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ICML 2010 Workshop on Statistical Network Analysis

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ICML
2010
IEEE

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The Margin Perceptron with Unlearning

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We introduce into the classical Perceptron algorithm with margin a mechanism of unlearning which in the course of the regular update allows for a reduction of possible contributio...

Constantinos Panagiotakopoulos, Petroula Tsampouka

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Id	Views	Title	Status
1	315	The Margin Perceptron with Unlearning	claim
2	288	Conditional Topic Random Fields	claim
3	212	A Conditional Random Field for Multiple-Instance Learning	claim
4	155	Deep learning via Hessian-free optimization	claim
5	149	Bayesian Nonparametric Matrix Factorization for Recorded Music	claim
6	134	The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling	claim
7	124	A Theoretical Analysis of Feature Pooling in Visual Recognition	claim
8	123	Efficient Learning with Partially Observed Attributes	claim
9	122	Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process	claim
10	121	Variable Selection in Model-Based Clustering: To Do or To Facilitate	claim
11	119	Deep networks for robust visual recognition	claim
12	117	Hilbert Space Embeddings of Hidden Markov Models	claim
13	116	Learning Temporal Causal Graphs for Relational Time-Series Analysis	claim
14	114	Bayesian Multi-Task Reinforcement Learning	claim
15	113	Spherical Topic Models	claim
16	113	A New Analysis of Co-Training	claim
17	111	Learning Fast Approximations of Sparse Coding	claim
18	111	Large Graph Construction for Scalable Semi-Supervised Learning	claim
19	110	Accelerated dual decomposition for MAP inference	claim
20	109	A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices	claim
21	109	Fast boosting using adversarial bandits	claim
22	109	Structured Output Learning with Indirect Supervision	claim
23	108	From Transformation-Based Dimensionality Reduction to Feature Selection	claim
24	108	The Role of Machine Learning in Business Optimization	claim
25	107	On Sparse Nonparametric Conditional Covariance Selection	claim
26	106	Feature Selection as a One-Player Game	claim
27	104	Generalization Bounds for Learning Kernels	claim
28	104	Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate	claim
29	104	Mixed Membership Matrix Factorization	claim
30	103	Large Scale Max-Margin Multi-Label Classification with Priors	claim
31	103	Simple and Efficient Multiple Kernel Learning by Group Lasso	claim
32	102	Power Iteration Clustering	claim
33	102	Bottom-Up Learning of Markov Network Structure	claim
34	102	On learning with kernels for unordered pairs	claim
35	102	Convergence, Targeted Optimality, and Safety in Multiagent Learning	claim
36	101	Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets	claim
37	101	Toward Off-Policy Learning Control with Function Approximation	claim
38	100	Active Learning for Networked Data	claim
39	100	Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering	claim
40	99	Learning Tree Conditional Random Fields	claim
41	99	Inverse Optimal Control with Linearly-Solvable MDPs	claim
42	98	The Elastic Embedding Algorithm for Dimensionality Reduction	claim
43	98	An Analysis of the Convergence of Graph Laplacians	claim
44	98	Interactive Submodular Set Cover	claim
45	98	A fast natural Newton method	claim
46	98	Fast Neighborhood Subgraph Pairwise Distance Kernel	claim
47	97	Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes	claim
48	97	Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems	claim
49	96	Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains	claim
50	96	Rectified Linear Units Improve Restricted Boltzmann Machines	claim
51	96	Robust Subspace Segmentation by Low-Rank Representation	claim
52	95	Causal filter selection in microarray data	claim
53	94	Boosting for Regression Transfer	claim
54	94	Multi-Task Learning of Gaussian Graphical Models	claim
55	94	Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design	claim
56	94	OTL: A Framework of Online Transfer Learning	claim
57	93	High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models	claim
58	92	Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity	claim
59	91	Gaussian Process Change Point Models	claim
60	91	Submodular Dictionary Selection for Sparse Representation	claim
61	90	Modeling Interaction via the Principle of Maximum Causal Entropy	claim
62	90	Nonparametric Information Theoretic Clustering Algorithm	claim
63	90	Sequential Projection Learning for Hashing with Compact Codes	claim
64	90	3D Convolutional Neural Networks for Human Action Recognition	claim
65	89	A Stick-Breaking Construction of the Beta Process	claim
66	89	Active Learning for Multi-Task Adaptive Filtering	claim
67	88	Application of Machine Learning To Epileptic Seizure Detection	claim
68	88	Distance dependent Chinese restaurant processes	claim
69	88	One-sided Support Vector Regression for Multiclass Cost-sensitive Classification	claim
70	88	Online Streaming Feature Selection	claim
71	88	Non-Local Contrastive Objectives	claim
