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ORCID29th ICML 2012: Edinburgh, Scotland, UK
- Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. icml.cc / Omnipress 2012
- Dong Yu, Frank Seide, Gang Li:
Conversational Speech Transcription Using Context-Dependent Deep Neural Networks. - Ranjitha Kumar, Jerry O. Talton, Salman Ahmad, Scott R. Klemmer:
Data-driven Web Design. - Nathanael Chambers, Dan Jurafsky:
Learning the Central Events and Participants in Unlabeled Text. - Tomasz Malisiewicz, Abhinav Shrivastava, Abhinav Gupta, Alexei A. Efros:
Exemplar-SVMs for Visual Ob ject Detection, Label Transfer and Image Retrieval. - Jonathan Bischof, Edoardo M. Airoldi:
Capturing topical content with frequency and exclusivity. - Chao Liu, Yi-Min Wang:
TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings. - Dian Gong, Xuemei Zhao, Gérard G. Medioni:
Robust Multiple Manifold Structure Learning. - Byron Boots, Geoffrey J. Gordon:
Two Manifold Problems with Applications to Nonlinear System Identification. - Junfeng He, Sanjiv Kumar, Shih-Fu Chang:
On the Difficulty of Nearest Neighbor Search. - Mrinal Kalakrishnan, Ludovic Righetti, Peter Pastor, Stefan Schaal:
Learning Force Control Policies for Compliant Robotic Manipulation. - Pierre-André Savalle, Emile Richard, Nicolas Vayatis:
Estimation of Simultaneously Sparse and Low Rank Matrices. - Pannaga Shivaswamy, Thorsten Joachims:
Online Structured Prediction via Coactive Learning. - Paramveer S. Dhillon, Jordan Rodu, Dean P. Foster, Lyle H. Ungar:
Using CCA to improve CCA: A new spectral method for estimating vector models of words. - Roy Fox, Naftali Tishby:
Bounded Planning in Passive POMDPs. - Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan:
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. - Mehmet Gönen:
Bayesian Efficient Multiple Kernel Learning. - Murat Dundar, Ferit Akova, Yuan (Alan) Qi, Bartek Rajwa:
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes. - Steven C. H. Hoi, Jialei Wang, Peilin Zhao:
Exact Soft Confidence-Weighted Learning. - Fabio Massimo Zanzotto, Lorenzo Dell'Arciprete:
Distributed Tree Kernels. - Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou:
Multiple Kernel Learning from Noisy Labels by Stochastic Programming. - Kai Zhang, Liang Lan, Jun Liu, Andreas Rauber:
Improved Nystrom Low-rank Decomposition with Priors. - Laurent Charlin, Richard S. Zemel, Craig Boutilier:
Active Learning for Matching Problems. - Lauren Hannah, David B. Dunson:
Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design. - Ruijiang Li, Bin Li, Cheng Jin, Xiangyang Xue:
Groupwise Constrained Reconstruction for Subspace Clustering. - Yu-Xiang Wang, Huan Xu:
Stability of matrix factorization for collaborative filtering. - Koby Crammer, Gal Chechik:
Adaptive Regularization for Similarity Measures. - Thomas Degris, Martha White, Richard S. Sutton:
Linear Off-Policy Actor-Critic. - M. Pawan Kumar, Benjamin Packer, Daphne Koller:
Modeling Latent Variable Uncertainty for Loss-based Learning. - Nathan Parrish, Maya R. Gupta:
Dimensionality Reduction by Local Discriminative Gaussians. - Volodymyr Mnih, Geoffrey E. Hinton:
Learning to Label Aerial Images from Noisy Data. - Novi Quadrianto, Chao Chen, Christoph H. Lampert:
The Most Persistent Soft-Clique in a Set of Sampled Graphs. - Alexander M. Bronstein, Pablo Sprechmann, Guillermo Sapiro:
Learning Efficient Structured Sparse Models. - Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone:
PAC Subset Selection in Stochastic Multi-armed Bandits. - Samuel Gershman, Matthew D. Hoffman, David M. Blei:
Nonparametric variational inference. - Mark D. Reid, Robert C. Williamson, Peng Sun:
The Convexity and Design of Composite Multiclass Losses. - Peter Haider, Tobias Scheffer:
Finding Botnets Using Minimal Graph Clusterings. - Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson:
Learning the Experts for Online Sequence Prediction. - Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh:
Efficient Active Algorithms for Hierarchical Clustering. - Mélanie Rey, Volker Roth:
