Maria-Florina Balcan

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 137

Top Venues

Publications

Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Ravikumar
Spectrally Transformed Kernel Regression. CoRR (2024)
Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Kumar Ravikumar
Spectrally Transformed Kernel Regression. ICLR (2024)
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. J. ACM 71 (2) (2024)
Maria-Florina Balcan, Hedyeh Beyhaghi
New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs. Trans. Mach. Learn. Res. 2024 (2024)
Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
Regret Minimization in Stackelberg Games with Side Information. CoRR (2024)
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. ICLR (2024)
Maria-Florina Balcan, Dravyansh Sharma
Learning accurate and interpretable decision trees. CoRR (2024)
Siddharth Prasad, Ellen Vitercik, Maria-Florina Balcan, Tuomas Sandholm
New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets. CoRR (2024)
Maria-Florina Balcan, Anh Nguyen, Dravyansh Sharma
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances. NeurIPS (2023)
Siddharth Prasad, Maria-Florina Balcan, Tuomas Sandholm
Bicriteria Multidimensional Mechanism Design with Side Information. NeurIPS (2023)
Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Steven Z. Wu
Meta-Learning Adversarial Bandit Algorithms. NeurIPS (2023)
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. CoRR (2023)
Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar
Learning with Explanation Constraints. NeurIPS (2023)
Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar
Learning with Explanation Constraints. CoRR (2023)
Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
Meta-Learning Adversarial Bandit Algorithms. CoRR (2023)
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
Reliable Learning for Test-time Attacks and Distribution Shift. CoRR (2023)
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
Reliable learning in challenging environments. NeurIPS (2023)
Maria-Florina Balcan, Hedyeh Beyhaghi
Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs. CoRR (2023)
Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. AISTATS (2023)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
Bicriteria Multidimensional Mechanism Design with Side Information. CoRR (2023)
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang
An Analysis of Robustness of Non-Lipschitz Networks. J. Mach. Learn. Res. 24 (2023)
Maria-Florina Balcan, Christopher Seiler, Dravyansh Sharma
Faster algorithms for learning to link, align sequences, and price two-part tariffs. CoRR (2022)
Maria-Florina Balcan
Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk). STACS (2022)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
Improved Sample Complexity Bounds for Branch-And-Cut. CP (2022)
Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu
Meta-Learning Adversarial Bandits. CoRR (2022)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts. NeurIPS (2022)
Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
Robustly-reliable learners under poisoning attacks. CoRR (2022)
Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, Pradeep Ravikumar
Label Propagation with Weak Supervision. CoRR (2022)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts. CoRR (2022)
Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
Robustly-reliable learners under poisoning attacks. COLT (2022)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Learning Predictions for Algorithms with Predictions. CoRR (2022)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Provably tuning the ElasticNet across instances. CoRR (2022)
Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. CoRR (2022)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Learning-to-learn non-convex piecewise-Lipschitz functions. NeurIPS (2021)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Learning-to-learn non-convex piecewise-Lipschitz functions. CoRR (2021)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. CoRR (2021)
Maria-Florina Balcan, Dravyansh Sharma
Data driven algorithms for limited labeled data learning. CoRR (2021)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
Learning Within an Instance for Designing High-Revenue Combinatorial Auctions. IJCAI (2021)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. NeurIPS (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS (2021)
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design. STOC (2021)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Generalization in Portfolio-Based Algorithm Selection. AAAI (2021)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
Improved Learning Bounds for Branch-and-Cut. CoRR (2021)
Maria-Florina Balcan, Dravyansh Sharma
Data driven semi-supervised learning. NeurIPS (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR (2021)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. AAAI (2020)
Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Geometry-Aware Gradient Algorithms for Neural Architecture Search. CoRR (2020)
Travis Dick, Wesley Pegden, Maria-Florina Balcan
Semi-bandit Optimization in the Dispersed Setting. UAI (2020)
Maria-Florina Balcan, Nika Haghtalab, Colin White
-center Clustering under Perturbation Resilience. ACM Trans. Algorithms 16 (2) (2020)
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness. CoRR (2020)
Dravyansh Sharma, Maria-Florina Balcan, Travis Dick
Learning piecewise Lipschitz functions in changing environments. AISTATS (2020)
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs. IJCAI (2020)
Maria-Florina Balcan
Data-driven Algorithm Design. CoRR (2020)
Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan
Lifelong learning in costly feature spaces. Theor. Comput. Sci. 808 (2020)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Refined bounds for algorithm configuration: The knife-edge of dual class approximability. CoRR (2020)
Kaiwen Wang, Travis Dick, Maria-Florina Balcan
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning. CoRR (2020)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Refined bounds for algorithm configuration: The knife-edge of dual class approximability. ICML (2020)
Maria-Florina Balcan, Nika Haghtalab
Noise in Classification. CoRR (2020)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Generalization in portfolio-based algorithm selection. CoRR (2020)
Maria-Florina Balcan, Travis Dick, Manuel Lang
Learning to Link. ICLR (2020)
Maria-Florina Balcan, Travis Dick, Wesley Pegden
Semi-bandit Optimization in the Dispersed Setting. CoRR (2019)
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
How much data is sufficient to learn high-performing algorithms? CoRR (2019)
Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff
Robust Communication-Optimal Distributed Clustering Algorithms. ICALP (2019)
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Provable Guarantees for Gradient-Based Meta-Learning. ICML (2019)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Estimating Approximate Incentive Compatibility. CoRR (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Adaptive Gradient-Based Meta-Learning Methods. CoRR (2019)
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
Envy-Free Classification. NeurIPS (2019)
Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang
Testing Matrix Rank, Optimally. SODA (2019)
Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang
Non-Convex Matrix Completion and Related Problems via Strong Duality. J. Mach. Learn. Res. 20 (2019)
Maria-Florina Balcan, Travis Dick, Manuel Lang
Learning to Link. CoRR (2019)
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma
Online optimization of piecewise Lipschitz functions in changing environments. CoRR (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Provable Guarantees for Gradient-Based Meta-Learning. CoRR (2019)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. CoRR (2019)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Estimating Approximate Incentive Compatibility. EC (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet S. Talwalkar
Adaptive Gradient-Based Meta-Learning Methods. NeurIPS (2019)
Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang
Testing Matrix Rank, Optimally. CoRR (2018)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
A General Theory of Sample Complexity for Multi-Item Profit Maximization. EC (2018)
Maria-Florina Balcan, Travis Dick, Ellen Vitercik
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. FOCS (2018)
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
Learning to Branch. CoRR (2018)
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
Learning to Branch. ICML (2018)
Maria-Florina Balcan, Travis Dick, Colin White
Data-Driven Clustering via Parameterized Lloyd's Families. NeurIPS (2018)
Maria-Florina Balcan, Avrim Blum, Shang-Tse Chen
Diversified Strategies for Mitigating Adversarial Attacks in Multiagent Systems. AAMAS (2018)
Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang
Matrix Completion and Related Problems via Strong Duality. ITCS (2018)
Maria-Florina Balcan, Nicholas J. A. Harvey
Submodular Functions: Learnability, Structure, and Optimization. SIAM J. Comput. 47 (3) (2018)
Maria-Florina Balcan, Travis Dick, Colin White
Data-Driven Clustering via Parameterized Lloyd's Families. CoRR (2018)
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
Envy-Free Classification. CoRR (2018)
Maria-Florina Balcan, Travis Dick, Yishay Mansour
Label Efficient Learning by Exploiting Multi-Class Output Codes. AAAI (2017)
Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems. COLT (2017)
Maria-Florina Balcan, Travis Dick, Ellen Vitercik
Private and Online Optimization of Piecewise Lipschitz Functions. CoRR (2017)
Nan Du, Yingyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks. J. Mach. Learn. Res. 18 (2017)
Daniel McNamara, Maria-Florina Balcan
Risk Bounds for Transferring Representations With and Without Fine-Tuning. ICML (2017)
Daniel McNamara, Maria-Florina Balcan
Performance guarantees for transferring representations. ICLR (Workshop) (2017)
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola
Data Driven Resource Allocation for Distributed Learning. AAAI Workshops (2017)
Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski
Local algorithms for interactive clustering. J. Mach. Learn. Res. 18 (2017)
Maria-Florina Balcan, Colin White
Clustering under Local Stability: Bridging the Gap between Worst-Case and Beyond Worst-Case Analysis. CoRR (2017)
Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang
-Regularization. CoRR (2017)
Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan
Lifelong Learning in Costly Feature Spaces. CoRR (2017)
Maria-Florina Balcan, Mark Braverman
Nash Equilibria in Perturbation-Stable Games. Theory Comput. 13 (1) (2017)
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Sample Complexity of Multi-Item Profit Maximization. CoRR (2017)
Maria-Florina Balcan, Hongyang Zhang
S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Maria-Florina Balcan