ICML 2010 - PDF Preprint Papers

ICML 2010 Workshop on Statistical Network Analysis

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ICML
2010
IEEE

816views Machine Learning» more ICML 2010»

The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling

14 years 1 months ago

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The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric mixed membership model--each data point is modeled with a collection of components of different proportions. T...

Sinead Williamson, Chong Wang, Katherine A. Heller...

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Id	Views	Title	Status

1	816	The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling	claim
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7	329	Bayesian Nonparametric Matrix Factorization for Recorded Music	claim
8	314	A Theoretical Analysis of Feature Pooling in Visual Recognition	claim
9	304	Application of Machine Learning To Epileptic Seizure Detection	claim
10	304	FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model	claim
11	300	3D Convolutional Neural Networks for Human Action Recognition	claim
12	287	Rectified Linear Units Improve Restricted Boltzmann Machines	claim
13	284	The Role of Machine Learning in Business Optimization	claim
14	282	Bayesian Multi-Task Reinforcement Learning	claim
15	281	Boosting Classifiers with Tightened L0-Relaxation Penalties	claim
16	281	Deep learning via Hessian-free optimization	claim
17	280	Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity	claim
18	280	Deep networks for robust visual recognition	claim
19	279	Learning Temporal Causal Graphs for Relational Time-Series Analysis	claim
20	273	Variable Selection in Model-Based Clustering: To Do or To Facilitate	claim
21	272	Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets	claim
22	272	Large Graph Construction for Scalable Semi-Supervised Learning	claim
23	272	Spherical Topic Models	claim
24	269	Simple and Efficient Multiple Kernel Learning by Group Lasso	claim
25	265	From Transformation-Based Dimensionality Reduction to Feature Selection	claim
26	264	Bottom-Up Learning of Markov Network Structure	claim
27	259	Causal filter selection in microarray data	claim
28	259	On the Consistency of Ranking Algorithms	claim
29	258	Feature Selection as a One-Player Game	claim
30	257	Boosting for Regression Transfer	claim
31	256	Music Plus One and Machine Learning	claim
32	253	A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices	claim
33	251	Efficient Learning with Partially Observed Attributes	claim
34	251	Fast boosting using adversarial bandits	claim
35	250	Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process	claim
36	250	Active Learning for Networked Data	claim
37	248	A New Analysis of Co-Training	claim
38	248	Large Scale Max-Margin Multi-Label Classification with Priors	claim
39	248	On Sparse Nonparametric Conditional Covariance Selection	claim
40	247	Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process	claim
41	247	Inverse Optimal Control with Linearly-Solvable MDPs	claim
42	245	Robust Subspace Segmentation by Low-Rank Representation	claim
43	244	On learning with kernels for unordered pairs	claim
44	243	Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering	claim
45	241	Accelerated dual decomposition for MAP inference	claim
46	241	Learning Programs: A Hierarchical Bayesian Approach	claim
47	240	High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models	claim
48	240	Cognitive Models of Test-Item Effects in Human Category Learning	claim
49	240	Proximal Methods for Sparse Hierarchical Dictionary Learning	claim
50	239	Mixed Membership Matrix Factorization	claim
51	239	A DC Programming Approach for Sparse Eigenvalue Problem	claim
52	234	Power Iteration Clustering	claim
53	233	Metric Learning to Rank	claim
54	233	Hilbert Space Embeddings of Hidden Markov Models	claim
55	232	Submodular Dictionary Selection for Sparse Representation	claim
56	231	Toward Off-Policy Learning Control with Function Approximation	claim
57	230	Multi-Task Learning of Gaussian Graphical Models	claim
58	228	Clustering processes	claim
59	228	Structured Output Learning with Indirect Supervision	claim
60	228	Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences	claim
61	227	Learning Efficiently with Approximate Inference via Dual Losses	claim
62	227	Label Ranking Methods based on the Plackett-Luce Model	claim
63	227	Implicit Regularization in Variational Bayesian Matrix Factorization	claim
64	226	A fast natural Newton method	claim
65	224	A Stick-Breaking Construction of the Beta Process	claim
66	223	Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes	claim
67	222	Active Risk Estimation	claim
68	222	Sequential Projection Learning for Hashing with Compact Codes	claim
69	222	Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate	claim
70	222	Gaussian Process Change Point Models	claim
71	222	Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda	claim
72	221	An Analysis of the Convergence of Graph Laplacians	claim
73	219	Convergence, Targeted Optimality, and Safety in Multiagent Learning	claim
