-
1
-
-
34547984768
-
1-regularized log-linear models
-
Corvallis, OR, USA
-
1-regularized log-linear models. In Proceedings of the 24th International Conference on Machine Learning (ICML), pages 33-40, Corvallis, OR, USA, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning (ICML)
, pp. 33-40
-
-
Andrew, G.1
Gao, J.2
-
2
-
-
33747151859
-
Interior gradient and proximal methods for convex and conic optimization
-
A. Auslender and M. Teboulle. Interior gradient and proximal methods for convex and conic optimization. SIAM Journal on Optimization, 16:697-725, 2006.
-
(2006)
SIAM Journal on Optimization
, vol.16
, pp. 697-725
-
-
Auslender, A.1
Teboulle, M.2
-
3
-
-
84972574511
-
Weighted sums of certain dependent random variables
-
K. Azuma. Weighted sums of certain dependent random variables. Tohoku Mathematical Journal, 19:357-367, 1967.
-
(1967)
Tohoku Mathematical Journal
, vol.19
, pp. 357-367
-
-
Azuma, K.1
-
4
-
-
41549108812
-
Algorithms for sparse linear classifiers in the massive data setting
-
S. Balakrishnan and D. Madigan. Algorithms for sparse linear classifiers in the massive data setting. Journal of Machine Learning Research, 9:313-337, 2008.
-
(2008)
Journal of Machine Learning Research
, vol.9
, pp. 313-337
-
-
Balakrishnan, S.1
Madigan, D.2
-
5
-
-
85162021730
-
Adaptive online gradient descent
-
J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, MIT Press, Cambridge, MA
-
P. Bartlett, E. Hazan, and A. Rakhlin. Adaptive online gradient descent. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 65-72. MIT Press, Cambridge, MA, 2008.
-
(2008)
Advances in Neural. Information Processing Systems
, vol.20
, pp. 65-72
-
-
Bartlett, P.1
Hazan, E.2
Rakhlin, A.3
-
6
-
-
85014561619
-
A fast iterative shrinkage-threshold algorithm for linear inverse problems
-
A. Beck and M. Teboulle. A fast iterative shrinkage-threshold algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2 (1):183-202, 2009.
-
(2009)
SIAM Journal on Imaging Sciences
, vol.2
, Issue.1
, pp. 183-202
-
-
Beck, A.1
Teboulle, M.2
-
7
-
-
85162035281
-
The tradeoffs of large scale learning
-
J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, MIT Press, Cambridge, MA
-
L. Bottou and O. Bousquet. The tradeoffs of large scale learning. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 161-168. MIT Press, Cambridge, MA, 2008.
-
(2008)
Advances in Neural. Information Processing Systems
, vol.20
, pp. 161-168
-
-
Bottou, L.1
Bousquet, O.2
-
8
-
-
84899022736
-
Large scale online learning
-
S. Thrun, L. Saul, and B. Schölkopf, editors, MIT Press, Cambridge, MA
-
L. Bottou and Y. LeCun. Large scale online learning. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, pages 217-224. MIT Press, Cambridge, MA, 2004.
-
(2004)
Advances in Neural. Information Processing Systems
, vol.16
, pp. 217-224
-
-
Bottou, L.1
LeCun, Y.2
-
10
-
-
76749123278
-
Differentiable sparse coding
-
D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, MIT Press, Cambridge, MA, USA
-
D. M. Bradley and J. A. Bagnell. Differentiable sparse coding. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 113-120. MIT Press, Cambridge, MA, USA, 2009.
-
(2009)
Advances in Neural. Information Processing Systems
, vol.21
, pp. 113-120
-
-
Bradley, D.M.1
Bagnell, J.A.2
-
11
-
-
41449091142
-
Iterated hard shrinkage for minimization problems with sparsity constraints
-
K. Bredies and D. A. Lorenz. Iterated hard shrinkage for minimization problems with sparsity constraints. SIAM Journal on Scientific Computing, 30 (2):657-683, 2008.
