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ORCIDMaria-Florina Balcan
Nina Balcan – Maria-Florina Popa
Person information
- affiliation: Georgia Institute of Technology , School of Computer Science
- affiliation: Carnegie Mellon University, Computer Science Department
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2020 – today
- 2024
[j31]Maria-Florina Balcan
, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. J. ACM 71(2): 13:1-13:73 (2024)- [j30]Maria-Florina Balcan, Hedyeh Beyhaghi:
New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs. Trans. Mach. Learn. Res. 2024 (2024) - [c123]Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar:
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. ICLR 2024 - [c122]Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Kumar Ravikumar:
Spectrally Transformed Kernel Regression. ICLR 2024 - [i92]Siddharth Prasad, Ellen Vitercik, Maria-Florina Balcan, Tuomas Sandholm:
New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets. CoRR abs/2401.13773 (2024) - [i91]Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Ravikumar:
Spectrally Transformed Kernel Regression. CoRR abs/2402.00645 (2024) - [i90]Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan:
Regret Minimization in Stackelberg Games with Side Information. CoRR abs/2402.08576 (2024) - [i89]Maria-Florina Balcan, Dravyansh Sharma:
Learning accurate and interpretable decision trees. CoRR abs/2405.15911 (2024) - 2023
- [j29]Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang:
An Analysis of Robustness of Non-Lipschitz Networks. J. Mach. Learn. Res. 24: 98:1-98:43 (2023) - [c121]Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang:
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. AISTATS 2023: 9607-9636 - [c120]Rattana Pukdee, Dylan Sam, Pradeep Kumar Ravikumar, Nina Balcan:
Label Propagation with Weak Supervision. ICLR 2023 - [c119]Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma:
Reliable learning in challenging environments. NeurIPS 2023 - [c118]Maria-Florina Balcan, Anh Nguyen, Dravyansh Sharma:
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances. NeurIPS 2023 - [c117]Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. NeurIPS 2023 - [c116]Siddharth Prasad, Maria-Florina Balcan, Tuomas Sandholm:
Bicriteria Multidimensional Mechanism Design with Side Information. NeurIPS 2023 - [c115]Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar:
Learning with Explanation Constraints. NeurIPS 2023 - [i88]Maria-Florina Balcan, Hedyeh Beyhaghi:
Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs. CoRR abs/2302.11700 (2023) - [i87]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm:
Bicriteria Multidimensional Mechanism Design with Side Information. CoRR abs/2302.14234 (2023) - [i86]Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar:
Learning with Explanation Constraints. CoRR abs/2303.14496 (2023) - [i85]Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma:
Reliable Learning for Test-time Attacks and Distribution Shift. CoRR abs/2304.03370 (2023) - [i84]Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. CoRR abs/2307.02295 (2023) - [i83]Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar:
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. CoRR abs/2310.02246 (2023) - 2022
- [c114]Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. COLT 2022: 4498-4534 - [c113]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Improved Sample Complexity Bounds for Branch-And-Cut. CP 2022: 3:1-3:19 - [c112]Maria-Florina Balcan, Misha Khodak, Dravyansh Sharma, Ameet Talwalkar:
Provably tuning the ElasticNet across instances. NeurIPS 2022 - [c111]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm:
Maximizing Revenue under Market Shrinkage and Market Uncertainty. NeurIPS 2022 - [c110]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 - [c109]Misha Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. NeurIPS 2022 - [c108]Maria-Florina Balcan:
Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk). STACS 2022: 2:1-2:1 - [i82]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. CoRR abs/2202.09312 (2022) - [i81]Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. CoRR abs/2203.04160 (2022) - [i80]Maria-Florina Balcan, Christopher Seiler, Dravyansh Sharma:
Faster algorithms for learning to link, align sequences, and price two-part tariffs. CoRR abs/2204.03569 (2022) - [i79]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts. CoRR abs/2204.07312 (2022) - [i78]Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandits. CoRR abs/2205.14128 (2022) - [i77]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Provably tuning the ElasticNet across instances. CoRR abs/2207.10199 (2022) - [i76]Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, Pradeep Ravikumar:
Label Propagation with Weak Supervision. CoRR abs/2210.03594 (2022) - [i75]Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang:
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. CoRR abs/2210.12606 (2022) - 2021
- [c107]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Generalization in Portfolio-Based Algorithm Selection. AAAI 2021: 12225-12232 - [c106]Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. ICLR 2021 - [c105]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm:
