Selected Publications in Chronological Order - (by research area)

• New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets.
  With Siddharth Prasad, Ellen Vitercik, and Tuomas Sandholm. Arxiv 2024.
  

• Subsidy design for better social outcomes.
  With Matteo Pozzi and Dravyansh Sharma. Arxiv 2024.
  

• Regret Minimization in Stackelberg Games with Side Information.
  With Keegan Harris and Zhiwei Steven Wu. NeurIPS 2024.
  

• Accelerating ERM for data-driven algorithm design using output-sensitive techniques.
  With Christopher Seiler and Dravyansh Sharma. NeurIPS 2024.
  

• Learning Accurate and Interpretable Decision Trees.
  With Dravyansh Sharma. UAI 2024 (Oral).
  Winner of the Outstanding student paper award.
  

• Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs.
  With Hedyeh Beyhaghi. Transactions on Machine Learning Research (TMLR), 2024.
  

• Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances.
  With Mikhail Khodak, Edmond Chow, and Ameet Talwalkar. ICLR 2024 (Spotlgiht).
  

• Spectrally Transformed Kernel Regression
  With Runtian Zhai, Rattana Pukdee, Roger Jin, Pradeep Ravikumar. ICLR 2024 (Spotlgiht).
  

• Subsidy for repair in component maintenance games
  With Dravyansh Sharma and Matteo Pozzi. Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference (EMI/PMC) 2024.
  

• Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms
  With Tuomas Sandholm and Ellen Vitercik. Operations Research (OR) 2023.
  

• Reliable learning in challenging environments.
  With Steve Hanneke, Rattana Pukdee, and Dravyansh Sharma. NeurIPS 2023.
  

• Meta-Learning Adversarial Bandit Algorithms.
  With Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Kfir Y. Levy, Ron Meir, and Zhiwei Steven Wu. NeurIPS 2023.
  

• Bicriteria Multidimensional Mechanism Design with Side Information.
  With Siddharth Prasad and Tuomas Sandholm. NeurIPS 2023.
  

• New Bounds for Hyperparameter Tuning of Regression Problems Across Instances.
  With Dravy Sharma and Anh Tuan Nguyen. NeurIPS 2023.
  

• Learning with Explanation Constraints.
  With Rattana Pukdee, Dylan Sam, Zico Kolter, and Pradeep Ravikumar. NeurIPS 2023.
  

• Label Propagation with Weak Supervision.
  With Rattana Pukdee, Dylan Sam, and Pradeep Ravikumar. ICLR 2023.
  

• Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games.
  With Rattana Pukdee, Pradeep Ravikumar, and Hongyang Zhang. AISTATS 2023.
  

• An Analysis of Robustness of Non-Lipschitz Networks
  With Avrim Blum, Dravyansh Sharma, and Hongyang Zhang. JMLR 2023.
  

• Provably tuning the ElasticNet across instances.
  With With Mikhail Khodak, Dravyansh Sharma, and Ameet Talwalkar. NeurIPS 2022.
  

• Maximizing Revenue under Market Shrinkage and Market Uncertainty.
  With Siddharth Prasad and Tuomas Sandholm NeurIPS 2022.
  

• Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts.
  With Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik. NeurIPS 2022 (Oral).
  

• Learning Predictions for Algorithms with Predictions.
  With Mikhail Khodak, Ameet Talwalkar, and Sergei Vassilvitskii. NeurIPS 2022.
  

• Multi-agent Value of Information for Components’ Inspections.
  With Chao-Chao Lin, Avrim Blum, and Matteo Pozzi. International Conference on Structural Safety & Reliability (ICOSSAR 2021-2022).
  

• Robustly-reliable learners under poisoning attacks.
  With Avrim Blum, Steve Hanneke, and Dravyansh Sharma. COLT 2022.
  

• Improved Sample Complexity Bounds for Branch-and-Cut.
  With Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik. CP 2022.
  

