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Sham M. Kakade
Sham Kakade
Author information
- affiliation: Microsoft Research New England
- affiliation: Toyota Technological Institute at Chicago
- affiliation: University of Pennsylvania, Department of Statistics
- affiliation: University College London, Gatsby Computational Neuroscience Unit
2010 – today
- 2013
[j21]Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin: Stochastic Convex Optimization with Bandit Feedback. SIAM Journal on Optimization 23(1): 213-240 (2013)- [c49]Daniel Hsu, Sham M. Kakade: Learning mixtures of spherical gaussians: moment methods and spectral decompositions. ITCS 2013: 11-20
- [i27]Anima Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade: A Tensor Spectral Approach to Learning Mixed Membership Community Models. CoRR abs/1302.2684 (2013)
- 2012
- [j20]Daniel Hsu, Sham M. Kakade, Tong Zhang: A spectral algorithm for learning Hidden Markov Models. J. Comput. Syst. Sci. 78(5): 1460-1480 (2012)
- [j19]Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Foster: Domain Adaptation: A Small Sample Statistical Approach. Journal of Machine Learning Research - Proceedings Track 22: 960-968 (2012)
- [j18]Elad Hazan, Sham M. Kakade: (weak) Calibration is Computationally Hard. Journal of Machine Learning Research - Proceedings Track 23: 3.1-3.10 (2012)
- [j17]Daniel Hsu, Sham M. Kakade, Tong Zhang: Random Design Analysis of Ridge Regression. Journal of Machine Learning Research - Proceedings Track 23: 9.1-9.24 (2012)
- [j16]Animashree Anandkumar, Daniel Hsu, Sham M. Kakade: A Method of Moments for Mixture Models and Hidden Markov Models. Journal of Machine Learning Research - Proceedings Track 23: 33.1-33.34 (2012)
- [j15]Sébastien Bubeck, Nicolò Cesa-Bianchi, Sham M. Kakade: Towards Minimax Policies for Online Linear Optimization with Bandit Feedback. Journal of Machine Learning Research - Proceedings Track 23: 41.1-41.14 (2012)
- [j14]Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger: Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. IEEE Transactions on Information Theory 58(5): 3250-3265 (2012)
- [c48]Anima Anandkumar, Dean P. Foster, Daniel Hsu, Sham Kakade, Yi-Kai Liu: A Spectral Algorithm for Latent Dirichlet Allocation. NIPS 2012: 926-934
- [c47]Animashree Anandkumar, Daniel Hsu, Furong Huang, Sham Kakade: Learning Mixtures of Tree Graphical Models. NIPS 2012: 1061-1069
- [c46]Daniel Hsu, Sham M. Kakade, Percy Liang: Identifiability and Unmixing of Latent Parse Trees. NIPS 2012: 1520-1528
- [i26]Sébastien Bubeck, Nicolò Cesa-Bianchi, Sham M. Kakade: Towards minimax policies for online linear optimization with bandit feedback. CoRR abs/1202.3079 (2012)
- [i25]
- [i24]Animashree Anandkumar, Daniel Hsu, Sham M. Kakade: A Method of Moments for Mixture Models and Hidden Markov Models. CoRR abs/1203.0683 (2012)
- [i23]Animashree Anandkumar, Daniel Hsu, Sham M. Kakade: Learning High-Dimensional Mixtures of Graphical Models. CoRR abs/1203.0697 (2012)
- [i22]Animashree Anandkumar, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Yi-Kai Liu: Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation. CoRR abs/1204.6703 (2012)
- [i21]Daniel Hsu, Sham M. Kakade, Percy Liang: Identifiability and Unmixing of Latent Parse Trees. CoRR abs/1206.3137 (2012)
- [i20]Daniel Hsu, Sham M. Kakade: Learning Gaussian Mixture Models: Moment Methods and Spectral Decompositions. CoRR abs/1206.5766 (2012)
- [i19]Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: Planning in POMDPs Using Multiplicity Automata. CoRR abs/1207.1388 (2012)
- [i18]Animashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham M. Kakade: Learning Linear Bayesian Networks with Latent Variables. CoRR abs/1209.5350 (2012)
- [i17]Anima Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky: Tensor decompositions for learning latent variable models. CoRR abs/1210.7559 (2012)
- [i16]Daniel Hsu, Sham M. Kakade, Tong Zhang: Analysis of a randomized approximation scheme for matrix multiplication. CoRR abs/1211.5414 (2012)
- 2011
- [j13]John Blitzer, Sham Kakade, Dean P. Foster: Domain Adaptation with Coupled Subspaces. Journal of Machine Learning Research - Proceedings Track 15: 173-181 (2011)
- [j12]Sham M. Kakade, Ulrike von Luxburg: Preface. Journal of Machine Learning Research - Proceedings Track 19 (2011)
