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2008

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Hybrid Variational/Gibbs Collapsed Inference in Topic Models

14 years 25 days 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	333	Hybrid Variational/Gibbs Collapsed Inference in Topic Models	claim
2	313	Projected Subgradient Methods for Learning Sparse Gaussians	claim
3	299	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features	claim
4	293	Estimation and clustering with infinite rankings	claim
5	290	Continuous Time Dynamic Topic Models	claim
6	261	Cumulative distribution networks and the derivative-sum-product algorithm	claim
7	259	Feature Selection via Block-Regularized Regression	claim
8	257	Model-Based Bayesian Reinforcement Learning in Large Structured Domains	claim
9	252	Small Sample Inference for Generalization Error in Classification Using the CUD Bound	claim
10	251	Almost Optimal Intervention Sets for Causal Discovery	claim
11	251	On Identifying Total Effects in the Presence of Latent Variables and Selection bias	claim
12	242	Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping	claim
13	242	Sensitivity analysis in decision circuits	claim
14	241	Toward Experiential Utility Elicitation for Interface Customization	claim
15	240	AND/OR Importance Sampling	claim
16	239	Constrained Approximate Maximum Entropy Learning of Markov Random Fields	claim
17	239	Bounds on the Bethe Free Energy for Gaussian Networks	claim
18	236	CORL: A Continuous-state Offset-dynamics Reinforcement Learner	claim
19	234	Improving Gradient Estimation by Incorporating Sensor Data	claim
20	234	Improving the Accuracy and Efficiency of MAP Inference for Markov Logic	claim
21	232	Modelling local and global phenomena with sparse Gaussian processes	claim
22	230	Refractor Importance Sampling	claim
23	230	Partitioned Linear Programming Approximations for MDPs	claim
24	230	Efficient Inference in Persistent Dynamic Bayesian Networks	claim
25	229	Discovering Cyclic Causal Models by Independent Components Analysis	claim
26	226	Tightening LP Relaxations for MAP using Message Passing	claim
27	224	Explanation Trees for Causal Bayesian Networks	claim
28	224	CT-NOR: Representing and Reasoning About Events in Continuous Time	claim
29	224	Sampling First Order Logical Particles	claim
30	223	The Computational Complexity of Sensitivity Analysis and Parameter Tuning	claim
31	223	Church: a language for generative models	claim
32	221	Learning the Bayesian Network Structure: Dirichlet Prior vs Data	claim
33	221	Complexity of Inference in Graphical Models	claim
34	219	On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach	claim
35	217	Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression	claim
36	216	Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies	claim
37	216	Latent Topic Models for Hypertext	claim
38	216	Learning Hidden Markov Models for Regression using Path Aggregation	claim
39	214	Adaptive inference on general graphical models	claim
40	214	A Game-Theoretic Analysis of Updating Sets of Probabilities	claim
41	214	Learning Inclusion-Optimal Chordal Graphs	claim
42	213	Multi-View Learning over Structured and Non-Identical Outputs	claim
43	212	Bayesian Out-Trees	claim
44	206	Convex Point Estimation using Undirected Bayesian Transfer Hierarchies	claim
45	204	Bayesian network learning by compiling to weighted MAX-SAT	claim
46	203	Strategy Selection in Influence Diagrams using Imprecise Probabilities	claim
47	203	Identifying Dynamic Sequential Plans	claim
48	202	New Techniques for Algorithm Portfolio Design	claim
49	201	Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices	claim
50	200	Bounding Search Space Size via (Hyper)tree Decompositions	claim
51	198	Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression	claim
52	194	Hierarchical POMDP Controller Optimization by Likelihood Maximization	claim
53	192	Sparse Stochastic Finite-State Controllers for POMDPs	claim
54	192	Identifying reasoning patterns in games	claim
55	187	Learning and Solving Many-Player Games through a Cluster-Based Representation	claim
56	187	Knowledge Combination in Graphical Multiagent Models	claim
57	187	Flexible Priors for Exemplar-based Clustering	claim
58	187	Gibbs Sampling in Factorized Continuous-Time Markov Processes	claim
59	186	Sensitivity analysis for finite Markov chains in discrete time	claim
60	185	Learning Arithmetic Circuits	claim
61	184	Observation Subset Selection as Local Compilation of Performance Profiles	claim
62	183	Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction	claim
63	183	A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games	claim
64	182	Inference for Multiplicative Models	claim
65	181	Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium	claim
66	180	Causal discovery of linear acyclic models with arbitrary distributions	claim
67	177	Identifying Optimal Sequential Decisions	claim

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