Maria-Florina Balcan
Cadence Design Systems Professor
Studies foundations of machine learning, including learning theory, data-driven algorithm design and learning in game-theoretic settings.
Maria-Florina Balcan's lab at CMU works on learning theory & optimization. 32 papers since 2023, 28 at top venues, mostly NeurIPS and ICLR. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 88%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 7 · collecting
- 6 papers2025 · T1 5
- 9 papers2024 · T1 8
- 8 papers2023 · T1 8
T1 venuesOtherVenues: NeurIPS, ICLR, AAAI, EC, ICML
Research focuspaper titles that use each topic's words
- Learning theory & optimization15 papers
- algorithm3 papers
- algorithm design2 papers
- studies foundations0 papers
- game-theoretic0 papers
Paper list
From the lab homepage.
20269 papers
- Mathematics of Operations ResearchBicriteria Multidimensional Mechanism Design with Side Information (opens in a new tab)
- ICML (T1)Learning in Structured Stackelberg Games (opens in a new tab)
- NeurIPS (T1)Learning to Price with Persuasion (opens in a new tab)
- ICLR (T1)Nearly-optimal bandit learning in stackelberg games with side information (opens in a new tab)
- Invited Open Problem, COLTOnline Optimization of Piecewise-Lipschitz Functions with Applications to Data-Driven Algorithm Design (opens in a new tab)
- EC (T1)The Complexity of Equilibrium Refinements in Potential Games (opens in a new tab)
- EC (T1)The Complexity of Proper Equilibrium in Extensive-Form and Polytope Games (opens in a new tab)
- AAAI (T1)Weakest Bidder Types and New Core-Selecting Combinatorial Auctions (opens in a new tab)
- ICML (T1)What Does Preference Learning Recover from Pairwise Comparison Data? (opens in a new tab)
20256 papers
- TMLR (T1)Algorithm Configuration for Structured Pfaffian Settings (opens in a new tab)
- AAAI (T1)Increasing Revenue in Efficient Combinatorial Auctions by Learning to Generate Artificial Competition
- ICLR (T1)Learning from weak labelers as constraints (opens in a new tab)
- IJCAI (T1)New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets (opens in a new tab)
- NeurIPS (T1)On Learning Verifiers and Implications to Chain-of-Thought Reasoning (opens in a new tab)
- NeruIPSSample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function (opens in a new tab)
20249 papers
- NeurIPS (T1)Accelerating ERM for data-driven algorithm design using output-sensitive techniques (opens in a new tab)
- Journal of the ACM (T1)How much data is sufficient to learn high-performing algorithms? (opens in a new tab)
- UAI (T1)Learning Accurate and Interpretable Decision Trees (opens in a new tab)
- TMLR (T1)Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs (opens in a new tab)
- Journal of the ACM (T1)Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization (opens in a new tab)
- ICLR (T1)Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances (opens in a new tab)
- NeurIPS (T1)Regret Minimization in Stackelberg Games with Side Information (opens in a new tab)
- ICLR (T1)Spectrally Transformed Kernel Regression (opens in a new tab)
- EMI/PMCSubsidy for repair in component maintenance games
20238 papers
- JMLR (T1)An Analysis of Robustness of Non-Lipschitz Networks (opens in a new tab)
- Operations Research (T1)Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms (opens in a new tab)
- ICLR (T1)Label Propagation with Weak Supervision (opens in a new tab)
- NeurIPS (T1)Learning with Explanation Constraints (opens in a new tab)
- NeurIPS (T1)Meta-Learning Adversarial Bandit Algorithms (opens in a new tab)
- AISTATS (T1)Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games (opens in a new tab)
- NeurIPS (T1)New Bounds for Hyperparameter Tuning of Regression Problems Across Instances (opens in a new tab)
- NeurIPS (T1)Reliable learning in challenging environments (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
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