• Data driven semi-supervised learning.
  With Dravyansh Sharma. NeurIPS 2021 (Oral).
  

• Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond.
  With Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik. NeurIPS 2021 (Spotlight).
  

• Learning-to-learn non-convex piecewise-Lipschitz functions.
  With Mikhail Khodak, Dravyansh Sharma, and Ameet Talwalkar. NeurIPS 2021.
  

• Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
  With Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Virginia Smith, and Ameet Talwalkar. NeurIPS 2021.
  

• Geometry-Aware Gradient Algorithms for Neural Architecture Search
  With Liam Li, Mikhail Khodak, and Ameet Talwalkar. ICLR 2021 (Spotlight).
  

• Learning Within an Instance for Designing High-Revenue Combinatorial Auctions.
  With Siddharth Prasad and Tuomas Sandholm. IJCAI 2021.
  

• How much data is sufficient to learn high-performing algorithms?
  With Dan DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, and Ellen Vitercik. STOC 2021.
  

• Generalization in Portfolio-based Algorithm Selection
  With Tuomas Sandholm and Ellen Vitercik. AAAI 2021.
  

• Data-Driven Algorithm Design.
  Book Chapter in Beyond Worst Case Analysis of Algorithms, T. Roughgarden (Ed). Cambridge University Press.
  

• Noise in Classification.
  With Nika Haghtalab. Book Chapter in Beyond Worst Case Analysis of Algorithms, T. Roughgarden (Ed). Cambridge University Press.
  

• Semi-bandit Optimization in the Dispersed Setting.
  With Travis Dick and Wesley Pegden. UAI 2020.
  

• Learning Piecewise Lipschitz Functions in Changing Environments.
  With Travis Dick and Dravyansh Sharma. AISTATS 2020.
  

• Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs.
  With Siddharth Prasad and Tuomas Sandholm. IJCAI 2020.
  

• Refined Bounds for Algorithm Configuration: The Knife-edge of Dual Class Approximability.
  With Tuomas Sandholm and Ellen Vitercik. ICML 2020.
  

• Learning to Link.
  With Travis Dick and Manuel Lang. ICLR 2020.
  

• k-Center Clustering Under Perturbation Resilience.
  With Nika Haghtalab and Colin White. Transactions of Algorithms Journal.
  See also an earlier version in ICALP 2016.

• Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees.
  With Tuomas Sandholm and Ellen Vitercik. AAAI 2020.
  

• Envy-free Classification.
  With Travis Dick, Ritesh Noothigattu, and Ariel D. Procaccia. NeurIPS 2019.
  

• Adaptive Gradient-Based Meta-Learning Method.
  With Mikhail Khodak and Ameet Talwalkar. NeurIPS 2019.
  

• Estimating Approximate Incentive Compatibility.
  With Tuomas Sandholm and Ellen Vitercik. EC 2019.
  Winner of the Exemplary Artificial Intelligence Track Paper Award.
  

• Provable Guarantees for Gradient-Based Meta-Learning.
  With Mikhail Khodak and Ameet Talwalkar. ICML 2019.
  

• Robust Communication-Optimal Distributed Clustering Algorithms.
  With Pranjal Awasthi, Ainesh Bakshi, Colin White, and David Woodruff. ICALP 2019.
  

• Testing Matrix Rank Optimally.
  With Yi Li, David P. Woodruff, and Hongyang Zhang. SODA 2019.
  

• Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization.
  With Travis Dick and Ellen Vitercik. FOCS 2018.
  

• Data-Driven Clustering via Parameterized Lloyd's Families.
  With Travis Dick and Colin White. NeurIPS 2018 (Spotlight).
  

• Learning to Branch.
  With Travis Dick, Tuomas Sandholm, and Ellen Vitercik. ICML 2018.
  

• Submodular Functions: Learnability, Structure, and Optimization.
  With Nick Harvey. SIAM Journal of Computing 2018.
  See also an earlier version in STOC 2011.

