Sham M. Kakade

Sham Kakade

Microsoft Research New England

	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		Elad Hazan, Sham Kakade: (weak) Calibration is Computationally Hard. CoRR abs/1202.4478 (2012)
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)
	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		Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor. ICML 2006: 97-104
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		Sham M. Kakade, Michael J. Kearns: Trading in Markovian Price Models. COLT 2005: 606-620
c21		Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: Reinforcement Learning in POMDPs Without Resets. IJCAI 2005: 690-695
c20		Sham M. Kakade, Adam Kalai: From Batch to Transductive Online Learning. NIPS 2005
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		Sham Kakade, Michael J. Kearns, Luis E. Ortiz: Graphical Economics. COLT 2004: 17-32
c16		Sham Kakade, Dean P. Foster: Deterministic Calibration and Nash Equilibrium. COLT 2004: 33-48
c15		Eyal Even-Dar, Sham M. Kakade, Yishay Mansour: Experts in a Markov Decision Process. NIPS 2004
c14		Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri: Economic Properties of Social Networks. NIPS 2004
c13		Sham M. Kakade, Andrew Y. Ng: Online Bounds for Bayesian Algorithms. NIPS 2004
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		Sham Kakade: Optimizing Average Reward Using Discounted Rewards. COLT/EuroCOLT 2001: 605-615
c4		Sham Kakade: A Natural Policy Gradient. NIPS 2001: 1531-1538
	2000
c3		Sham Kakade, Peter Dayan: Dopamine Bonuses. NIPS 2000: 131-137
c2		Peter Dayan, Sham Kakade: Explaining Away in Weight Space. NIPS 2000: 451-457
	1999
c1		Sham Kakade, Peter Dayan: Acquisition in Autoshaping. NIPS 1999: 24-30

Coauthor Index

Colors in the list of coauthors

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