-
(2008)
SIAM Journal on Scientific Computing
, vol.30
, Issue.2
, pp. 657-683
-
-
Bredies, K.1
Lorenz, D.A.2
-
12
-
-
78649405622
-
An interior-point stochastic approximation method and an l1-regularized delta rule
-
D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, MIT Press
-
P. Carbonetto, M. Schmidt, and N. De Freitas. An interior-point stochastic approximation method and an l1-regularized delta rule. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 233-240. MIT Press, 2009.
-
(2009)
Advances in Neural. Information Processing Systems
, vol.21
, pp. 233-240
-
-
Carbonetto, P.1
Schmidt, M.2
De Freitas, N.3
-
14
-
-
0000433247
-
Convergence analysis of a proximal-like minimization algorithm using Bregman functions
-
August
-
G. Chen and M. Teboulle. Convergence analysis of a proximal-like minimization algorithm using Bregman functions. SIAM Journal on Optimization, 3 (3):538-543, August 1993.
-
(1993)
SIAM Journal on Optimization
, vol.3
, Issue.3
, pp. 538-543
-
-
Chen, G.1
Teboulle, M.2
-
17
-
-
75249102673
-
Efficient online and batch learning using forward backward splitting
-
J. Duchi and Y. Singer. Efficient online and batch learning using forward backward splitting. Journal of Machine Learning Research, 10:2873-2898, 2009.
-
(2009)
Journal of Machine Learning Research
, vol.10
, pp. 2873-2898
-
-
Duchi, J.1
Singer, Y.2
-
19
-
-
80052423377
-
Adaptive subgradient methods for online learning and stochastic optimization
-
To appear in
-
J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. To appear in Journal of Machine Learning Research, 2010.
-
(2010)
Journal of Machine Learning Research
-
-
Duchi, J.1
Hazan, E.2
Singer, Y.3
-
20
-
-
0041657519
-
Interior-point methods for massive support vector machines
-
M. C. Ferris and T. S. Munson. Interior-point methods for massive support vector machines. SIAM Journal on Optimization, 13 (3):783-804, 2003.
-
(2003)
SIAM Journal on Optimization
, vol.13
, Issue.3
, pp. 783-804
-
-
Ferris, M.C.1
Munson, T.S.2
-
21
-
-
39449126969
-
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
-
M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal on Selected Topics in Signal Processing, 1 (4):586-597, 2007.
-
(2007)
IEEE Journal on Selected Topics in Signal Processing
, vol.1
, Issue.4
, pp. 586-597
-
-
Figueiredo, M.A.T.1
Nowak, R.D.2
Wright, S.J.3
-
22
-
-
0002384441
-
On tail probabilities for martingales
-
D. A. Freedman. On tail probabilities for martingales. The Annals of Probability, 3 (1):100-118, 1975.
-
(1975)
The Annals of Probability
, vol.3
, Issue.1
, pp. 100-118
-
-
Freedman, D.A.1
-
23
-
-
0344875562
-
The robustness of the p-norm algorithms
-
C. Gentile. The robustness of the p-norm algorithms. Machine Learning, 53:265-299, 2003.
-
(2003)
Machine Learning
, vol.53
, pp. 265-299
-
-
Gentile, C.1
-
24
-
-
45849097241
-
Local strong convexity and local Lipschitz continuity of the gradient of convex functions
-
R. Goebel and R. T. Rockafellar. Local strong convexity and local Lipschitz continuity of the gradient of convex functions. Journal of Convex Analysis, 15 (2):263-270, 2008.
-
(2008)
Journal of Convex Analysis
, vol.15
, Issue.2
, pp. 263-270
-
-
Goebel, R.1
Rockafellar, R.T.2
-
25
-
-
33746081666
-
Logarithmic regret algorithms for online convex optimization
-
Pittsburgh, PA, USA
-
E. Hazan, A. Kalai, S. Kale, and A. Agarwal. Logarithmic regret algorithms for online convex optimization. In Proceedings of 19th Annual Conference on Computational Learning Theory (COLT), pages 499-513, Pittsburgh, PA, USA, 2006.