• A General Theory of Sample Complexity for Multi-Item Profit Maximization.
  With Tuomas Sandholm and Ellen Vitercik. ACM Conference on Economics and Computation (EC) 2018.
  

• Diversified Strategies for Mitigating Adversarial Attacks in Multiagent Systems.
  With Avrim Blum and Shang Tse Chen. International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2018.
  

• Matrix Completion and Related Problems via Strong Duality.
  With Yingyu Liang, David P. Woodruff and Hongyang Zhang. ITCS 2018.
  

• Nash Equilibria in Perturbation-Stable Games.
  With Mark Braverman. Theory of Computing Journal 2017.
  

• Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions.
  With Hongyang Zhang. NIPS 2017.

• Risk Bounds for Transferring Representations With and Without Fine-Tuning.
  With Daniel McNamara. ICML 2017.
  See also the ICLR Workshop Track version Performance Guarantees for Transferring Representations.

• Differentially Private Clustering in High-Dimensional Euclidean Spaces.
  With Travis Dick, Yingyu Liang, Wenlong Mou, and Hongyang Zhang. ICML 2017.

• Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems.
  With Vaishnavh Nagarajan, Ellen Vitercik, and Colin White. COLT 2017.

• Data Driven Resource Allocation for Distributed Learning.
  With Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, and Alex Smola. AISTATS 2017.

• Label Efficient Learning by Exploiting Multi-class Output Codes.
  With Travis Dick and Yishay Mansour. AAAI 2017.

• Lifelong Learning in Costly Feature Spaces.
  With Avrim Blum and Vaishnavh Nagarajan. ALT 2017.

• The Power of Localization for Efficiently Learning Linear Separators with Noise.
  With Pranjal Awasthi and Phil Long. Journal of the ACM 2017.
  See also an earlier version in STOC 2014.

• Local Algorithms for Interactive Clustering.
  With Pranjal Awasthi and Konstantin Voevodski. Journal of Machine Learning Research 2017.
  See also an earlier version in ICML 2014.

• Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks.
  With Nan Du, Yingyu Liang, Manuel Gomez-Rodriguez, Hongyuan Zha, and Le Song. Journal of Machine Learning Research 2017.
  See also an earlier arxiv version: Continuous-Time Influence Maximization for Multiple Items.
  

• Sample Complexity of Automated Mechanism Design.
  With Tuomas Sandholm and Ellen Vitercik. NIPS 2016.

• Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling.
  With Hongyang Zhang. NIPS 2016.

• Learning Combinatorial Functions from Pairwise Comparisons.
  With Ellen Vitercik and Colin White. COLT 2016.

• Efficient Algorithms for Learning and 1-bit Compressed Sensing under Asymmetric Noise.
  With Pranjal Awasthi, Nika Haghtalab, and Hongyang Zhang. COLT 2016.

• Communication Efficient Distributed Kernel Principal Component Analysis.
  With Yingyu Liang, Le Song, David Woodruff, Bo Xie. KDD 2016 (Oral).

• An Improved Gap-Dependency Analysis of the Noisy Power Method.
  With Simon Du, Yining Wang, and Adams Wei Yu. COLT 2016.

• k-Center Clustering Under Perturbation Resilience.
  With Nika Haghtalab and Colin White. ICALP 2016.

• Communication Efficient Distributed Agnostic Boosting.
  With Shang-Tse Chen and Polo Chau. AISTATS 2016.

• Active Learning Algorithms for Graphical Model Selection.
  With Gautam Dasarathy, Aarti Singh, and J. H. Park. AISTATS 2016.

• Clustering under Perturbation Resilience.
  With Yingyu Liang. SIAM Journal of Computing 2016.
  See also an earlier version in ICALP 2012.

• Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy.
  With Vitaly Feldman. Algorithmica 2014. Special issue on Learning Theory (Invited).
  See also an earlier version in NIPS 2013.

• Center Based Clustering: A Foundational Perspective.
  With Pranjal Awasthi. Book Chapter in Handbook of Cluster Analysis.

• Learning Cooperative Games.
  With Ariel D. Procaccia and Yair Zick. IJCAI 2015.
  
  Also appears in the Sixth Workshop on Cooperative Games in Multiagent Systems, CoopMAS-2015

• Efficient Learning of Linear Separators under Bounded Noise.
  With Pranjal Awasthi, Nika Haghtalab, and Ruth Urner. COLT 2015.

• Efficient Representations for Life-Long Learning and Autoencoding.
  With Avrim Blum and Santosh Vempala. COLT 2015.

• Commitment Without Regrets: Online Learning in Stackelberg Security Games.
  With Avrim Blum, Nika Haghtalab, and Ariel Procaccia. EC 2015.

• Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning.
  With Aurelien Bellet, Yingyu Liang, Alireza Bagheri Garakani, and Fei Sha. SIAM International Conference on Data Mining 2015.
  
  An earlier version appeared in the ICML 2014 workshop on New Learning Frameworks and Models for Big Data.

• Active Learning.
  With Ruth Urner. Encyclopedia of Algorithms 2014.

• Robust Hierarchical Clustering.
  Yingyu Liang and Pramod Gupta. Journal of Machine Learning Research 2014.
  See also an earlier version (in COLT 2010).

• Improved Distributed Principal Component Analysis.
  With Vandana Kanchanapally, Yingyu Liang, and David Woodruff. NIPS 2014.
  
  An earlier version (Distributed PCA and k-Means Clustering) appeared in the NIPS 2013 Workshop on Big Learning.
  

• Active Learning and Best-Response Dynamics.
  With Chris Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, and Le Song. NIPS 2014.

• Scalable Kernel Methods via Doubly Stochastic Gradients.
  With Bo Dai, Xie Dai, Niao He, Yingyu Liang, Anant Raj, and Le Song. NIPS 2014.

• Learning Time-Varying Coverage Functions.
  With Yingyu Liang, Nan Du, and Le Song. NIPS 2014.

• Learning Economic Parameters from Revealed Preferences.
  With Amit Daniely, Ruta Mehta, Ruth Urner, and Vijay Vazirani. WINE 2014.
  See also the arXiv:1407.7937 version.
  

• The Power of Localization for Efficiently Learning Linear Separators with Noise.
  With Pranjal Awasthi and Phil Long. STOC 2014.

• Influence Function Learning in Information Diffusion Networks.
  With Nan Du, Yingyu Liang, and Le Song. ICML 2014.
  
  Also presented at the ICML 2014 Workshop: Crowdsourcing and Human Computing.

• Local Algorithms for Interactive Clustering.
  With Pranjal Awasthi and Konstantin Voevodski. ICML 2014.

• A New Perspective on Learning Linear Separators with Large L_q L_p Margins.
  With Chris Berlind. AISTATS 2014.
  Preliminary version appeared in the NIPS 2013 Workshop on Learning Faster from Easy Data Workshop.

• Near Optimality in Covering and Packing Games by Exposing Global Information.
  With Sara Krehbiel, Georgios Piliouras, and Jinwoo Shin. ACM Transactions on Economics and Computation 2014.
  See also an earlier version in CDC 2012.

• Statistical Active Learning Algorithms.
  With Vitaly Feldman. NIPS 2013.
  
  Extended version appears under the title Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy in Algorithmica 2014. Special issue on Learning Theory (Invited).

• Distributed k-Means and k-Median Clustering on General Topologies.
  With Steven Ehrlich and Yingyu Liang. NIPS 2013.

• Active and Passive Learning of Linear Separators under Log-concave Distributions.
  With Phil Long. COLT 2013.
  