Learning Within an Instance for Designing High-Revenue Combinatorial Auctions. IJCAI 2021: 31-37 - [c104]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. NeurIPS 2021: 4015-4027 - [c103]Maria-Florina Balcan, Dravyansh Sharma:
Data driven semi-supervised learning. NeurIPS 2021: 14782-14794 - [c102]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Learning-to-learn non-convex piecewise-Lipschitz functions. NeurIPS 2021: 15056-15069 - [c101]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: 19184-19197 - [c100]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: 919-932 - [i74]Maria-Florina Balcan, Dravyansh Sharma:
Data driven algorithms for limited labeled data learning. CoRR abs/2103.10547 (2021) - [i73]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. CoRR abs/2106.04033 (2021) - [i72]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 abs/2106.04502 (2021) - [i71]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Learning-to-learn non-convex piecewise-Lipschitz functions. CoRR abs/2108.08770 (2021) - [i70]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Improved Learning Bounds for Branch-and-Cut. CoRR abs/2111.11207 (2021) - 2020
- [j28]Maria-Florina Balcan, Nika Haghtalab, Colin White:
k-center Clustering under Perturbation Resilience. ACM Trans. Algorithms 16(2): 22:1-22:39 (2020) - [j27]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong learning in costly feature spaces. Theor. Comput. Sci. 808: 14-37 (2020) - [c99]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. AAAI 2020: 3227-3234 - [c98]Dravyansh Sharma, Maria-Florina Balcan, Travis Dick:
Learning piecewise Lipschitz functions in changing environments. AISTATS 2020: 3567-3577 - [c97]Maria-Florina Balcan, Travis Dick, Manuel Lang:
Learning to Link. ICLR 2020 - [c96]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Refined bounds for algorithm configuration: The knife-edge of dual class approximability. ICML 2020: 580-590 - [c95]Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm:
Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs. IJCAI 2020: 332-338 - [c94]Travis Dick, Wesley Pegden, Maria-Florina Balcan:
Semi-bandit Optimization in the Dispersed Setting. UAI 2020: 909-918 - [p3]Maria-Florina Balcan, Nika Haghtalab:
Noise in Classification. Beyond the Worst-Case Analysis of Algorithms 2020: 361-381 - [p2]Maria-Florina Balcan:
Data-Driven Algorithm Design. Beyond the Worst-Case Analysis of Algorithms 2020: 626-645 - [e3]Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin:
Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020 [contents] - [i69]Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. CoRR abs/2004.07802 (2020) - [i68]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Refined bounds for algorithm configuration: The knife-edge of dual class approximability. CoRR abs/2006.11827 (2020) - [i67]Maria-Florina Balcan, Nika Haghtalab:
Noise in Classification. CoRR abs/2010.05080 (2020) - [i66]Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang:
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness. CoRR abs/2010.06154 (2020) - [i65]Maria-Florina Balcan:
Data-driven Algorithm Design. CoRR abs/2011.07177 (2020) - [i64]Kaiwen Wang, Travis Dick, Maria-Florina Balcan:
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning. CoRR abs/2012.10602 (2020) - [i63]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Generalization in portfolio-based algorithm selection. CoRR abs/2012.13315 (2020)
2010 – 2019
- 2019
- [j26]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: 102:1-102:56 (2019) - [c93]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Estimating Approximate Incentive Compatibility. EC 2019: 867 - [c92]Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff:
Robust Communication-Optimal Distributed Clustering Algorithms. ICALP 2019: 18:1-18:16 - [c91]Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. ICML 2019: 424-433 - [c90]Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. NeurIPS 2019: 1238-1248 - [c89]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. NeurIPS 2019: 5915-5926 - [c88]Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang:
Testing Matrix Rank, Optimally. SODA 2019: 727-746 - [i62]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Estimating Approximate Incentive Compatibility. CoRR abs/1902.09413 (2019) - [i61]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. CoRR abs/1902.10644 (2019) - [i60]Maria-Florina Balcan, Travis Dick, Wesley Pegden:
Semi-bandit Optimization in the Dispersed Setting. CoRR abs/1904.09014 (2019) - [i59]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. CoRR abs/1905.10819 (2019) - [i58]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. CoRR abs/1906.02717 (2019) - [i57]Maria-Florina Balcan, Travis Dick, Manuel Lang:
Learning to Link. CoRR abs/1907.00533 (2019) - [i56]Maria-Florina Balcan, Travis Dick, Dravyansh Sharma:
Online optimization of piecewise Lipschitz functions in changing environments. CoRR abs/1907.09137 (2019) - [i55]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 abs/1908.02894 (2019) - 2018
- [j25]Maria-Florina Balcan, Nicholas J. A. Harvey:
