Ellen Vitercik @ellen-v.bsky.social · 01/09/2026LLMs can rewrite your optimization problem to improve solve time, pass every test, and still silently change the problem. Our new paper introduces FLARE, an LLM agent that uses Lean to prove that a MILP reformulation is valid across all possible problem instances, not just those used for testing. 150
Ellen Vitercik @ellen-v.bsky.social · 26/08/2026LLMs can make optimization more accessible, but they must not sacrifice the guarantees that make solvers trustworthy. This was the focus of my part of our @ijcai.org tutorial, Large Language Models for Optimization, w/ Fei Liu & @lawlessopt.bsky.social. Materials: feiliu36.github.io/llm_opt_tuto... 052
Ellen Vitercik @ellen-v.bsky.social · 18/05/2026Can machine learning improve discrete optimization algorithms without sacrificing theoretical guarantees? This was the central question of the talk I gave this spring at a few schools (UCSD, UIC, Yale, Penn): Machine Learning for Discrete Optimization: Theoretical Foundations. (🧵 1/7) 171
Ellen Vitercik @ellen-v.bsky.social · 01/05/2026More time to submit to LAMP 2026! The deadline for spotlight talks, posters, and open problems has been extended to May 14. Learn more and submit: dravy.ttic.edu/lamp26.htmldravy.ttic.eduLAMP Workshop — ML-assisted theory 020
Reposted by Ellen VitercikTTIC @tticconnect.bsky.social · 27/04/2026Nina Balcan, Avrim Blum, Piotr Indyk & Ali Vakilian are organizing a #STOC2026 Workshop on Machine Learning for Algorithms, featuring tutorials, talks & a poster session. Learn more and submit your poster by June 1: buff.ly/JdQci1c 072
Ellen Vitercik @ellen-v.bsky.social · 22/04/2026We’re excited to announce the call for submissions for our workshop on Learning-driven Algorithms and Machine-aided Proofs (LAMP) at Toyota Technical Institute of Chicago (@tticconnect.bsky.social) on August 6–7. Huge thanks to my co-organizers Sandeep Silwal and Dravyansh Sharma. 160
Ellen Vitercik @ellen-v.bsky.social · 27/01/2026This week at the Innovations in Theoretical Computer Science (ITCS) conference, Mingwei Yang is presenting our paper: 𝗦𝗺𝗼𝗼𝘁𝗵𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗲𝘁𝗿𝗶𝗰 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮 𝗦𝗶𝗻𝗴𝗹𝗲 𝗦𝗮𝗺𝗽𝗹𝗲: 𝗕𝗲𝘆𝗼𝗻𝗱 𝗠𝗲𝘁𝗿𝗶𝗰 𝗗𝗶𝘀𝘁𝗼𝗿𝘁𝗶𝗼𝗻 by Yingxi Li, myself, and Mingwei Yang See Mingwei's talk here: youtu.be/yEBPI9c7OE8?...youtu.beITCS 2026 - Smoothed Analysis of Online Metric Matching with a Single SampleYouTube video by Mingwei Yang 160
Ellen Vitercik @ellen-v.bsky.social · 20/01/2026@lawlessopt.bsky.social and I are excited to present our #AAAI2026 tutorial on “LLMs for Optimization: Modeling, Solving, and Validating with Generative AI.” When: Tuesday, Jan 20, 2026, 8:30am–12:30pm SGT Where: Garnet 216 (Singapore EXPO) (Connor’s intro slides are shown here.) CC @aaai.org 181
Ellen Vitercik @ellen-v.bsky.social · 02/12/2025I’m excited to share the materials from my Stanford seminar course, “AI for Algorithmic Reasoning and Optimization”: vitercik.github.io/ai4algs_25/. It covered formal algorithmic frameworks for analyzing LLM reasoning, GNNs for combinatorial/mathematical optimization, and theoretical guarantees. 142
Ellen Vitercik @ellen-v.bsky.social · 16/11/2025Please keep an eye out for Connor Lawless (@lawlessopt.bsky.social) on the faculty job market! Connor is a Stanford Human-Centered AI Postdoc, co-hosted by myself and Madeleine Udell. His research combines ML, computational optimization, and HCI, with the goal of building human-centered AI systems. 161
Reposted by Ellen VitercikConnor Lawless @lawlessopt.bsky.social · 14/07/2025Excited to be chatting about our new paper "Understanding Fixed Predictions via Confined Regions" (joint work with @berkustun.bsky.social, Lily Weng, and Madeleine Udell) at #ICML2025! 🕐 Wed 16 Jul 4:30 p.m. PDT — 7 p.m. PDT 📍East Exhibition Hall A-B #E-1104 🔗 arxiv.org/abs/2502.16380arxiv.orgUnderstanding Fixed Predictions via Confined RegionsMachine learning models can assign fixed predictions that preclude individuals from changing their outcome. Existing approaches to audit fixed predictions do so on a pointwise basis, which requires ac... 153
Reposted by Ellen VitercikYu He @dransyhe.bsky.social · 13/07/2025Our ✨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... 🧵 162
Reposted by Ellen VitercikDivyarthi Mohan @divyarthi.bsky.social · 02/07/2025Join us for a Wikipedia edit-a-thon at #ACMEC25! When: July 8th, 8PM-10PM Where: Stanford Econ Landau 139 Website: sites.google.com/view/econcs-... Come hangout, grab snacks, and edit/create Wikipedia pages for EC topics. Suggest topics/articles that need attention: docs.google.com/spreadsheets... 1123
Reposted by Ellen VitercikConnor Lawless @lawlessopt.bsky.social · 16/03/2025Super excited about this new work with Yingxi Li, Anders Wikun, @ellen-v.bsky.social, and Madeleine Udell forthcoming at CPAIOR2025: LLMs for Cold-Start Cutting Plane Separator Configuration 🔗: arxiv.org/abs/2412.12038arxiv.orgLLMs for Cold-Start Cutting Plane Separator ConfigurationMixed integer linear programming (MILP) solvers ship with a staggering number of parameters that are challenging to select a priori for all but expert optimization users, but can have an outsized impa... 1115
Ellen Vitercik @ellen-v.bsky.social · 12/12/2024Pulled a shoulder muscle trying to stay cool on the golf course in front of my PhD students and postdoc 😅 🏌♀️ 0180
Reposted by Ellen VitercikGautam Kamath @gautamkamath.com · 10/12/2024📢 Join us at #NeurIPS2024 for an in-person Learning Theory Alliance mentorship event! 📅 When: Thurs, Dec 12 | 7:30-9:30 PM PST 🔥 What: Fireside chat w/ Misha Belkin (UCSD) on Learning Theory Research in the Era of LLMs, + mentoring tables w/ amazing mentors. Don’t miss it if you’re at NeurIPS! 092