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Yu He

@dransyhe.bsky.social

7 followers 9 following 7 posts

CS PhD student @Stanford | BA+MEng @Cambridge dransyhe.github.io

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Yu He @dransyhe.bsky.social · 13/07/2025

[6/n] This is joint work with my advisor @ellen-v.bsky.social . 📍 Don’t miss our poster: Tuesday 11:00–13:30 @ # E-3003 Come chat about NAR, primal-dual reasoning, and how neural networks can think like algorithms 🧠➡️⚙️

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Yu He @dransyhe.bsky.social · 13/07/2025

[5/n] We evaluate PDNAR and show: ✅ Strong generalization to larger & OOD instances across three NP-hard tasks (vertex cover, set cover, hitting set) ✅ Utility as algorithmically-informed embeddings on real-world tasks ✅ Performance gains as warm starts in commercial solvers 🚀🧩

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Yu He @dransyhe.bsky.social · 13/07/2025

[4/n] But we don’t stop at algorithm replication 🛑 We inject optimal supervision from small problem instances—solved efficiently by IP solvers—to surpass the performance of the algorithm it learns from! 🔥

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Yu He @dransyhe.bsky.social · 13/07/2025

[3/n] We align GNNs with primal-dual algorithms via a bipartite representation between primal/dual variables, and theoretically prove it retains performance guarantees 📊📚

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Yu He @dransyhe.bsky.social · 13/07/2025

[2/n] PDNAR is a general NAR framework for NP-hard problems, built on the primal-dual approximation paradigm—a powerful tool in both exact and approximate algorithm design 🔧📐

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Yu He @dransyhe.bsky.social · 13/07/2025

[1/n] What is NAR? It trains neural networks to simulate algorithmic executions, imbuing them with structured, algorithmic thinking on real-world data 🧮🤖 But... most NAR work focuses on polynomial-time problems. ❗️Real-world problems like facility location are NP-hard!

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Yu He @dransyhe.bsky.social · 13/07/2025

Our ✨spotlight paper✨ "Primal-Dual Neural Algorithmic Reasoning" is coming to #ICML2025! We bring Neural Algorithmic Reasoning (NAR) to the NP-hard frontier 💥 🗓 Poster session: Tuesday 11:00–13:30 📍 East Exhibition Hall A-B, # E-3003 🔗 openreview.net/pdf?id=iBpkz... 🧵

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