Submodular Functions: Learnability, Structure, and Optimization. SIAM J. Comput. 47(3): 703-754 (2018) - [c87]Maria-Florina Balcan, Avrim Blum, Shang-Tse Chen:
Diversified Strategies for Mitigating Adversarial Attacks in Multiagent Systems. AAMAS 2018: 407-415 - [c86]Maria-Florina Balcan, Travis Dick, Ellen Vitercik:
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. FOCS 2018: 603-614 - [c85]Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch. ICML 2018: 353-362 - [c84]Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang:
Matrix Completion and Related Problems via Strong Duality. ITCS 2018: 5:1-5:22 - [c83]Maria-Florina Balcan, Travis Dick, Colin White:
Data-Driven Clustering via Parameterized Lloyd's Families. NeurIPS 2018: 10664-10674 - [c82]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
A General Theory of Sample Complexity for Multi-Item Profit Maximization. EC 2018: 173-174 - [i54]Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch. CoRR abs/1803.10150 (2018) - [i53]Maria-Florina Balcan, Travis Dick, Colin White:
Data-Driven Clustering via Parameterized Lloyd's Families. CoRR abs/1809.06987 (2018) - [i52]Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. CoRR abs/1809.08700 (2018) - [i51]Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang:
Testing Matrix Rank, Optimally. CoRR abs/1810.08171 (2018) - 2017
- [j24]Pranjal Awasthi, Maria-Florina Balcan, Philip M. Long:
The Power of Localization for Efficiently Learning Linear Separators with Noise. J. ACM 63(6): 50:1-50:27 (2017) - [j23]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: 2:1-2:45 (2017) - [j22]Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski:
Local algorithms for interactive clustering. J. Mach. Learn. Res. 18: 3:1-3:35 (2017) - [j21]Maria-Florina Balcan, Mark Braverman:
Nash Equilibria in Perturbation-Stable Games. Theory Comput. 13(1): 1-31 (2017) - [c81]Maria-Florina Balcan, Travis Dick, Yishay Mansour:
Label Efficient Learning by Exploiting Multi-Class Output Codes. AAAI 2017: 1735-1741 - [c80]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 - [c79]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AISTATS 2017: 662-671 - [c78]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong Learning in Costly Feature Spaces. ALT 2017: 250-287 - [c77]Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White:
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems. COLT 2017: 213-274 - [c76]Daniel McNamara, Maria-Florina Balcan:
Performance guarantees for transferring representations. ICLR (Workshop) 2017 - [c75]Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang:
Differentially Private Clustering in High-Dimensional Euclidean Spaces. ICML 2017: 322-331 - [c74]Daniel McNamara, Maria-Florina Balcan:
Risk Bounds for Transferring Representations With and Without Fine-Tuning. ICML 2017: 2373-2381 - [c73]Maria-Florina Balcan, Hongyang Zhang:
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions. NIPS 2017: 4796-4805 - [i50]Pranjal Awasthi, Maria-Florina Balcan, Colin White:
General and Robust Communication-Efficient Algorithms for Distributed Clustering. CoRR abs/1703.00830 (2017) - [i49]Maria-Florina Balcan, Hongyang Zhang:
S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms. CoRR abs/1703.07758 (2017) - [i48]Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang:
Optimal Sample Complexity for Matrix Completion and Related Problems via 𝓁s2-Regularization. CoRR abs/1704.08683 (2017) - [i47]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Sample Complexity of Multi-Item Profit Maximization. CoRR abs/1705.00243 (2017) - [i46]Maria-Florina Balcan, Colin White:
Clustering under Local Stability: Bridging the Gap between Worst-Case and Beyond Worst-Case Analysis. CoRR abs/1705.07157 (2017) - [i45]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong Learning in Costly Feature Spaces. CoRR abs/1706.10271 (2017) - [i44]Maria-Florina Balcan, Travis Dick, Ellen Vitercik:
Private and Online Optimization of Piecewise Lipschitz Functions. CoRR abs/1711.03091 (2017) - 2016
- [j20]Maria-Florina Balcan, Yingyu Liang:
Clustering under Perturbation Resilience. SIAM J. Comput. 45(1): 102-155 (2016) - [c72]Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau:
Communication Efficient Distributed Agnostic Boosting. AISTATS 2016: 1299-1307 - [c71]Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park:
Active Learning Algorithms for Graphical Model Selection. AISTATS 2016: 1356-1364 - [c70]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang:
Learning and 1-bit Compressed Sensing under Asymmetric Noise. COLT 2016: 152-192 - [c69]Maria-Florina Balcan, Simon Shaolei Du, Yining Wang, Adams Wei Yu:
An Improved Gap-Dependency Analysis of the Noisy Power Method. COLT 2016: 284-309 - [c68]Maria-Florina Balcan, Ellen Vitercik, Colin White:
Learning Combinatorial Functions from Pairwise Comparisons. COLT 2016: 310-335 - [c67]Maria-Florina Balcan, Nika Haghtalab, Colin White:
k-Center Clustering Under Perturbation Resilience. ICALP 2016: 68:1-68:14 - [c66]Maria-Florina Balcan, Yingyu Liang, Le Song, David P. Woodruff, Bo Xie:
Communication Efficient Distributed Kernel Principal Component Analysis. KDD 2016: 725-734 - [c65]Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Sample Complexity of Automated Mechanism Design. NIPS 2016: 2083-2091 - [c64]