ICML 2009 - PDF Preprint Papers

ICML 2009 Workshop on Statistical Network Analysis

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ICML
2009
IEEE

416views Machine Learning» more ICML 2009»

Independent factor topic models

14 years 7 months ago

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Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text document modeling. In this pap...

Duangmanee Putthividhya, Hagai Thomas Attias, Srik...

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Id	Views	Title	Status

1	416	Independent factor topic models	claim
2	309	Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations	claim
3	272	Multi-class image segmentation using conditional random fields and global classification	claim
4	246	BoltzRank: learning to maximize expected ranking gain	claim
5	237	Geometry-aware metric learning	claim
6	229	Learning non-redundant codebooks for classifying complex objects	claim
7	227	Online dictionary learning for sparse coding	claim
8	226	Large margin training for hidden Markov models with partially observed states	claim
9	222	Unsupervised hierarchical modeling of locomotion styles	claim
10	219	Non-linear matrix factorization with Gaussian processes	claim
11	216	Non-monotonic feature selection	claim
12	215	Incorporating domain knowledge into topic modeling via Dirichlet Forest priors	claim
13	197	Robust feature extraction via information theoretic learning	claim
14	194	Binary action search for learning continuous-action control policies	claim
15	190	A least squares formulation for a class of generalized eigenvalue problems in machine learning	claim
16	189	Graph construction and b-matching for semi-supervised learning	claim
17	189	A simpler unified analysis of budget perceptrons	claim
18	188	Convex variational Bayesian inference for large scale generalized linear models	claim
19	188	Nonparametric factor analysis with beta process priors	claim
20	187	Bayesian inference for Plackett-Luce ranking models	claim
21	186	Domain adaptation from multiple sources via auxiliary classifiers	claim
22	186	Regularization and feature selection in least-squares temporal difference learning	claim
23	185	Kernelized value function approximation for reinforcement learning	claim
24	185	Group lasso with overlap and graph lasso	claim
25	185	Decision tree and instance-based learning for label ranking	claim
26	185	Multiple indefinite kernel learning with mixed norm regularization	claim
27	183	Feature hashing for large scale multitask learning	claim
28	182	Archipelago: nonparametric Bayesian semi-supervised learning	claim
29	182	Partially supervised feature selection with regularized linear models	claim
30	177	Fast evolutionary maximum margin clustering	claim
31	175	Approximate inference for planning in stochastic relational worlds	claim
32	174	Multi-view clustering via canonical correlation analysis	claim
33	174	MedLDA: maximum margin supervised topic models for regression and classification	claim
34	172	Sparse Gaussian graphical models with unknown block structure	claim
35	172	Model-free reinforcement learning as mixture learning	claim
36	171	Learning spectral graph transformations for link prediction	claim
37	170	Interactively optimizing information retrieval systems as a dueling bandits problem	claim
38	170	Learning with structured sparsity	claim
39	169	Proximal regularization for online and batch learning	claim
40	169	Prototype vector machine for large scale semi-supervised learning	claim
41	168	A novel lexicalized HMM-based learning framework for web opinion mining	claim
42	168	Topic-link LDA: joint models of topic and author community	claim
43	166	Analytic moment-based Gaussian process filtering	claim
44	166	Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning	claim
45	166	Learning instance specific distances using metric propagation	claim
46	165	An accelerated gradient method for trace norm minimization	claim
47	164	Optimized expected information gain for nonlinear dynamical systems	claim
48	163	Accounting for burstiness in topic models	claim
49	163	Structure learning of Bayesian networks using constraints	claim
50	161	Matrix updates for perceptron training of continuous density hidden Markov models	claim
51	161	Sparse higher order conditional random fields for improved sequence labeling	claim
52	160	The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning	claim
53	160	A convex formulation for learning shared structures from multiple tasks	claim
54	160	The Bayesian group-Lasso for analyzing contingency tables	claim
55	159	Efficient learning algorithms for changing environments	claim
56	159	SimpleNPKL: simple non-parametric kernel learning	claim
57	158	Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property	claim
58	157	Function factorization using warped Gaussian processes	claim
59	157	Learning structurally consistent undirected probabilistic graphical models	claim
60	156	ABC-boost: adaptive base class boost for multi-class classification	claim
61	156	Deep transfer via second-order Markov logic	claim
62	156	Accelerated sampling for the Indian Buffet Process	claim
63	155	Near-Bayesian exploration in polynomial time	claim
64	154	Stochastic search using the natural gradient
65	154	Probabilistic dyadic data analysis with local and global consistency	claim
66	153	Bandit-based optimization on graphs with application to library performance tuning	claim
67	152	Partial order embedding with multiple kernels	claim
68	151	Robot trajectory optimization using approximate inference	claim
69	151	Transfer learning for collaborative filtering via a rating-matrix generative model	claim
70	150	Boosting with structural sparsity	claim
71	150	Factored conditional restricted Boltzmann Machines for modeling motion style	claim
72	150	Grammatical inference as a principal component analysis problem	claim