Copula Mixture Model for Dependency-seeking Clustering. - Krishnakumar Balasubramanian, Guy Lebanon:
The Landmark Selection Method for Multiple Output Prediction. - Nils M. Kriege
, Petra Mutzel:
Subgraph Matching Kernels for Attributed Graphs. - Florian Yger, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
Adaptive Canonical Correlation Analysis Based On Matrix Manifolds. - Javad Azimi, Alan Fern, Xiaoli Zhang Fern, Glencora Borradaile, Brent Heeringa:
Batch Active Learning via Coordinated Matching. - Javad Azimi, Ali Jalali, Xiaoli Zhang Fern:
Hybrid Batch Bayesian Optimization. - Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani:
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. - Hanhuai Shan, Jens Kattge, Peter B. Reich, Arindam Banerjee, Franziska Schrodt, Markus Reichstein:
Gap Filling in the Plant Kingdom - Trait Prediction Using Hierarchical Probabilistic Matrix Factorization. - Alain Rakotomamonjy:
Sparse Support Vector Infinite Push. - Matthieu Geist, Bruno Scherrer, Alessandro Lazaric, Mohammad Ghavamzadeh:
A Dantzig Selector Approach to Temporal Difference Learning. - Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, David Haglin:
Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems. - Shaobo Han, Xuejun Liao, Lawrence Carin:
Cross-Domain Multitask Learning with Latent Probit Models. - Xinghua Lou, Fred A. Hamprecht:
Structured Learning from Partial Annotations. - Yi Zhang, Jeff G. Schneider:
Maximum Margin Output Coding. - Haijie Gu, John D. Lafferty:
Sequential Nonparametric Regression. - Konstantina Palla, David A. Knowles, Zoubin Ghahramani:
An Infinite Latent Attribute Model for Network Data. - Michael Bowling, Martin Zinkevich:
On Local Regret. - Benjamin Yackley, Terran Lane:
Smoothness and Structure Learning by Proxy . - Andriy Mnih, Yee Whye Teh:
A fast and simple algorithm for training neural probabilistic language models. - Giorgos Borboudakis, Ioannis Tsamardinos:
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs. - Majid Janzamin, Animashree Anandkumar:
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains. - Jason Weston, Chong Wang, Ron J. Weiss, Adam Berenzweig:
Latent Collaborative Retrieval. - Shie Mannor, Ofir Mebel, Huan Xu:
Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty. - Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
On causal and anticausal learning. - Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang:
Compact Hyperplane Hashing with Bilinear Functions. - Sergey Levine, Vladlen Koltun:
Continuous Inverse Optimal Control with Locally Optimal Examples. - Wenliang Zhong, James Tin-Yau Kwok:
Convex Multitask Learning with Flexible Task Clusters. - Drausin Wulsin, Shane Jensen, Brian Litt:
A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling. - Yingjian Wang, Lawrence Carin:
Levy Measure Decompositions for the Beta and Gamma Processes. - Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeffrey Dean, Andrew Y. Ng:
Building high-level features using large scale unsupervised learning. - Mauricio Araya-López, Olivier Buffet, Vincent Thomas:
Near-Optimal BRL using Optimistic Local Transitions. - Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Robust Classification Model. - Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. - Alfredo A. Kalaitzis, Neil D. Lawrence:
Residual Components Analysis. - Jiabing Wang, Jiaye Chen:
Clustering to Maximize the Ratio of Split to Diameter. - Aaron Defazio, Tibério S. Caetano:
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training. - Bin Li, Steven C. H. Hoi:
On-Line Portfolio Selection with Moving Average Reversion. - Sebastian Nowozin:
Improved Information Gain Estimates for Decision Tree Induction. - Manuel Gomez-Rodriguez, Bernhard Schölkopf:
Influence Maximization in Continuous Time Diffusion Networks. - Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
On the Size of the Online Kernel Sparsification Dictionary. - Aurélie C. Lozano, Grzegorz Swirszcz:
Multi-level Lasso for Sparse Multi-task Regression. - Daisuke Kimura, Hisashi Kashima:
Fast Computation of Subpath Kernel for Trees. - Tong Lin, Hanlin Xue, Ling Wang, Hongbin Zha:
Total Variation and Euler's Elastica for Supervised Learning. - Ali Jalali, Sujay Sanghavi:
Learning the Dependence Graph of Time Series with Latent Factors. - Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner:
A Generalized Loop Correction Method for Approximate Inference in Graphical Models. - Mladen Kolar, Eric P. Xing:
Consistent Covariance Selection From Data With Missing Values. - Qinfeng Shi, Chunhua Shen, Rhys Hill, Anton van den Hengel:
Is margin preserved after random projection?. - Mingjun Zhong, Mark A. Girolami:
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices. - Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado:
Predicting accurate probabilities with a ranking loss. - Lixin Duan, Dong Xu, Ivor W. Tsang:
Learning with Augmented Features for Heterogeneous Domain Adaptation. - Dongwoo Kim, Suin Kim, Alice Oh:
Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data. - Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani:
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning . - Sanjay Purushotham, Yan Liu:
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems. - Patrick Pletscher, Sharon Wulff:
LPQP for MAP: Putting LP Solvers to Better Use. - Zhirong Yang, Erkki Oja:
Clustering by Low-Rank Doubly Stochastic Matrix Decomposition. - Changyou Chen, Nan Ding, Wray L. Buntine:
Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling. - Jouni Hartikainen, Mari Seppänen, Simo Särkkä:
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction. - Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty:
Sparse Additive Functional and Kernel CCA. - Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro:
The Kernelized Stochastic Batch Perceptron. - Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl:
Fast classification using sparse decision DAGs. - Franz J. Király, Ryota Tomioka:
A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion. - Issei Sato, Hiroshi Nakagawa:
Rethinking Collapsed Variational Bayes Inference for LDA. - Robert Peharz, Franz Pernkopf:
Exact Maximum Margin Structure Learning of Bayesian Networks. - Peng Sun, Mark D. Reid, Jie Zhou:
AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem. - Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu:
Hypothesis testing using pairwise distances and associated kernels. - Yi Wang, Kok Sung Won, David Hsu, Wee Sun Lee:
Monte Carlo Bayesian Reinforcement Learning. - Athina Spiliopoulou, Amos J. Storkey:
A Topic Model for Melodic Sequences. - Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli:
Output Space Search for Structured Prediction. - Yoram Bachrach, Thore Graepel, Tom Minka, John Guiver:
How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing. - Thomas Desautels, Andreas Krause, Joel W. Burdick:
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization. - Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han:
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound. - Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, Richard P. Mann:
Bayesian Optimal Active Search and Surveying. - Deepti Pachauri, Maxwell D. Collins, Vikas Singh:
Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis. - Mingyuan Zhou, Lingbo Li, David B. Dunson, Lawrence Carin:
Lognormal and Gamma Mixed Negative Binomial Regression. - Christopher Painter-Wakefield, Ronald Parr:
Greedy Algorithms for Sparse Reinforcement Learning. - Mladen Kolar, James Sharpnack:
Variance Function Estimation in High-dimensions. - Min Xu, John D. Lafferty:
Conditional Sparse Coding and Grouped Multivariate Regression. - Takaki Makino, Johane Takeuchi:
Apprenticeship Learning for Model Parameters of Partially Observable Environments. - Ke Zhai, Yuening Hu, Jordan L. Boyd-Graber, Sinead Williamson:
Modeling Images using Transformed Indian Buffet Processes. - Yun Jiang, Marcus Lim, Ashutosh Saxena:
Learning Object Arrangements in 3D Scenes using Human Context. - Yuhong Guo, Min Xiao:
Cross Language Text Classification via Subspace Co-regularized Multi-view Learning . - Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Volodya Vovk:
Plug-in martingales for testing exchangeability on-line. - Deguang Kong, Chris H. Q. Ding, Heng Huang, Feiping Nie:
An Iterative Locally Linear Embedding Algorithm. - Alexis Boukouvalas, Remi Louis Barillec, Dan Cornford:
Gaussian Process Quantile Regression using Expectation Propagation. - Myunghwan Kim, Jure Leskovec:
Latent Multi-group Membership Graph Model. - Nando de Freitas, Alexander J. Smola, Masrour Zoghi:
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. - James Martens, Ilya Sutskever, Kevin Swersky:
Estimating the Hessian by Back-propagating Curvature. - Andrea Pohoreckyj Danyluk, Nicholas Arnosti:
Feature Selection via Probabilistic Outputs. - Yisong Yue, Sue Ann Hong, Carlos Guestrin:
Hierarchical Exploration for Accelerating Contextual Bandits. - Max Vladymyrov, Miguel Á. Carreira-Perpiñán:
Fast Training of Nonlinear Embedding Algorithms. - Hoyt A. Koepke, Mikhail Bilenko:
Fast Prediction of New Feature Utility. - Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan, Hartmut Neven:
Robust Classification with Adiabatic Quantum Optimization. - Matus Telgarsky, Sanjoy Dasgupta:
Agglomerative Bregman Clustering. - Amos J. Storkey, Jono Millin, Krzysztof J. Geras:
Isoelastic Agents and Wealth Updates in Machine Learning Markets. - Philipp Hennig, Martin Kiefel:
Quasi-Newton Methods: A New Direction. - Marc Lanctot, Richard G. Gibson, Neil Burch, Michael Bowling:
No-Regret Learning in Extensive-Form Games with Imperfect Recall. - Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama:
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. - Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. - Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. - Roland Memisevic:
On multi-view feature learning. - Steven C. H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin, Pengcheng Wu:
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning. - Sören Laue:
A Hybrid Algorithm for Convex Semidefinite Optimization. - Julia E. Vogt, Volker Roth:
A Complete Analysis of the l_1, p Group-Lasso. - Jean Honorio:
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models. - Orly Avner, Shie Mannor, Ohad Shamir:
Decoupling Exploration and Exploitation in Multi-Armed Bandits. - Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to Identify Regular Expressions that Describe Email Campaigns. - Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Mixtures of Factor Analysers. - Brian Kulis, Michael I. Jordan:
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. - Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani:
Gaussian Process Regression Networks. - Yaoliang Yu, Csaba Szepesvári:
Analysis of Kernel Mean Matching under Covariate Shift. - Avraham Ruderman, Mark D. Reid, Dario García-García, James Petterson:
Tighter Variational Representations of f-Divergences via Restriction to Probability Measures. - George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. - Ivo Shterev, David B. Dunson:
Bayesian Watermark Attacks. - Jun Zhu:
Max-Margin Nonparametric Latent Feature Models for Link Prediction. - Edwin V. Bonilla, Antonio Robles-Kelly:
Discriminative Probabilistic Prototype Learning. - Yan Liu, Mohammad Taha Bahadori, Hongfei Li:
Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling. - Ryohei Fujimaki, Kohei Hayashi:
Factorized Asymptotic Bayesian Hidden Markov Models. - Emilie Morvant, Sokol Koço, Liva Ralaivola:
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. - Marthinus Christoffel du Plessis, Masashi Sugiyama:
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching. - Lin Xiao, Tong Zhang:
A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem. - Amin Karbasi, Stratis Ioannidis, Laurent Massoulié:
Comparison-Based Learning with Rank Nets. - Luke K. McDowell, David W. Aha:
Semi-Supervised Collective Classification via Hybrid Label Regularization. - Morteza Alamgir, