Maria-Florina Balcan
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Romanian computer scientist
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Maria-Florina Balcancomputer-science Degrees
Computer Science
#2271
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#2366
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Machine Learning
#1220
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#1240
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#7929
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Computer Science
Maria-Florina Balcan's Degrees
- PhD Computer Science Georgia Tech
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Why Is Maria-Florina Balcan Influential?
(Suggest an Edit or Addition)According to Wikipedia, Maria-Florina Balcan is a Romanian-American computer scientist whose research investigates machine learning, algorithmic game theory, theoretical computer science, including active learning, kernel methods, random-sampling mechanisms and envy-free pricing. She is an associate professor of computer science at Carnegie Mellon University.
Maria-Florina Balcan's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Agnostic active learning (2006) (513)
- Margin Based Active Learning (2007) (318)
- Co-Training and Expansion: Towards Bridging Theory and Practice (2004) (311)
- Adaptive Gradient-Based Meta-Learning Methods (2019) (238)
- Scalable Kernel Methods via Doubly Stochastic Gradients (2014) (213)
- Distributed Learning, Communication Complexity and Privacy (2012) (191)
- A discriminative framework for clustering via similarity functions (2008) (176)
- The true sample complexity of active learning (2010) (176)
- The Power of Localization for Efficiently Learning Linear Separators with Noise (2013) (155)
- On a theory of learning with similarity functions (2006) (154)
- Learning to Branch (2018) (148)
- Kernels as features: On kernels, margins, and low-dimensional mappings (2006) (145)
- Approximate clustering without the approximation (2009) (144)
- Learning submodular functions (2010) (138)
- Improved Distributed Principal Component Analysis (2014) (136)
- Approximation algorithms and online mechanisms for item pricing (2006) (134)
- A discriminative model for semi-supervised learning (2010) (129)
- Active and passive learning of linear separators under log-concave distributions (2012) (126)
- Robust hierarchical clustering (2013) (126)
- Mechanism design via machine learning (2005) (125)
- A PAC-Style Model for Learning from Labeled and Unlabeled Data (2005) (117)
- Item pricing for revenue maximization (2008) (110)
- Provable Guarantees for Gradient-Based Meta-Learning (2019) (108)
- Distributed k-Means and k-Median Clustering on General Topologies (2013) (103)
- Person Identification in Webcam Images: An Application of Semi-Supervised Learning (2005) (100)
- Combining Self Training and Active Learning for Video Segmentation (2011) (99)
- Commitment Without Regrets: Online Learning in Stackelberg Security Games (2015) (96)
- Local algorithms for interactive clustering (2013) (93)
- Clustering with Interactive Feedback (2008) (92)
- Reducing mechanism design to algorithm design via machine learning (2007) (88)
- A theory of learning with similarity functions (2008) (87)
- Clustering under Perturbation Resilience (2011) (86)
- Proceedings of the 33rd International Conference on Machine Learning (2016) (85)
- Influence Function Learning in Information Diffusion Networks (2014) (83)
- Learning and 1-bit Compressed Sensing under Asymmetric Noise (2016) (79)
- Efficient Learning of Linear Separators under Bounded Noise (2015) (79)
- Clustering under approximation stability (2013) (77)
- Efficient Representations for Lifelong Learning and Autoencoding (2014) (67)
- Improved Guarantees for Learning via Similarity Functions (2008) (66)
- Active Property Testing (2011) (61)
- Learning Valuation Functions (2011) (61)
- Sample Complexity of Automated Mechanism Design (2016) (59)
- Distributed k-means and k-median clustering on general communication topologies (2013) (58)
- Differentially Private Clustering in High-Dimensional Euclidean Spaces (2017) (56)
- Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization (2017) (55)
- An Improved Gap-Dependency Analysis of the Noisy Power Method (2016) (55)
- A General Theory of Sample Complexity for Multi-Item Profit Maximization (2017) (54)
- Improved equilibria via public service advertising (2009) (53)
- Finding Endogenously Formed Communities (2012) (49)
- Robust Interactive Learning (2011) (49)
- Geometry-Aware Gradient Algorithms for Neural Architecture Search (2020) (48)
- Learning Economic Parameters from Revealed Preferences (2014) (48)
- Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems (2016) (47)
- k-center Clustering under Perturbation Resilience (2015) (42)
- Active Clustering of Biological Sequences (2012) (41)
- A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning (2014) (40)
- Risk Bounds for Transferring Representations With and Without Fine-Tuning (2017) (40)
- Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing (2021) (39)
- Item pricing for revenue maximization (2008) (39)
- The Price of Uncertainty (2009) (37)