73	149	Learning kernels from indefinite similarities	claim
74	149	Trajectory prediction: learning to map situations to robot trajectories	claim
75	148	Predictive representations for policy gradient in POMDPs	claim
76	146	Dynamic mixed membership blockmodel for evolving networks	claim
77	146	Learning from measurements in exponential families	claim
78	146	Active learning for directed exploration of complex systems	claim
79	145	Online learning by ellipsoid method	claim
80	144	A Bayesian approach to protein model quality assessment	claim
81	144	Nonparametric estimation of the precision-recall curve	claim
82	144	Using fast weights to improve persistent contrastive divergence	claim
83	143	Proto-predictive representation of states with simple recurrent temporal-difference networks	claim
84	143	GAODE and HAODE: two proposals based on AODE to deal with continuous variables	claim
85	143	Uncertainty sampling and transductive experimental design for active dual supervision	claim
86	142	Curriculum learning	claim
87	142	Fitting a graph to vector data	claim
88	142	Robust bounds for classification via selective sampling	claim
89	141	Efficient Euclidean projections in linear time	claim
90	141	A stochastic memoizer for sequence data	claim
91	140	Learning dictionaries of stable autoregressive models for audio scene analysis	claim
92	140	Learning complex motions by sequencing simpler motion templates	claim
93	140	Deep learning from temporal coherence in video	claim
94	139	More generality in efficient multiple kernel learning	claim
95	138	Spectral clustering based on the graph p-Laplacian	claim
96	137	Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery	claim
97	137	Sequential Bayesian prediction in the presence of changepoints	claim
98	136	Learning structural SVMs with latent variables	claim
99	136	Detecting the direction of causal time series	claim
100	136	Herding dynamical weights to learn	claim
101	136	Online feature elicitation in interactive optimization	claim
102	136	Importance weighted active learning	claim
103	135	Rule learning with monotonicity constraints	claim
104	134	Unsupervised search-based structured prediction	claim
105	134	Discovering options from example trajectories	claim
106	134	Learning Markov logic network structure via hypergraph lifting	claim
107	133	Information theoretic measures for clusterings comparison: is a correction for chance necessary?	claim
108	133	Semi-supervised learning using label mean	claim
109	133	Exploiting sparse Markov and covariance structure in multiresolution models	claim
110	132	Split variational inference	claim
111	131	Bayesian clustering for email campaign detection	claim
112	131	Monte-Carlo simulation balancing	claim
113	130	Identifying suspicious URLs: an application of large-scale online learning	claim
114	129	Evaluation methods for topic models	claim
115	129	Solution stability in linear programming relaxations: graph partitioning and unsupervised learning	claim
116	125	Hilbert space embeddings of conditional distributions with applications to dynamical systems	claim
117	125	Supervised learning from multiple experts: whom to trust when everyone lies a bit	claim
118	124	Large-scale deep unsupervised learning using graphics processors	claim
119	124	A scalable framework for discovering coherent co-clusters in noisy data	claim
120	124	Good learners for evil teachers	claim
121	124	On sampling-based approximate spectral decomposition	claim
122	123	Structure preserving embedding	claim
123	123	Constraint relaxation in approximate linear programs	claim
124	122	Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties	claim
125	122	Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities	claim
126	121	Surrogate regret bounds for proper losses	claim
127	121	Learning prediction suffix trees with Winnow	claim
128	120	Fast gradient-descent methods for temporal-difference learning with linear function approximation	claim
129	120	Learning linear dynamical systems without sequence information	claim
130	119	Compositional noisy-logical learning	claim
131	118	Generalization analysis of listwise learning-to-rank algorithms	claim
132	117	K-means in space: a radiation sensitivity evaluation	claim
133	117	Stochastic methods for l1 regularized loss minimization	claim
134	116	Orbit-product representation and correction of Gaussian belief propagation	claim
135	115	PAC-Bayesian learning of linear classifiers	claim
136	114	Structure learning with independent non-identically distributed data	claim
137	113	Boosting products of base classifiers	claim
138	113	Learning nonlinear dynamic models	claim
139	111	The graphlet spectrum	claim
140	111	Learning to segment from a few well-selected training images	claim
141	110	Polyhedral outer approximations with application to natural language parsing	claim
142	110	Route kernels for trees	claim
143	109	Piecewise-stationary bandit problems with side observations	claim
144	109	Regression by dependence minimization and its application to causal inference in additive noise models	claim
145	105	Multi-assignment clustering for Boolean data	claim
146	105	Multi-instance learning by treating instances as non-I.I.D. samples	claim
147	104	Learning when to stop thinking and do something!	claim
148	102	Ranking interesting subgroups	claim
149	100	Optimistic initialization and greediness lead to polynomial time learning in factored MDPs	claim
150	98	On primal and dual sparsity of Markov networks	claim
151	98	Nearest neighbors in high-dimensional data: the emergence and influence of hubs	claim
152	96	Large-scale collaborative prediction using a nonparametric random effects model	claim
153	91	A majorization-minimization algorithm for (multiple) hyperparameter learning	claim
154	88	Discriminative k-metrics	claim

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