UAI 2001 - PDF Preprint Papers

UAI 2001

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UAI
2001

211views Artificial Intelligence» more UAI 2001»

Graphical Models for Game Theory

13 years 6 months ago

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We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equ...

Michael J. Kearns, Michael L. Littman, Satinder P....

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Id	Views	Title	Status

1	211	Graphical Models for Game Theory	claim
2	194	Expectation Propagation for approximate Bayesian inference	claim
3	173	A Calculus for Causal Relevance	claim
4	144	Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk	claim
5	143	A Clustering Approach to Solving Large Stochastic Matching Problems	claim
6	140	Learning the Dimensionality of Hidden Variables	claim
7	138	A Bayesian Multiresolution Independence Test for Continuous Variables	claim
8	129	The Optimal Reward Baseline for Gradient-Based Reinforcement Learning	claim
9	125	Markov Chain Monte Carlo using Tree-Based Priors on Model Structure	claim
10	125	Enumerating Markov Equivalence Classes of Acyclic Digraph Models	claim
11	123	A Logic for Reasoning about Upper Probabilities	claim
12	120	Classifier Learning with Supervised Marginal Likelihood	claim
13	120	Toward General Analysis of Recursive Probability Models	claim
14	118	Bayesian Error-Bars for Belief Net Inference	claim
15	117	Aggregating Learned Probabilistic Beliefs	claim
16	115	Causal Discovery from Changes	claim
17	114	Hypothesis Management in Situation-Specific Network Construction	claim
18	113	UCP-Networks: A Directed Graphical Representation of Conditional Utilities	claim
19	113	Improved learning of Bayesian networks	claim
20	111	A Bayesian Approach to Tackling Hard Computational Problems	claim
21	110	Instrumentality Tests Revisited	claim
22	109	Symmetric Collaborative Filtering Using the Noisy Sensor Model	claim
23	107	Similarity Measures on Preference Structures, Part II: Utility Functions	claim
24	105	Lattice Particle Filters	claim
25	102	Exact Inference in Networks with Discrete Children of Continuous Parents	claim
26	102	Using Temporal Data for Making Recommendations	claim
27	100	When do Numbers Really Matter?	claim
28	98	Policy Improvement for POMDPs Using Normalized Importance Sampling	claim
29	97	Linearity Properties of Bayes Nets with Binary Variables	claim
30	97	Multivariate Information Bottleneck	claim
31	97	Maximum Likelihood Bounded Tree-Width Markov Networks	claim
32	93	Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments	claim
33	89	Efficient Stepwise Selection in Decomposable Models	claim
34	86	Vector-space Analysis of Belief-state Approximation for POMDPs	claim
35	74	Pre-processing for Triangulation of Probabilistic Networks	claim
36	69	Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation	claim

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