Alekh Agarwal

	2012
28		Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire: Contextual Bandit Learning with Predictable Rewards CoRR abs/1202.1334: (2012)
27		John C. Duchi, Alekh Agarwal, Martin J. Wainwright: Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling. IEEE Trans. Automat. Contr. 57(3): 592-606 (2012)
26		Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright: Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Transactions on Information Theory 58(5): 3235-3249 (2012)
25		Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire: Contextual Bandit Learning with Predictable Rewards. Journal of Machine Learning Research - Proceedings Track 22: 19-26 (2012)
	2011
24		Alekh Agarwal, Sahand Negahban, Martin J. Wainwright: Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. ICML 2011: 1129-1136
23		Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin: Stochastic convex optimization with bandit feedback. NIPS 2011: 1035-1043
22		Alekh Agarwal, John C. Duchi: Distributed Delayed Stochastic Optimization. NIPS 2011: 873-881
21		Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett: Learning with Missing Features. UAI 2011: 635-642
20		Alekh Agarwal, Sahand Negahban, Martin J. Wainwright: Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions CoRR abs/1102.4807: (2011)
19		Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett: Online and Batch Learning Algorithms for Data with Missing Features CoRR abs/1104.0729: (2011)
18		Alekh Agarwal, Sahand Negahban, Martin J. Wainwright: Fast global convergence of gradient methods for high-dimensional statistical recovery CoRR abs/1104.4824: (2011)
17		Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin: Stochastic convex optimization with bandit feedback CoRR abs/1107.1744: (2011)
16		Alekh Agarwal, John C. Duchi: The Generalization Ability of Online Algorithms for Dependent Data CoRR abs/1110.2529: (2011)
15		Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford: A Reliable Effective Terascale Linear Learning System CoRR abs/1110.4198: (2011)
14		Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard: Oracle inequalities for computationally budgeted model selection. Journal of Machine Learning Research - Proceedings Track 19: 69-86 (2011)
	2010
13		Alekh Agarwal, Ofer Dekel, Lin Xiao: Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. COLT 2010: 28-40
12		Alekh Agarwal, Sahand Negahban, Martin J. Wainwright: Fast global convergence rates of gradient methods for high-dimensional statistical recovery. NIPS 2010: 37-45
11		John C. Duchi, Alekh Agarwal, Martin J. Wainwright: Distributed Dual Averaging In Networks. NIPS 2010: 550-558
10		Pradeep D. Ravikumar, Alekh Agarwal, Martin J. Wainwright: Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. Journal of Machine Learning Research 11: 1043-1080 (2010)
9		Alekh Agarwal, Peter L. Bartlett, Max Dama: Optimal Allocation Strategies for the Dark Pool Problem. Journal of Machine Learning Research - Proceedings Track 9: 9-16 (2010)
	2009
8		Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality. COLT 2009
7		Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright: Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS 2009: 1-9
6		Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality CoRR abs/0903.5328: (2009)
	2008
5		Pradeep D. Ravikumar, Alekh Agarwal, Martin J. Wainwright: Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. ICML 2008: 800-807
	2007
4		Alekh Agarwal, Soumen Chakrabarti: Learning random walks to rank nodes in graphs. ICML 2007: 9-16
3		Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf: An Analysis of Inference with the Universum. NIPS 2007
	2006
2		Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal: Learning to rank networked entities. KDD 2006: 14-23
1		Soumen Chakrabarti, Alekh Agarwal: Learning Parameters in Entity Relationship Graphs from Ranking Preferences. PKDD 2006: 91-102

Coauthor Index

1	Jacob Abernethy	[6] [8]
2	Sunny Aggarwal	[2]
3	Peter L. Bartlett	[6] [7] [8] [9] [14] [19] [21] [26]
4	Soumen Chakrabarti	[1] [2] [4]
5	Olivier Chapelle	[3] [15]
6	Max Dama	[9]
7	Ofer Dekel	[13]
8	John C. Duchi	[11] [14] [16] [22] [27]
9	Miroslav Dudík	[15] [25] [28]
10	Dean P. Foster	[17] [23]
11	Daniel Hsu	[17] [23]
12	Sham M. Kakade (Sham Kakade)	[17] [23]
13	Satyen Kale	[25] [28]
14	John Langford	[15] [25] [28]
15	Clement Levrard	[14]
16	Sahand Negahban	[12] [18] [20] [24]
17	Alexander Rakhlin	[6] [8] [17] [23]
18	Pradeep D. Ravikumar	[5] [7] [10] [26]
19	Afshin Rostamizadeh	[19] [21]
20	Robert E. Schapire	[25] [28]
21	Bernhard Schölkopf	[3]
22	Fabian H. Sinz	[3]
23	Martin J. Wainwright	[5] [7] [10] [11] [12] [18] [20] [24] [26] [27]
24	Lin Xiao	[13]

Colors in the list of coauthors

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