  Also presented in the 51st Annual Allerton Conference on Communication, Control, and Computing.

• Efficient Semi-supervised and Active Learning of Disjunctions.
  With Chris Berlind, Steven Ehrlich, and Yingyu Liang. ICML 2013.
  

• Exploiting Ontology Structures and Unlabeled Data for Learning.
  With Avrim Blum and Yishay Mansour. ICML 2013.
  

• Finding Endogenously Formed Communities.
  With Christian Borgs, Mark Braverman, Jennifer Chayes, and Shang-Hua Teng. SODA 2013.
  

• Modeling and Detecting Community Hierarchies.
  With Yingyu Liang. SIMBAD 2013.
  

• Clustering Perturbation Resilient k-Median Instances.
  With Yingyu Liang. NIPS 2013 Workshop on Learning Faster from Easy Data Workshop.
  

• Learning Symmetric Non-monotone Submodular Functions.
  With Nick Harvey and Satoru Iwata. NIPS Workshop on Discrete Optimization in Machine Learning (DISCML) 2012.

• Robust Interactive Learning.
  With Steve Hanneke. COLT 2012.
  

• Learning Valuation Functions.
  With Florin Constantin, Satoru Iwata, and Lei Wang. COLT 2012.

• Distributed Learning, Communication Complexity, and Privacy.
  With Avrim Blum, Shai Fine, and Yishay Mansour. COLT 2012.
  Winner of the Runner Up Best Paper Award.
  

• Clustering under Perturbation Resilience.
  With Yingyu Liang. ICALP 2012.
  
  See also the extended version (2014).
  

• Near Optimality in Covering and Packing Games by Exposing Global Information.
  With Sara Krehbiel, Georgios Piliouras, and Jinwoo Shin. CDC 2012.
  
  An extended version in ACM Transactions on Economics and Computation 2014.
  

• Active Property Testing.
  With Eric Blais, Avrim Blum, and Liu Yang. FOCS 2012.
  
  See also the an extended version.

• The Weighted Majority Algorithm does not Converge in Nearly Zero-sum Games.
  With Florin Constantin and Ruta Mehta. ICML 2012 Workshop on Markets, Mechanisms, and Multi-Agent Models.

• Learning Submodular Functions.
  With Nick Harvey. STOC 2011.
  
  Also a NECTAR track paper at ECML-PKDD 2012 (for "significant machine learning results"). See short abstract
  
  See also Submodular Functions: Learnability, Structure, and Optimization (expanded version, 2012).

• The Snowball Effect of Uncertainty in Potential Games.
  With Florin Constantin and Steven Ehrlich. WINE 2011.

• Game-theoretic Couplings and Applications.
  With Florin Constantin, Georgios Piliouras, and Jeff Shamma. CDC 2011.
  
  See also extended version (2013).

• Leading Dynamics to Good Behavior.
  SIGecom Exchanges, Vol. 10.2, 2011 (Invited).
  

• Min-Sum Clustering of Protein Sequences with Limited Distance Information.
  With Heiko Roeglin, ShangHua Teng, Konstantin Voevodski, and Yu Xia. SIMBAD 2011.
  

• Combining Self Training and Active Learning for Video Segmentation.
  With Alireza Fathi, Xiaofeng Ren, and James M. Rehg. BMVC 2011.
  
  See also Alireza's Interactive Segmentation Software.

• Robust Hierarchical Clustering.
  With Pramod Gupta. COLT 2010.
  
  An extended version (joint with Yingyu Liang and Pramod Gupta) appears in Journal of Machine Learning Research 2014.

• Efficient Clustering with Limited Distance Information.
  With Heiko Roeglin, ShangHua Teng, Konstantin Voevodski, and Yu Xia. UAI 2010.
  
  Extended version appears under the title Active Clustering of Biological Sequences in Journal of Machine Learning Research 2012.
  

• Sequential Item Pricing for Unlimited Supply.
  With Florin Constantin. WINE 2010.

• On Nash-Equilibria of Approximation-Stable Games.
  With Pranjal Awasthi, Avrim Blum, Or Sheffet, and Santosh Vempala. SAGT 2010.
  
  A general audience version appears in the Current Science Journal 2012 (Invited).
  

• Circumventing the Price of Anarchy: Leading Dynamics to Good Behavior.
  With Avrim Blum and Yishay Mansour. ICS 2010.
  
  See also a journal paper appearing in SIAM Journal on Computing 2013, combining and extending the ICS'10 and SODA'09 papers.
  

• On the Equilibria of Alternating Move Games.
  With Aaron Roth, Adam Kalai, and Yishay Mansour. SODA 2010.
  

• Finding Low Error Clusterings.
  With Mark Braverman. COLT 2009.

• Agnostic Clustering.
  With Heiko Roeglin and Shang-Hua Teng. ALT 2009.
  

• Better Guarantees for Sparsest Cut Clustering
  Open Problem. COLT 2009.
  

• The Price of Uncertainty
  With Avrim Blum and Yishay Mansour. EC 2009.
  
  An extended version appears in the ACM Transactions on Economics and Computation 2013.
  

• Improved Equilibria via Public Service Advertising.
  With Avrim Blum and Yishay Mansour. SODA 2009.
  

• Approximate Clustering without the Approximation.
  With Avrim Blum and Anupam Gupta. SODA 2009.
  
  Extended version appears under the title Clustering under Approximation Stability in Journal of the ACM 2013.

• New Theoretical Frameworks for Machine Learning.
  PhD Thesis, Carnegie Mellon University, 2008. <
  Winner of the CMU SCS Distinguished Dissertation Award.

• A Discriminative Framework for Clustering via Similarity Functions.
  With Avrim Blum and Santosh Vempala. STOC 2008.
  
  See also an extended version (2009).

• Item Pricing for Revenue Maximization.
  With Avrim Blum and Yishay Mansour. ACM Conference on Electronic Commerce, 2008.
  
  A related note appears in SIGecom Exchanges, Issue 7.3, 2008 (Invited).

• Improved Guarantees for Learning via Similarity Functions.
  With Avrim Blum and Nathan Srebro. COLT 2008.
  
  A related note on Similarity-Based Theoretical Foundation for Sparse Parzen Window Prediction.
  With Avrim Blum and Nathan Srebro. ICML/UAI/COLT 2008 Workshop on Sparse Optimization and Variable Selection.

• The True Sample Complexity of Active Learning.
  With Steve Hanneke and Jennifer Wortman. COLT 2008.
  Winner of the Mark Fulk Best Student Paper Award.
  
  An extended version appears in Machine Learning Journal, 80(2-3):111-139, 2010. Special issue for COLT 2008 (Invited).
  

• Clustering with Interactive Feedback.
  With Avrim Blum. ALT 2008.
  

• New Theoretical Frameworks for Machine Learning.
  Thesis Proposal, Carnegie Mellon University, May 2007.
  
  
• Margin Based Active Learning.
  With Andrei Broder and Tong Zhang. COLT 2007.
  
  
• A Theory of Loss-leaders: Making Money by Pricing below Cost.
  With Avrim Blum, Hubert Chan, and MohammadTaghi Hajiaghayi. WINE 2007. Also available as Technical Report, CMU-CS-07-143.
  
  
• Robust Reductions from Ranking to Classification.
  With Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Greg Sorkin. COLT 2007.
  
  An extended version appears in Machine Learning Journal, 72(1-2):139-153, 2008. Special issue for COLT 2007 (Invited).
  
  
• Asymptotic Active Learning.
  With Eyal Even-Dar, Steve Hanneke, Michael Kearns, Yishay Mansour, and Jennifer Wortman. NIPS 2007 Workshop on Principles of Learning Design Problem.
  
  
• Open Problems in Efficient Semi-Supervised PAC Learning.
  With Avrim Blum. COLT 2007.
  
  
• Mechanism Design, Machine Learning, and Pricing Problems.
  With Avrim Blum. SIGecom Exchanges 2007, special issue on Combinatorial Auctions (Invited).
  
  
• Random Sampling Auctions for Limited Supply.
  With Nikhil Devanur, Jason D. Hartline, and Kunal Talwar. Technical Report, CMU-CS-07-154, 2007.
  

• An Augmented PAC Model for Semi-Supervised Learning.
  With Avrim Blum. Book Chapter in Semi-Supervised Learning, Chapelle, O., Zien, A., and Scholkopf, B. (Eds.), MIT Press, 2006.
  
  
• On a Theory of Learning with Similarity Functions.
  With Avrim Blum. ICML 2006.
  
  Combined with subsequent paper by Nathan Srebro in journal version, Machine Learning Journal, 72(1-2):89--112, 2008. DOI 10.1007/s10994-008-5059-5. Special issue for COLT 2007 (Invited).
  
  
• Agnostic Active Learning.
  With Alina Beygelzimer and John Langford. Journal of Computer and System Sciences, 75(1):78--89, 2009. Special issue on Learning Theory for best papers in 2006 (Invited).
  
  A preliminary version of this paper appears in the Proceedings of the 23rd International Conference on Machine Learning (ICML) 2006.
  
  
• Approximation and Online Mechanisms for Item Pricing.
  With Avrim Blum. ACM Conference on Electronic Commerce, 2006.
  
  An extended version appears in Theory of Computing, 3/9 : 179--195, 2007.
  
  

• A PAC-style Model for Learning from Labeled and Unlabeled Data.
  With Avrim Blum. COLT 2005.
  
  Extended version appears under the title A Discriminative Model for Semi-Supervised Learning in Journal of the ACM, 2010.
  
  
• Search and Knowledge Representation in Artificial Intelligence. Theory and Applications.
  With Florentina Hristea. University of Bucharest Publishing House, 2005 (in Romanian).
  
  
• Mechanism Design via Machine Learning.
  With Avrim Blum, Jason D. Hartline, and Yishay Mansour. Proceedings of the 46th Annual Symposium on Foundations of Computer Science (FOCS) 2005.
  
  Extended version appears under the title Reducing Mechanism Design to Algorithm Design via Machine Learning in Journal of Computer and System Sciences, 74:1245--1270, 2008. Special issue on Learning Theory for best papers in 2005 (Invited).
  
  A related paper on Sponsored Search Auction Design via Machine Learning appears in the Workshop on Sponsored Search Auctions, 2005.
  
  
• Person Identification in Webcam Images: An Application of Semi-Supervised Learning.
  With Avrim Blum, Pakyan Choi, John Lafferty, Brian Pantano, Mugizi Rwebangira, and Xiaojin Zhu. ICML 2005 Workshop on Learning with Partially Classified Training Data.
  
  

• On Kernels, Margins, and Low-dimensional Mappings.
  With Avrim Blum and Santosh Vempala. Proceedings of the 15th International Conference on Algorithmic Learning Theory (ALT) 2004.
  
  Extended version appears under the title Kernels as Features: On Kernels, Margins, and Low-dimensional Mappings in Machine Learning Journal, 65(1):79--94, 2006.
  
  
• Co-Training and Expansion: Towards Bridging Theory and Practice.
  With Avrim Blum and Ke Yang. NIPS 2004.
  
  

• Handwritten Text Localization in Skewed Documents.
  With Ergina Kavallieratou, Doru Balcan, and Nikos Fakotakis. International Conference on Image Processing (ICIP) 2001.
  
  
• An Adaptive Resonance Theory (ART) Based Approach to Handwritten/ Machine-Printed Text Discrimination.
  With Doru Balcan. ICC&IE 2001.