- [j11]Sham M. Kakade, Ilan Lobel, Hamid Nazerzadeh: Optimal dynamic mechanism design via a virtual VCG mechanism. SIGecom Exchanges 10(1): 27-30 (2011)
- [j10]Daniel Hsu, Sham M. Kakade, Tong Zhang: Robust Matrix Decomposition With Sparse Corruptions. IEEE Transactions on Information Theory 57(11): 7221-7234 (2011)
- [c45]Sham M. Kakade, Adam Kalai, Varun Kanade, Ohad Shamir: Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. NIPS 2011: 927-935
- [c44]Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin: Stochastic convex optimization with bandit feedback. NIPS 2011: 1035-1043
- [c43]Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu, Sham M. Kakade, Le Song, Tong Zhang: Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS 2011: 2025-2033
- [e1]Sham M. Kakade, Ulrike von Luxburg (Eds.): COLT 2011 - The 24th Annual Conference on Learning Theory, June 9-11, 2011, Budapest, Hungary. JMLR.org 2011
- [i15]Sham Kakade, Adam Tauman Kalai, Varun Kanade, Ohad Shamir: Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. CoRR abs/1104.2018 (2011)
- [i14]Dean P. Foster, Sham M. Kakade, Ruslan Salakhutdinov: Domain Adaptation: Overfitting and Small Sample Statistics. CoRR abs/1105.0857 (2011)
- [i13]Daniel Hsu, Sham M. Kakade, Tong Zhang: An Analysis of Random Design Linear Regression. CoRR abs/1106.2363 (2011)
- [i12]Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu, Sham M. Kakade, Le Song, Tong Zhang: Spectral Methods for Learning Multivariate Latent Tree Structure. CoRR abs/1107.1283 (2011)
- [i11]Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin: Stochastic convex optimization with bandit feedback. CoRR abs/1107.1744 (2011)
- [i10]Daniel Hsu, Sham M. Kakade, Tong Zhang: A tail inequality for quadratic forms of subgaussian random vectors. CoRR abs/1110.2842 (2011)
- [i9]Nicolò Cesa-Bianchi, Sham Kakade: An Optimal Algorithm for Linear Bandits. CoRR abs/1110.4322 (2011)
- 2010
- [j9]Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari: Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. Journal of Machine Learning Research - Proceedings Track 9: 381-388 (2010)
- [j8]Sham Kakade, Ping Li: Guest editorial: special issue on learning theory. Machine Learning 80(2-3): 109-110 (2010)
- [c42]Niranjan Srinivas, Andreas Krause, Sham Kakade, Matthias Seeger: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. ICML 2010: 1015-1022
- [c41]Alexander L. Strehl, John Langford, Lihong Li, Sham Kakade: Learning from Logged Implicit Exploration Data. NIPS 2010: 2217-2225
- [i8]Sham M. Kakade, Ilan Lobel, Hamid Nazerzadeh: An Optimal Dynamic Mechanism for Multi-Armed Bandit Processes. CoRR abs/1001.4598 (2010)
- [i7]Alexander L. Strehl, John Langford, Sham M. Kakade: Learning from Logged Implicit Exploration Data. CoRR abs/1003.0120 (2010)
- [i6]Daniel Hsu, Sham M. Kakade, Tong Zhang: Robust Matrix Decomposition with Outliers. CoRR abs/1011.1518 (2010)
2000 – 2009
- 2009
- [j7]Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: Online Markov Decision Processes. Math. Oper. Res. 34(3): 726-736 (2009)
- [j6]Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett: Playing Games with Approximation Algorithms. SIAM J. Comput. 39(3): 1088-1106 (2009)
- [c40]Daniel Hsu, Sham M. Kakade, Tong Zhang: A Spectral Algorithm for Learning Hidden Markov Models. COLT 2009
- [c39]Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan: Multi-view clustering via canonical correlation analysis. ICML 2009: 17
- [c38]Daniel Hsu, Sham Kakade, John Langford, Tong Zhang: Multi-Label Prediction via Compressed Sensing. NIPS 2009: 772-780
- [c37]Nikhil R. Devanur, Sham M. Kakade: The price of truthfulness for pay-per-click auctions. ACM Conference on Electronic Commerce 2009: 99-106
- [i5]Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang: Multi-Label Prediction via Compressed Sensing. CoRR abs/0902.1284 (2009)
- [i4]Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari: Applications of strong convexity--strong smoothness duality to learning with matrices. CoRR abs/0910.0610 (2009)
- [i3]Sham M. Kakade, Ohad Shamir, Karthik Sridharan, Ambuj Tewari: Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. CoRR abs/0911.0054 (2009)
- [i2]Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias Seeger: Gaussian Process Bandits without Regret: An Experimental Design Approach. CoRR abs/0912.3995 (2009)
- 2008