-
(2006)
Proceedings of 19th Annual Conference on Computational Learning Theory (COLT)
, pp. 499-513
-
-
Hazan, E.1
Kalai, A.2
Kale, S.3
Agarwal, A.4
-
27
-
-
77956508892
-
Accelerated gradient methods for stochastic optimization and online learning
-
Y Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors
-
C. Hu, J. T. Kwok, and W. Pan. Accelerated gradient methods for stochastic optimization and online learning. In Y Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 781-789. 2009.
-
(2009)
Advances in Neural. Information Processing Systems
, vol.22
, pp. 781-789
-
-
Hu, C.1
Kwok, J.T.2
Pan, W.3
-
28
-
-
73249115078
-
A randomized incremental subgradient method for distributed optimization in networked systems
-
B. Johansson, M. Rabi, and M. Johansson. A randomized incremental subgradient method for distributed optimization in networked systems. SIAM Journal on Optimization, 20 (3):1157-1170, 2009.
-
(2009)
SIAM Journal on Optimization
, vol.20
, Issue.3
, pp. 1157-1170
-
-
Johansson, B.1
Rabi, M.2
Johansson, M.3
-
29
-
-
84864918805
-
Large deviations of vector-valued martingales in 2-smooth normed spaces
-
Manuscript submitted to, arXiv:0809.0813vl
-
A. Juditsky and A. Nemirovski. Large deviations of vector-valued martingales in 2-smooth normed spaces. Manuscript submitted to The Annals of Probability, 2008. arXiv:0809.0813vl.
-
(2008)
The Annals of Probability
-
-
Juditsky, A.1
Nemirovski, A.2
-
30
-
-
31344435933
-
Recursive aggregation of estimators by mirror descent algorithm with averaging
-
A. Juditsky, A. Nazin, A. Tsybakov, and N. Vayatis. Recursive aggregation of estimators by mirror descent algorithm with averaging. Problems of Information Transmission, 41 (4):368-384, 2005.
-
(2005)
Problems of Information Transmission
, vol.41
, Issue.4
, pp. 368-384
-
-
Juditsky, A.1
Nazin, A.2
Tsybakov, A.3
Vayatis, N.4
-
31
-
-
78649399493
-
On the generalization ability of online strongly convex programming algorithms
-
D. Koller, D. Schuurmans, Y Bengio, and L. Bottou, editors, MIT Press, Cambridge, MA, USA
-
S. M. Kakade and A. Tewari. On the generalization ability of online strongly convex programming algorithms. In D. Koller, D. Schuurmans, Y Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 801-808. MIT Press, Cambridge, MA, USA, 2009.
-
(2009)
Advances in Neural. Information Processing Systems
, vol.21
, pp. 801-808
-
-
Kakade, S.M.1
Tewari, A.2
-
32
-
-
0001079593
-
Stochastic estimation of the maximum of a regression function
-
J. Kiefer and J. Wolfowitz. Stochastic estimation of the maximum of a regression function. The Annuals of Mathematical Statistics, 23:462-466, 1952.
-
(1952)
The Annuals of Mathematical Statistics
, vol.23
, pp. 462-466
-
-
Kiefer, J.1
Wolfowitz, J.2
-
33
-
-
0008815681
-
Exponentiated gradient versus gradient descent for linear predictors
-
J. Kivinen and M. K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. Information and Computation, 132 (1):1-63, 1997.
-
(1997)
Information and Computation
, vol.132
, Issue.1
, pp. 1-63
-
-
Kivinen, J.1
Warmuth, M.K.2
-
35
-
-
84862283078
-
An optimal method for stochastic composite optimization
-
To appear in
-
G. Lan. An optimal method for stochastic composite optimization. To appear in Mathematical Programming, 2010.
-
(2010)
Mathematical Programming
-
-
Lan, G.1
-
36
-
-
78649429189
-
Validation analysis of robust stochastic approximation methods
-
Submitted to
-
G. Lan, A. Nemirovski, and A. Shapiro. Validation analysis of robust stochastic approximation methods. Submitted to Mathematical Programming, 2008.
-
(2008)
Mathematical Programming
-
-
Lan, G.1
Nemirovski, A.2
Shapiro, A.3
-
37
-
-
78651417720
-
Primal-dual first-order methods with O (1/ε) iterationcomplexity for cone programming
-
February, Published online, DOI 10.1007/sl0107-008-0261-6
-
G. Lan, Z. Lu, and R. D. C. Monteiro. Primal-dual first-order methods with O (1/ε) iterationcomplexity for cone programming. Mathematical Programming, February 2009. Published online, DOI 10.1007/sl0107-008-0261-6.
-
(2009)
Mathematical Programming
-
-
Lan, G.1
Lu, Z.2
Monteiro, R.D.C.3
-
39
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
Dataset available at
-
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11):2278-2324, 1998. Dataset available at http://yann. lecun.com/exdb/mnist.
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.11
, pp. 2278-2324
-
-
LeCun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
40
-
-
0000345334
-
Splitting algorithms for the sum of two nonlinear operators
-
P.-L. Lions and B. Mercier. Splitting algorithms for the sum of two nonlinear operators. SIAM Journal on Numerical Analysis, 16:964-979, 1979.
-
(1979)
SIAM Journal on Numerical Analysis
, vol.16
, pp. 964-979
-
-
Lions, P.-L.1
Mercier, B.2
-
42
-
-
0036342213
-
Incremental subgradient methods for nondifferentiable optimization
-
A. Nedic and D. P. Bertsekas. Incremental subgradient methods for nondifferentiable optimization. SIAM Journal on Optimization, 12 (1):109-138, 2001.
-
(2001)
SIAM Journal on Optimization
, vol.12
, Issue.1
, pp. 109-138
-
-
Nedic, A.1
Bertsekas, D.P.2
-
43
-
-
70450197241
-
Robust stochastic approximation approach to stochastic programming
-
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro. Robust stochastic approximation approach to stochastic programming. SIAM Journal on Optimization, 19 (4):1574-1609, 2009.
-
(2009)
SIAM Journal on Optimization
, vol.19
, Issue.4
, pp. 1574-1609
-
-
Nemirovski, A.1
Juditsky, A.2
Lan, G.3
Shapiro, A.4
-
45
-
-
34548480020
-
2)
-
Translated from Russian by A. Rosa
-
2). Soviet Math. Doklady, 27 (2):372-376, 1983. Translated from Russian by A. Rosa.
-
(1983)
Soviet Math. Doklady
, vol.27
, Issue.2
, pp. 372-376
-
-
Nesterov, Yu.1
-
47
-
-
17444406259
-
Smooth minimization of nonsmooth functions
-
Yu. Nesterov. Smooth minimization of nonsmooth functions. Mathematical Programming, 103:127-152, 2005.
-
(2005)
Mathematical Programming
, vol.103
, pp. 127-152
-
-
Nesterov, Yu.1
-
48
-
-
67651063011
-
Gradient methods for minimizing composite objective function
-
Catholic University of Louvain, Center for Operations Research and Econometrics
-
Yu. Nesterov. Gradient methods for minimizing composite objective function. Technical Report 2007/76, Catholic University of Louvain, Center for Operations Research and Econometrics, 2007.
-
(2007)
Technical Report 2007/76
-
-
Nesterov, Yu.1
-
50
-
-
65249121279
-
Primal-dual subgradient methods for convex problems
-
Appeared early as CORE discussion paper 2005/67, Catholic University of Louvain, Center for Operations Research and Econometrics
-
Yu. Nesterov. Primal-dual subgradient methods for convex problems. Mathematical Programming, 120 (1):221-259, 2009. Appeared early as CORE discussion paper 2005/67, Catholic University of Louvain, Center for Operations Research and Econometrics.
-
(2009)
Mathematical Programming
, vol.120
, Issue.1
, pp. 221-259
-
-
Nesterov, Yu.1
-
51
-
-
44349128988
-
Confidence level solutions for stochastic programming
-
Yu. Nesterov and J.-Ph. Vial. Confidence level solutions for stochastic programming. Automatica, 44 (6):1559-1568, 2008.
