited to release this large collaborative work for unifying web agent benchmarks under the same roof.

In this TMLR paper, we dive in-depth into #BrowserGym and #AgentLab. We also present some unexpected performances from Claude 3.5-Sonnet

[Alexandre Lacoste @alex_lacoste_]

How capable are web agents at solving knowledge work tasks? 🤔 Are LLMs up to the challenge? 🤖

Introducing WorkArena: a benchmark where agents meet the world 𝘸𝘪𝘭𝘥 web of enterprise software 🌐🖥️

Paper: https://t.co/9WvM7xOWyC
Website: https://t.co/lfNj3LnzmV

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[Petar Veličković @PetarV_93]

I am simultaneously excited & exhausted to announce that, 2 years later, our survey on GNNs for CO has been published @JmlrOrg (now at 61 pages of length!!!) 😅🚀

https://t.co/DBklZQPOAm

Persistence paid off, guys! 😊 @chrsmrrs @69alodi @lyeskhalil @qcappart @didier_chetelat

[DTAI @DTAI_KULeuven]

Are you looking for a summer school on the integration of constraint programming and machine learning? See all details of our ACP summer school: https://t.co/YPR0zSrJ24
📅 When? 10-14/07/23
🏰 Where? Leuven, Belgium
✍️Registration: https://t.co/xfpeOT8s7w

[Thibaut Vidal @vidalthi]

Call for applications: new MSc, PhD, and postdoc positions opening at the SCALE-AI chair in @polymtl! We conduct research on hybrid OR/ML algorithms and interpretable #MachineLearning. Can you relay this announcement to your network?
https://t.co/pGKiA3kUB6

[Christopher Morris @ ICML 2026 @chrsmrrs]

If you are interested in ML for combinatorial optimization, checkout out our short write-up of the results of the ML4CO NeurIPS competition: https://t.co/f1cvVLg7b5. It also includes summaries of the winning approaches.

[Michael Galkin @michael_galkin]

🎄 A new winter holiday longread - what happened in the Graph ML field in 2021? What to expect in 2022? We reflect upon a dozen of research trends - brought by yours truly, @epsiloncorrect and @SergeyI49013776 (and a surprise cameo appearance in the end)

https://t.co/lbKryKU16B

[Thibaut Vidal @vidalthi]

New MSc, Ph.D., and postdoc positions are opening at the SCALE-AI Chair on Data-Driven Supply Chains at @polymtl. Research topics on #ORMS, #optimization and interpretable #MachineLearning. Can you relay this announcement to your network?
https://t.co/u3f3A0k3CA

[Sebastian Pokutta @spokutta]

Brand-new #NeurIPS2021 #competition about "Machine Learning for Discrete Optimization" - see below for more.

Please RT so that we can reach a broad set of people.

https://t.co/ESAoVhJgmk

#ml #ai @Montreal_AI #optimization #integerprogramming #ml4co

[Yuandong Tian @tydsh]

[1/3] The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims at improving state-of-the-art combinatorial optimization solvers by replacing / integrating key heuristic components with machine learning models.

[L.-M. Rousseau @LM_Rousseau]

Thanks to @qcappart and our great team from both Polytechnique (MTL and Paris), we are finally releasing SeaPearl, our open-source RL-guided CP solver (CP-RL) in Julia. All details are available on our new website. https://t.co/14d00X26zK

[Petar Veličković @PetarV_93]

A short version of our GNN+CO survey was just accepted by @IJCAIconf #IJCAI2021 in the survey track!

Looking forward to presenting our work in (virtual) Montréal ✨

[Petar Veličković @PetarV_93]

If you're interested in GNNs for combinatorial tasks (certainly an exciting time!), we've released our 43-page comprehensive survey on the area! + detailed blueprint of algorithmic reasoning in S3.3.

https://t.co/F4TG4svKMG

with @chrsmrrs @69alodi @lyeskhalil @qcappart & Didier