D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--35, 1999. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents
Efficient Algorithms for Online Decision Problems - Kalai, Vempala (2003) (2 citations) (Correct)
....algorithm as well as a lazy one that rarely switches between decisions. 1 Introduction In an online decision problem, one has to make a sequence of decisions without knowledge of the future. Exponential weighting schemes for these problems have been discovered and rediscovered in may areas [7]. Even in learning, there are too many results to mention (for a survey, see [1] We show that Hannan s original idea of doing what worked best against the past (with perturbed totals) gives efficient and simple algorithms for online decision problems. We extend his algorithm to get ....
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, vol.29, pp.1084-1090, 1999.
On Optimal Sequential Prediction for General Processes - Nobel (2001) (Correct)
..... Aggregating methods, and corresponding bounds on the di erence between the loss of the aggregate scheme and that of the best scheme in the family, have been established in a variety of settings. Representative work and further references can be found in [41, 17, 27, 10, 9, 25] Foster and Vohra [19] give an account of the aggregating problem and its history. Merhav and Feder [28] give an overview of prediction from individual sequences. Weissman and Merhav [43] establish nite sample aggregation bounds for the prediction of individual binary sequences observed in additive, independent noise, ....
D.P. Foster and R. Vohra, Regret in the on-line decision problem, Games and Economic Behavior, vol.29, pp.1084-1090, 1999.
The Non-Stochastic Multi-Armed Bandit Problem - Auer, Cesa-Bianchi (2002) (10 citations) (Correct)
....A desirable property for a player is Hannan consistency, which is similar to saying (in our bandit framework) that the weak regret per time step of the player converges to 0 with probability 1. Examples of Hannan consistent player strategies have been provided by several authors in the past (see [18] for a survey of these results) By applying (slight extensions of) Theorems 6.3 and 6.4, we can prove provide an example of a simple Hannan consistent player whose convergence rate is optimal up to logarithmic factors. Our player algorithms are based in part on an algorithm presented by Freund ....
....is Hannan consistency [8] defined as follows. Player i is Hannan consistent if lim sup T 1 max j2S i R (j) i (T ) 0 with probability 1. The existence and properties of Hannan consistent players have been first investigated by Hannan [10] and Blackwell [2] and later by many others (see [18] for a nice survey) Hannan consistency can be also studied in the so called unknown game setup , where it is further assumed that: 1) each player knows neither the total number of players nor the payoff function of any player (including itself) 2) after each round each player sees its own ....
[Article contains additional citation context not shown here]
Dean P. Foster and Rakesh Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--36, 1999.
On No-Regret Learning, Fictitious Play, and Nash Equilibrium - Jafari, Greenwald, Gondek (Correct)
....i (s 0 i ; s i js i ) Correlated equilibrium generalizes the notion of Nash equilibrium by allowing for correlations among the players strategies. An algorithm achieves no conditional regret i its empirical distribution of play converges to correlated equilibrium (see, for example, [3, 11]) In general, no conditional regret implies no regret, and these two properties are equivalent in two strategy games. Hence, no regret al..gorithms are guaranteed to converge to correlated equilibrium in 2 2 games. By studying the conditional regret matrices given opposing sequence of ....
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
Potential-based Algorithms in On-line Prediction and Game.. - Cesa-Bianchi, Lugosi (Correct)
....k. Variants of the learning with experts framework, such as shifting experts or the more general specialists [11] can be analyzed using generalized regret. Example 16. An important special case of the generalized regret (9) is the socalled internal or conditional regret [19] see also [7] for a survey) In this case the N = m(m 1) experts are labeled by pairs (i; j) for i 6= j. Expert (i; j) predicts always i, that is, f (i;j) t = i for all t, and it is active only when the predictor s guess is j, that is, A (i;j) k; t) 1 if and only if k = j. Thus, component (i; j) of the ....
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7-36, 1999.
On Optimal Sequential Decisions Schemes for General Processes - Nobel (2000) (Correct)
....in the family for every random process. Aggregating methods, and corresponding bounds on the di erence between the loss of the aggregate scheme and that of the best scheme in the family, have been established in a variety of settings. Representative work and further references can be found in [28, 12, 22, 6, 5, 23, 20, 14]. Here we describe a simple aggregate decision scheme that is based on weighted majority methods [28, 22] for predicting individual binary sequences. Let F be a xed, countable family of decision schemes and let x = x 1 ; x 2 ; be a sequence with values x i 2 X . Fix 2 (0; 1) and let fF j ....
D.P. Foster and R. Vohra. Regret in the on-line decision problem, Games and Economic Behavior, vol. 29, pp. 1084-1090, 1999.
Computing Equilibria in Multi-Player Games - Christos Papadimitriou Tim (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--35, 1999.
Correlated-Q Learning - Amy Greenwald Amy (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
Probabilistic Pricebots - Amy Greenwald Department (2000) (3 citations) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
From External to Internal Regret - Avrim Blum Avrim (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--36, 1999.
On-Line Algorithms For Combining Language Models - Adam Kalai Stanley (1998) (3 citations) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. In Something for Nothing Workshop, May 1995.
Bounds for Regret-Matching Algorithms - Amy Greenwald Amy (Correct)
No context found.
Dean Foster and Rakesh Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29: 7--35, 1999.
Bounds for Regret-Matching Algorithms - Amy Greenwald Amy (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem, 1995.
From External to Internal Regret - Blum, Mansour (2004) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40--55, 1997.
Correlated-Q Learning - Amy (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
Learning in the Santa Fe Bar Problem - Amy Greenwald Amy (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40--55, 1997.
Online Learning of Non-stationary Sequences - Monteleoni, Jaakkola (2003) (1 citation) (Correct)
No context found.
D. P. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--35, 1999.
Computing Equilibria in Multi-Player Games - Papadimitriou, Roughgarden (2004) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--35, 1999.
Potential-based Algorithms in On-line Prediction and Game.. - Cesa-Bianchi, Lugosi (2001) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:736, 1999.
A General Class of No-Regret Learning Algorithms and.. - Greenwald, Jafari (2003) (1 citation) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
QnR-Learning in Markov Games - David Gondek Amy (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
On No-Regret Learning, Fictitious Play, and Nash.. - Greenwald, Jafari.. (2001) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 21:40-55, 1997.
Online Convex Programming and Generalized Infinitesimal Gradient .. - Zinkevich (2003) (6 citations) (Correct)
No context found.
D. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29(1):7-35, 1999.
Efficient Algorithms for Universal Portfolios - Kalai, Vempala (2002) (Correct)
No context found.
D.P. Foster and R.V. Vohra. Regret in the On-line Decision Problem. Games and Economic Behavior, 29(1/2):7-35, 1999.
Online Learning of Non-stationary Sequences - Claire Monteleoni And (2003) (1 citation) (Correct)
No context found.
D. P. Foster and R. Vohra. Regret in the on-line decision problem. Games and Economic Behavior, 29:7--35, 1999.
First 50 documents
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC