Ellen Vitercik
Assistant Professor of Management Sciences and Engineering and of Computer Science
Studies machine learning for algorithm design and discrete optimization.
Ellen Vitercik's lab at Stanford works on learning theory & optimization. 21 papers since 2023, 16 at top venues, mostly ICML and NeurIPS. 5 PhD students. 3 alumni. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 76%
- PhD students
- 5
- MS students
- 0
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 5 · collecting
- 9 papers2025 · T1 7
- 4 papers2024 · T1 4
- 1 papers2023 · T1 0
T1 venuesOtherVenues: ICML, NeurIPS, Journal of the ACM, Operations Research, EC
Research focuspaper titles that use each topic's words
- Learning theory & optimization5 papers
- algorithm1 paper
- algorithm design0 papers
- discrete0 papers
- studies machine0 papers
Paper list
From the lab homepage.
20267 papers
- ICML (T1)Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures
- NeurIPS (T1)Compositional Reasoning in Language Models under Reinforcement Learning Post-Training
- NeurIPS (T1)FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving
- ICML (T1)Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking
- Operations Research (T1)Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
- AI4MathNeural Algorithmic Reasoning Must Explain When Neuralization Adds Value
- ITCSSmoothed Analysis of Online Metric Matching with a Single Sample: Beyond Metric Distortion
20259 papers
- NeurIPS (T1)Accelerating Data-Driven Algorithm Selection for Combinatorial Partitioning Problems
- ICML (T1)Algorithms with Calibrated Machine Learning Predictions
- ICML (T1)EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations
- Operations Research (T1)Generalization Guarantees for Multi-item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms
- CPAIORLLMs for Cold-Start Cutting Plane Separator Configuration
- IJCAI (T1)New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets
- Learning on Graphs Conference (LoG). Extended AbstractOvercoming Information Bottlenecks in Directed Graph Neural Networks through Rewiring
- ICML (T1)Primal-Dual Neural Algorithmic Reasoning
- ICML (T1)Wait-Less Offline Tuning and Re-solving for Online Decision Making
20244 papers
- EC (T1)Bandit Profit-maximization for Targeted Marketing
- Journal of the ACM (T1)How Much Data Is Sufficient to Learn High-performing Algorithms?
- Journal of the ACM (T1)Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization
- ICML (T1)MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
20231 paper
- EAAMODisincentivizing Polarization in Social Networks
Where alumni went3 listed
Apply to StanfordFall 2027
Stanford Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- See program page
Confirmed on the program page.
Similar labsnearest by research summary
- CAToronto
Eldan Cohen
- Learning theory & optimization
- DETUM
Stefanie Jegelka
- Learning theory & optimization
- USUIUC
Richard Y. Zhang
- Learning theory & optimization
- GBEdinburgh
Henry Gouk
- Trustworthy & safe ML
- CNZhejiang
Tianhang Zheng
- Trustworthy & safe ML
- CHETH Zurich
Josef Teichmann
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Stanfordcloses