72	87	Continuous-Time Belief Propagation	claim
73	87	Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda	claim
74	87	Two-Stage Learning Kernel Algorithms	claim
75	86	Multi-Class Pegasos on a Budget	claim
76	86	Detecting Large-Scale System Problems by Mining Console Logs	claim
77	86	Cognitive Models of Test-Item Effects in Human Category Learning	claim
78	86	FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model	claim
79	85	Estimation of (near) low-rank matrices with noise and high-dimensional scaling	claim
80	85	On the Consistency of Ranking Algorithms	claim
81	85	Music Plus One and Machine Learning	claim
82	85	Model-based reinforcement learning with nearly tight exploration complexity bounds	claim
83	84	Exploiting Data-Independence for Fast Belief-Propagation	claim
84	84	Multi-agent Learning Experiments on Repeated Matrix Games	claim
85	83	Label Ranking Methods based on the Plackett-Luce Model	claim
86	83	Deep Supervised t-Distributed Embedding	claim
87	83	Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences	claim
88	83	A DC Programming Approach for Sparse Eigenvalue Problem	claim
89	83	Climbing the Tower of Babel: Unsupervised Multilingual Learning	claim
90	83	Gaussian Covariance and Scalable Variational Inference	claim
91	83	Boosting Classifiers with Tightened L0-Relaxation Penalties	claim
92	83	A Language-based Approach to Measuring Scholarly Impact	claim
93	83	Generalizing Apprenticeship Learning across Hypothesis Classes	claim
94	83	Learning Efficiently with Approximate Inference via Dual Losses	claim
95	83	Improved Local Coordinate Coding using Local Tangents	claim
96	82	Analysis of a Classification-based Policy Iteration Algorithm	claim
97	82	Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains	claim
98	82	Learning the Linear Dynamical System with ASOS	claim
99	82	The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data	claim
100	82	Online Learning for Group Lasso	claim
101	82	Gaussian Processes Multiple Instance Learning	claim
102	81	Mining Clustering Dimensions	claim
103	81	Boosted Backpropagation Learning for Training Deep Modular Networks	claim
104	81	Clustering processes	claim
105	81	Robust Formulations for Handling Uncertainty in Kernel Matrices	claim
106	81	Risk minimization, probability elicitation, and cost-sensitive SVMs	claim
107	80	Learning Markov Logic Networks Using Structural Motifs	claim
108	80	Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process	claim
109	79	Metric Learning to Rank	claim
110	79	A Simple Algorithm for Nuclear Norm Regularized Problems	claim
111	79	Graded Multilabel Classification: The Ordinal Case	claim
112	79	Learning Programs: A Hierarchical Bayesian Approach	claim
113	79	Random Spanning Trees and the Prediction of Weighted Graphs	claim
114	78	Efficient Selection of Multiple Bandit Arms: Theory and Practice	claim
115	78	Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes	claim
116	78	Proximal Methods for Sparse Hierarchical Dictionary Learning	claim
117	78	Convergence of Least Squares Temporal Difference Methods Under General Conditions	claim
118	78	Active Risk Estimation	claim
119	77	A scalable trust-region algorithm with application to mixed-norm regression	claim
120	77	Dynamical Products of Experts for Modeling Financial Time Series	claim
121	77	Learning optimally diverse rankings over large document collections	claim
122	77	SVM Classifier Estimation from Group Probabilities	claim
123	77	COFFIN: A Computational Framework for Linear SVMs	claim
124	77	On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning	claim
125	76	Particle Filtered MCMC-MLE with Connections to Contrastive Divergence	claim
126	75	Internal Rewards Mitigate Agent Boundedness	claim
127	74	Nonparametric Return Distribution Approximation for Reinforcement Learning	claim
128	74	Projection Penalties: Dimension Reduction without Loss	claim
129	73	Learning Deep Boltzmann Machines using Adaptive MCMC	claim
130	73	Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing	claim
131	72	Comparing Clusterings in Space	claim
132	72	Budgeted Nonparametric Learning from Data Streams	claim
133	71	Supervised Aggregation of Classifiers using Artificial Prediction Markets	claim
134	71	Multiple Non-Redundant Spectral Clustering Views	claim
135	70	Telling cause from effect based on high-dimensional observations	claim
136	70	Multiagent Inductive Learning: an Argumentation-based Approach	claim
137	70	Implicit Online Learning	claim
138	70	Asymptotic Analysis of Generative Semi-Supervised Learning	claim
139	69	Local Minima Embedding	claim
140	68	Finite-Sample Analysis of LSTD	claim
141	68	Implicit Regularization in Variational Bayesian Matrix Factorization	claim
142	66	Budgeted Distribution Learning of Belief Net Parameters	claim
143	66	Learning Hierarchical Riffle Independent Groupings from Rankings	claim
144	66	Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds	claim
145	63	Approximate Predictive Representations of Partially Observable Systems	claim
146	62	Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis	claim
147	59	Constructing States for Reinforcement Learning	claim
148	54	Online Prediction with Privacy	claim

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