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2008

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Hybrid Variational/Gibbs Collapsed Inference in Topic Models

1 years 6 months ago

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Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...

Max Welling, Yee Whye Teh, Bert Kappen

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Id	Views	Title	Status
1	123	Hybrid Variational/Gibbs Collapsed Inference in Topic Models	claim
2	96	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features	claim
3	93	Projected Subgradient Methods for Learning Sparse Gaussians	claim
4	91	CT-NOR: Representing and Reasoning About Events in Continuous Time	claim
5	85	Estimation and clustering with infinite rankings	claim
6	76	Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression	claim
7	75	Latent Topic Models for Hypertext	claim
8	74	Learning the Bayesian Network Structure: Dirichlet Prior vs Data	claim
9	74	Partitioned Linear Programming Approximations for MDPs	claim
10	74	Complexity of Inference in Graphical Models	claim
11	74	Almost Optimal Intervention Sets for Causal Discovery	claim
12	71	Multi-View Learning over Structured and Non-Identical Outputs	claim
13	71	Constrained Approximate Maximum Entropy Learning of Markov Random Fields	claim
14	70	Discovering Cyclic Causal Models by Independent Components Analysis	claim
15	69	Efficient Inference in Persistent Dynamic Bayesian Networks	claim
16	69	Refractor Importance Sampling	claim
17	69	Explanation Trees for Causal Bayesian Networks	claim
18	68	Gibbs Sampling in Factorized Continuous-Time Markov Processes	claim
19	68	Model-Based Bayesian Reinforcement Learning in Large Structured Domains	claim
20	68	A Game-Theoretic Analysis of Updating Sets of Probabilities	claim
21	68	Church: a language for generative models	claim
22	67	Improving the Accuracy and Efficiency of MAP Inference for Markov Logic	claim
23	67	Continuous Time Dynamic Topic Models	claim
24	67	Sensitivity analysis for finite Markov chains in discrete time	claim
25	66	A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games	claim
26	66	Sampling First Order Logical Particles	claim
27	66	Bounds on the Bethe Free Energy for Gaussian Networks	claim
28	65	On Identifying Total Effects in the Presence of Latent Variables and Selection bias	claim
29	65	Adaptive inference on general graphical models	claim
30	65	AND/OR Importance Sampling	claim
31	65	Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction	claim
32	65	Learning Hidden Markov Models for Regression using Path Aggregation	claim
33	65	Feature Selection via Block-Regularized Regression	claim
34	64	Bayesian network learning by compiling to weighted MAX-SAT	claim
35	64	New Techniques for Algorithm Portfolio Design	claim
36	64	Sensitivity analysis in decision circuits	claim
37	64	Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression	claim
38	63	Toward Experiential Utility Elicitation for Interface Customization	claim
39	63	CORL: A Continuous-state Offset-dynamics Reinforcement Learner	claim
40	63	Strategy Selection in Influence Diagrams using Imprecise Probabilities	claim
41	63	Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies	claim
42	63	Tightening LP Relaxations for MAP using Message Passing	claim
43	62	Sparse Stochastic Finite-State Controllers for POMDPs	claim
44	62	Small Sample Inference for Generalization Error in Classification Using the CUD Bound	claim
45	62	Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping	claim
46	62	Learning and Solving Many-Player Games through a Cluster-Based Representation	claim
47	62	Cumulative distribution networks and the derivative-sum-product algorithm	claim
48	61	Improving Gradient Estimation by Incorporating Sensor Data	claim
49	61	Learning Inclusion-Optimal Chordal Graphs	claim
50	61	Flexible Priors for Exemplar-based Clustering	claim
51	61	Bounding Search Space Size via (Hyper)tree Decompositions	claim
52	60	Bayesian Out-Trees	claim
53	60	Identifying Dynamic Sequential Plans	claim
54	60	Knowledge Combination in Graphical Multiagent Models	claim
55	59	Learning Arithmetic Circuits	claim
56	59	On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach	claim
57	58	Identifying Optimal Sequential Decisions	claim
58	58	Inference for Multiplicative Models	claim
59	57	Hierarchical POMDP Controller Optimization by Likelihood Maximization	claim
60	57	Identifying reasoning patterns in games	claim
61	56	Observation Subset Selection as Local Compilation of Performance Profiles	claim
62	55	Modelling local and global phenomena with sparse Gaussian processes	claim
63	54	Convex Point Estimation using Undirected Bayesian Transfer Hierarchies	claim
64	53	Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium	claim
65	51	The Computational Complexity of Sensitivity Analysis and Parameter Tuning	claim
66	50	Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices	claim
67	40	Causal discovery of linear acyclic models with arbitrary distributions	claim

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