- [j5]Sham M. Kakade, Dean P. Foster: Deterministic calibration and Nash equilibrium. J. Comput. Syst. Sci. 74(1): 115-130 (2008)
- [j4]Matthias W. Seeger, Sham M. Kakade, Dean P. Foster: Information Consistency of Nonparametric Gaussian Process Methods. IEEE Transactions on Information Theory 54(5): 2376-2382 (2008)
- [c36]Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham Kakade, Alexander Rakhlin, Ambuj Tewari: High-Probability Regret Bounds for Bandit Online Linear Optimization. COLT 2008: 335-342
- [c35]Varsha Dani, Thomas P. Hayes, Sham M. Kakade: Stochastic Linear Optimization under Bandit Feedback. COLT 2008: 355-366
- [c34]Karthik Sridharan, Sham M. Kakade: An Information Theoretic Framework for Multi-view Learning. COLT 2008: 403-414
- [c33]Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari: Efficient bandit algorithms for online multiclass prediction. ICML 2008: 440-447
- [c32]Sham M. Kakade, Karthik Sridharan, Ambuj Tewari: On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. NIPS 2008: 793-800
- [c31]Sham M. Kakade, Ambuj Tewari: On the Generalization Ability of Online Strongly Convex Programming Algorithms. NIPS 2008: 801-808
- [c30]Shai Shalev-Shwartz, Sham M. Kakade: Mind the Duality Gap: Logarithmic regret algorithms for online optimization. NIPS 2008: 1457-1464
- [i1]Daniel Hsu, Sham M. Kakade, Tong Zhang: A Spectral Algorithm for Learning Hidden Markov Models. CoRR abs/0811.4413 (2008)
- 2007
- [j3]Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade: Maximum Entropy Correlated Equilibria. Journal of Machine Learning Research - Proceedings Track 2: 347-354 (2007)
- [c29]Sham M. Kakade, Dean P. Foster: Multi-view Regression Via Canonical Correlation Analysis. COLT 2007: 82-96
- [c28]Deva Ramanan, Simon Baker, Sham Kakade: Leveragingarchivalvideo for building face datasets. ICCV 2007: 1-8
- [c27]Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: The Value of Observation for Monitoring Dynamic Systems. IJCAI 2007: 2474-2479
- [c26]Varsha Dani, Thomas P. Hayes, Sham Kakade: The Price of Bandit Information for Online Optimization. NIPS 2007
- [c25]Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett: Playing games with approximation algorithms. STOC 2007: 546-555
- 2006
- [c24]
- [c23]Eyal Even-Dar, Sham M. Kakade, Michael Kearns, Yishay Mansour: (In)Stability properties of limit order dynamics. ACM Conference on Electronic Commerce 2006: 120-129
- 2005
- [c22]
- [c21]Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: Reinforcement Learning in POMDPs Without Resets. IJCAI 2005: 690-695
- [c20]
- [c19]Sham M. Kakade, Matthias Seeger, Dean P. Foster: Worst-Case Bounds for Gaussian Process Models. NIPS 2005
- [c18]Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: Planning in POMDPs Using Multiplicity Automata. UAI 2005: 185-192
- 2004
- [c17]
- [c16]
- [c15]
- [c14]Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri: Economic Properties of Social Networks. NIPS 2004
- [c13]
- [c12]Sham Kakade, Michael J. Kearns, Yishay Mansour, Luis E. Ortiz: Competitive algorithms for VWAP and limit order trading. ACM Conference on Electronic Commerce 2004: 189-198
- 2003
- [c11]Sham Kakade, Michael J. Kearns, John Langford: Exploration in Metric State Spaces. ICML 2003: 306-312
- [c10]J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff G. Schneider: Policy Search by Dynamic Programming. NIPS 2003
- [c9]Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz: Correlated equilibria in graphical games. ACM Conference on Electronic Commerce 2003: 42-47
- 2002
- [j2]Sham Kakade, Peter Dayan: Dopamine: generalization and bonuses. Neural Networks 15(4-6): 549-559 (2002)
- [j1]Nathaniel D. Daw, Sham Kakade, Peter Dayan: Opponent interactions between serotonin and dopamine. Neural Networks 15(4-6): 603-616 (2002)
- [c8]Sham Kakade, John Langford: Approximately Optimal Approximate Reinforcement Learning. ICML 2002: 267-274
- [c7]Sham Kakade, Yee Whye Teh, Sam T. Roweis: An Alternate Objective Function for Markovian Fields. ICML 2002: 275-282
- [c6]John Langford, Martin Zinkevich, Sham Kakade: Competitive Analysis of the Explore/Exploit Tradeoff. ICML 2002: 339-346
- 2001
- [c5]
- [c4]
- 2000
- [c3]
- [c2]
1990 – 1999
- 1999
- [c1]
Coauthor Index
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last updated on 2013-04-20 20:23 CEST by the dblp team