74	219	Convergence of Least Squares Temporal Difference Methods Under General Conditions	claim
75	218	Projection Penalties: Dimension Reduction without Loss	claim
76	217	Fast Neighborhood Subgraph Pairwise Distance Kernel	claim
77	217	Budgeted Nonparametric Learning from Data Streams	claim
78	217	The Elastic Embedding Algorithm for Dimensionality Reduction	claim
79	217	Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains	claim
80	216	Multi-agent Learning Experiments on Repeated Matrix Games	claim
81	215	Modeling Interaction via the Principle of Maximum Causal Entropy	claim
82	215	Deep Supervised t-Distributed Embedding	claim
83	215	Active Learning for Multi-Task Adaptive Filtering	claim
84	213	Online Learning for Group Lasso	claim
85	213	Particle Filtered MCMC-MLE with Connections to Contrastive Divergence	claim
86	213	Nonparametric Information Theoretic Clustering Algorithm	claim
87	212	Gaussian Processes Multiple Instance Learning	claim
88	211	One-sided Support Vector Regression for Multiclass Cost-sensitive Classification	claim
89	211	The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data	claim
90	210	Multi-Class Pegasos on a Budget	claim
91	210	A Language-based Approach to Measuring Scholarly Impact	claim
92	209	Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes	claim
93	209	Interactive Submodular Set Cover	claim
94	209	OTL: A Framework of Online Transfer Learning	claim
95	209	Boosted Backpropagation Learning for Training Deep Modular Networks	claim
96	207	Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains	claim
97	207	Learning optimally diverse rankings over large document collections	claim
98	206	Mining Clustering Dimensions	claim
99	204	Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design	claim
100	204	Detecting Large-Scale System Problems by Mining Console Logs	claim
101	204	Multiple Non-Redundant Spectral Clustering Views	claim
102	203	Budgeted Distribution Learning of Belief Net Parameters	claim
103	202	Asymptotic Analysis of Generative Semi-Supervised Learning	claim
104	202	Learning the Linear Dynamical System with ASOS	claim
105	202	Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems	claim
106	202	Continuous-Time Belief Propagation	claim
107	201	Robust Formulations for Handling Uncertainty in Kernel Matrices	claim
108	200	Generalizing Apprenticeship Learning across Hypothesis Classes	claim
109	199	Online Streaming Feature Selection	claim
110	199	Non-Local Contrastive Objectives	claim
111	199	Generalization Bounds for Learning Kernels	claim
112	199	Climbing the Tower of Babel: Unsupervised Multilingual Learning	claim
113	198	Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds	claim
114	198	COFFIN: A Computational Framework for Linear SVMs	claim
115	198	A Simple Algorithm for Nuclear Norm Regularized Problems	claim
116	198	Graded Multilabel Classification: The Ordinal Case	claim
117	198	Approximate Predictive Representations of Partially Observable Systems	claim
118	196	Learning Tree Conditional Random Fields	claim
119	195	Analysis of a Classification-based Policy Iteration Algorithm	claim
120	194	Supervised Aggregation of Classifiers using Artificial Prediction Markets	claim
121	194	Estimation of (near) low-rank matrices with noise and high-dimensional scaling	claim
122	194	Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing	claim
123	194	Online Prediction with Privacy	claim
124	193	Efficient Selection of Multiple Bandit Arms: Theory and Practice	claim
125	193	Distance dependent Chinese restaurant processes	claim
126	191	Learning Deep Boltzmann Machines using Adaptive MCMC	claim
127	191	Comparing Clusterings in Space	claim
128	189	Nonparametric Return Distribution Approximation for Reinforcement Learning	claim
129	188	Improved Local Coordinate Coding using Local Tangents	claim
130	188	Constructing States for Reinforcement Learning	claim
131	187	Two-Stage Learning Kernel Algorithms	claim
132	186	Random Spanning Trees and the Prediction of Weighted Graphs	claim
133	183	A scalable trust-region algorithm with application to mixed-norm regression	claim
134	183	Multiagent Inductive Learning: an Argumentation-based Approach	claim
135	182	Gaussian Covariance and Scalable Variational Inference	claim
136	181	Learning Markov Logic Networks Using Structural Motifs	claim
137	180	On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning	claim
138	180	Exploiting Data-Independence for Fast Belief-Propagation	claim
139	179	SVM Classifier Estimation from Group Probabilities	claim
140	178	Implicit Online Learning	claim
141	177	Local Minima Embedding	claim
142	177	Risk minimization, probability elicitation, and cost-sensitive SVMs	claim
143	175	Telling cause from effect based on high-dimensional observations	claim
144	171	Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis	claim
145	169	Model-based reinforcement learning with nearly tight exploration complexity bounds	claim
146	167	Internal Rewards Mitigate Agent Boundedness	claim
147	167	Finite-Sample Analysis of LSTD	claim
148	161	Learning Hierarchical Riffle Independent Groupings from Rankings	claim

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