-
(2008)
Automatica
, vol.44
, Issue.6
, pp. 1559-1568
-
-
Nesterov, Yu.1
Vial, J.-Ph.2
-
53
-
-
70450201554
-
Incremental stochastic subgradient algorithms for convex optimization
-
S. Sundhar Ram, A. Nedic, and V. V. Veeravalli. Incremental stochastic subgradient algorithms for convex optimization. SIAM Journal on Optimization, 20 (2):691-717, 2009.
-
(2009)
SIAM Journal on Optimization
, vol.20
, Issue.2
, pp. 691-717
-
-
Ram, S.S.1
Nedic, A.2
Veeravalli, V.V.3
-
57
-
-
77951165785
-
Mind the duality gap: Logarithmic regret algorithms for online optimization
-
D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, MIT Press
-
S. Shalev-Shwartz and S. M. Kakade. Mind the duality gap: Logarithmic regret algorithms for online optimization. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 1457-1464. MIT Press, 2009.
-
(2009)
Advances in Neural. Information Processing Systems
, vol.21
, pp. 1457-1464
-
-
Shalev-Shwartz, S.1
Kakade, S.M.2
-
58
-
-
84864036264
-
Convex repeated games and Fenchel duality
-
B. Schölkopf, J. Platt, and T. Hofmann, editors, MIT Press
-
S. Shalev-Shwartz and Y. Singer. Convex repeated games and Fenchel duality. In B. Schölkopf, J. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems, volume 19, pages 1265-1272. MIT Press, 2006.
-
(2006)
Advances in Neural. Information Processing Systems
, vol.19
, pp. 1265-1272
-
-
Shalev-Shwartz, S.1
Singer, Y.2
-
62
-
-
0032222083
-
An incremental gradient (-projection) method with momentum term and adaptive stepsize rule
-
P. Tseng. An incremental gradient (-projection) method with momentum term and adaptive stepsize rule. SIAM Journal on Optimization, 8 (2):506-531, 1998.
-
(1998)
SIAM Journal on Optimization
, vol.8
, Issue.2
, pp. 506-531
-
-
Tseng, P.1
-
63
-
-
0033884548
-
A modified forward-backward splitting method for maximal monotone mappings
-
P. Tseng. A modified forward-backward splitting method for maximal monotone mappings. SIAM Journal on Control and Optimization, 38 (2):431-446, 2000.
-
(2000)
SIAM Journal on Control and Optimization
, vol.38
, Issue.2
, pp. 431-446
-
-
Tseng, P.1
-
64
-
-
70049111607
-
On accelerated proximal gradient methods for convex-concave optimization
-
Manuscript submitted to
-
P. Tseng. On accelerated proximal gradient methods for convex-concave optimization. Manuscript submitted to SIAM Journal on Optimization, 2008.
-
(2008)
SIAM Journal on Optimization
-
-
Tseng, P.1
-
65
-
-
0027607344
-
On the convergence of exponential multiplier method for convex programming
-
P. Tseng and D. P. Bertsekas. On the convergence of exponential multiplier method for convex programming. Mathematical Programming, 60:1-19, 1993.
-
(1993)
Mathematical Programming
, vol.60
, pp. 1-19
-
-
Tseng, P.1
Bertsekas, D.P.2
-
66
-
-
67650178787
-
Sparse reconstruction by separable approximation
-
S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo. Sparse reconstruction by separable approximation. IEEE Transactions on Signal Processing, 57 (7):2479-2493, 2009.
-
(2009)
IEEE Transactions on Signal Processing
, vol.57
, Issue.7
, pp. 2479-2493
-
-
Wright, S.J.1
Nowak, R.D.2
Figueiredo, M.A.T.3
-
67
-
-
14344259207
-
Solving large scale linear prediction problems using stochastic gradient descent algorithms
-
Banff, Alberta, Canada
-
T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In Proceedings of the 21st International Conference on Machine Learning (ICML), pages 116-123, Banff, Alberta, Canada, 2004.
-
(2004)
Proceedings of the 21st International Conference on Machine Learning (ICML)
, pp. 116-123
-
-
Zhang, T.1
|