- Distributed PCA and k-Means Clustering (2013) (36)
- Learning Cooperative Games (2015) (36)
- Finding Low Error Clusterings (2009) (35)
- Communication Efficient Distributed Kernel Principal Component Analysis (2015) (34)
- How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design (2019) (34)
- Center Based Clustering: A Foundational Perspective (2014) (34)
- Exploiting Ontology Structures and Unlabeled Data for Learning (2013) (33)
- Circumventing the Price of Anarchy: Leading Dynamics to Good Behavior (2013) (33)
- Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling (2016) (33)
- Statistical Active Learning Algorithms (2013) (33)
- Envy-Free Classification (2018) (32)
- Robust reductions from ranking to classification (2007) (31)
- Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks (2016) (30)
- On Kernels, Margins, and Low-Dimensional Mappings (2004) (29)
- Efficient Clustering with Limited Distance Information (2010) (26)
- Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions (2017) (26)
- Data-Driven Clustering via Parameterized Lloyd's Families (2018) (24)
- Estimating Approximate Incentive Compatibility (2019) (24)
- Learning Combinatorial Functions from Pairwise Comparisons (2016) (24)
- Submodular Functions: Learnability, Structure, and Optimization (2010) (23)
- Agnostic Clustering (2009) (23)
- Learning to Link (2019) (21)
- Testing Matrix Rank, Optimally (2018) (20)
- Active Learning Algorithms for Graphical Model Selection (2016) (20)
- Matrix Completion and Related Problems via Strong Duality (2017) (19)
- A Theory of Loss-Leaders: Making Money by Pricing Below Cost (2007) (19)
- Communication Efficient Distributed Agnostic Boosting (2015) (18)
- On the equilibria of alternating move games (2010) (18)
- An Augmented PAC Model for Semi-Supervised Learning (2006) (17)
- Single Price Mechanisms for Revenue Maximization in Unlimited Supply Combinatorial Auctions (2006) (17)
- Handwritten text localization in skewed documents (2001) (16)
- Semi-bandit Optimization in the Dispersed Setting (2019) (16)
- Data Driven Resource Allocation for Distributed Learning (2015) (15)
- Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy (2013) (15)
- Active Learning – Modern Learning Theory (2015) (15)
- Distributed Kernel Principal Component Analysis (2015) (14)
- Budgeted Influence Maximization for Multiple Products (2013) (13)
- Active Learning - Modern Learning Theory (2016) (13)
- Minimally invasive mechanism design: Distributed covering with carefully chosen advice (2012) (13)
- Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond (2021) (13)
- On Nash-Equilibria of Approximation-Stable Games (2010) (12)
- Learning piecewise Lipschitz functions in changing environments (2019) (12)
- The power of localization for efficiently learning linear separators with noise (2014) (12)
- Min-sum Clustering of Protein Sequences with Limited Distance Information (2011) (12)
- Approximate Nash Equilibria under Stability Conditions (2010) (11)
- Efficient Semi-supervised and Active Learning of Disjunctions (2013) (11)
- Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning (2014) (11)
- Modeling and Detecting Community Hierarchies (2013) (11)
- New theoretical frameworks for machine learning (2008) (11)
- I Like Her more than You: Self-determined Communities (2012) (10)
- Refined bounds for algorithm configuration: The knife-edge of dual class approximability (2020) (10)
- Learning Submodular Functions with Applications to Multi-Agent Systems (2015) (10)
- Nash Equilibria in Perturbation-Stable Games (2017) (10)
- Learning Predictions for Algorithms with Predictions (2022) (10)
- Clustering via Similarity Functions : Theoretical Foundations and Algorithms ∗ (2008) (9)
- On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness (2020) (9)
- Distributed Clustering on Graphs (2013) (9)
- The Snowball Effect of Uncertainty in Potential Games (2011) (9)
- General and Robust Communication-Efficient Algorithms for Distributed Clustering (2017) (8)
- Optimal Sample Complexity for Matrix Completion and Related Problems via 𝓁s2-Regularization (2017) (8)
- Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (2016) (8)
- Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees (2019) (8)
- Symmetric and Asymmetric $k$-center Clustering under Stability (2015) (7)
- The Power of Localization for Efficiently Learning Linear Separators with Malicious Noise (2013) (7)
- Random Sampling Auctions for Limited Supply (2007) (7)
- Clustering under Local Stability: Bridging the Gap between Worst-Case and Beyond Worst-Case Analysis (2017) (7)
- Noise in Classification (2020) (6)
- Continuous-Time Influence Maximization for Multiple Items (2013) (6)
- Sequential Item Pricing for Unlimited Supply (2010) (6)
- Learning-to-learn non-convex piecewise-Lipschitz functions (2021) (6)
- Active Learning and Best-Response Dynamics (2014) (5)
- The price of uncertainty (2009) (5)
- Open Problems in Efficient Semi-supervised PAC Learning (2007) (5)
- Near Optimality in Covering and Packing Games by Exposing Global Information (2011) (5)
- Asymptotic Active Learning (2007) (5)
- Diversified Strategies for Mitigating Adversarial Attacks in Multiagent Systems (2018) (5)
- Learning Time-Varying Coverage Functions (2014) (5)
- Analysis of Algorithms Beyond the Worst Case (Dagstuhl Seminar 14372) (2015) (4)
- Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning (2020) (4)
- Meta-Learning Adversarial Bandits (2022) (4)
- Approximation Algorithms for Item Pricing (2005) (4)
- Sample Complexity of Multi-Item Profit Maximization (2017) (4)
- 21 An Augmented PAC Model for Semi-Supervised Learning (2005) (4)
- Leading dynamics to good behavior (2011) (4)
- A New Perspective on Learning Linear Separators with Large \(L_qL_p\) Margins (2014) (4)
- Near-Optimality in Covering Games by Exposing Global Information (2014) (4)
- Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs (2020) (4)
- A Theory of Similarity Functions for Clustering (2007) (4)
- Robustly-reliable learners under poisoning attacks (2022) (4)
- S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms (2017) (3)
- Sponsored Search Auction Design via Machine Learning (2008) (3)
- Active Testing (2011) (3)
- Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games (2022) (3)
- Robust Communication-Optimal Distributed Clustering Algorithms (2017) (3)
- Data driven semi-supervised learning (2021) (3)
- Learning the best algorithm for max-cut, clustering, and other partitioning problems (2016) (3)
- Game couplings: Learning dynamics and applications (2011) (3)
- Better Guarantees for Sparsest Cut Clustering (2009) (3)
- Mechanism design, machine learning, and pricing problems (2007) (3)
- Faster algorithms for learning to link, align sequences, and price two-part tariffs (2022) (2)
- Improved Sample Complexity Bounds for Branch-And-Cut (2021) (2)
- On Weight-Sharing and Bilevel Optimization in Architecture Search (2019) (2)
- Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts (2022) (2)
- Improved Learning Bounds for Branch-and-Cut (2021) (2)
- Online optimization of piecewise Lipschitz functions in changing environments (2019) (2)
- Learning symmetric non-monotone submodular functions (2012) (2)
- Generalization in portfolio-based algorithm selection (2020) (2)
- Nash Equilibria in Perturbation Resilient Games (2010) (2)
- 8803 Machine Learning Theory (2010) (2)
- Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments (2015) (1)
- Lifelong Learning in Costly Feature Spaces (2017) (1)
- Data driven algorithms for limited labeled data learning (2021) (1)
- Learning with Multiple Similarity Functions (2008) (1)
- Label Propagation with Weak Supervision (2022) (1)
- Provably tuning the ElasticNet across instances (2022) (1)
- Maximizing Revenue under Market Shrinkage and Market Uncertainty (2022) (1)
- WEIGHT-SHARING BEYOND NEURAL ARCHITECTURE SEARCH: EFFICIENT FEATURE MAP SELECTION AND FEDERATED HYPERPARAMETER TUNING (2020) (1)
- Active Learning Algorithms for Graphical Model Selection : Supplementary Material (2016) (1)
- A Learning Theoretic Framework for Clustering with Similarity Functions (2007) (1)
- Data-driven Algorithm Design (2020) (1)
- 8803 Connections between Learning , Game Theory , and Optimization (2010) (0)
- Clustering Protein Sequences Given the Approximation Stability of the Min-Sum Objective Function (2011) (0)
- Bicriteria Multidimensional Mechanism Design with Side Information (2023) (0)
- 10-601 Machine Learning (2015) (0)
- UvA-DARE (Digital Academic Repository) Natural Graph Networks (2020) (0)
- UvA-DARE (Digital Academic Repository) Experimental design for MRI by greedy policy search (2020) (0)
- Learning with Explanation Constraints (2023) (0)
- Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs (2023) (0)
- Clustering Perturbation Resilient k-Median Instances (2013) (0)
- Appendix A Discretization based algorithm (2020) (0)
- Learning Within an Instance for Designing High-Revenue Combinatorial Auctions (2021) (0)
- UvA-DARE (Digital Academic Repository) MDP homomorphic networks: Group symmetries in reinforcement learning (2021) (0)
- Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk) (2022) (0)
- Performance guarantees for transferring representations (2017) (0)
- Machine Learning : Theory , Applications , Experiences (2006) (0)
- Foundations of Unsupervised Learning (Dagstuhl Seminar 16382) (2017) (0)
- Special Section on the Fiftieth Annual IEEE Symposium on Foundations of Computer Science (FOCS 2009) (2013) (0)
- 98 16382 – Foundations of Unsupervised Learning 3 Overview of Talks 3 . 1 Linear Algebraic Structure of Word Meanings (2017) (0)
- An Analysis of Robustness of Non-Lipschitz Networks (2020) (0)
- 10-806 Foundations of Machine Learning and Data Science (2015) (0)
- Reliable Learning for Test-time Attacks and Distribution Shift (2023) (0)
- Private and Online Optimization of Piecewise Lipschitz Functions (2017) (0)
- Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy (2014) (0)
- ERFORMANCE GUARANTEES FOR TRANSFERRING REPRESENTATIONS (2017) (0)
- Based Clustering : A Foundational Perspective (2014) (0)
- On the geometry of output-code multi-class learning (2015) (0)
- Item Pricing for Revenue Maximization in Combinatorial Auctions (2007) (0)
- Label Efficient Learning by Exploiting Multi-Class Output Codes (2015) (0)
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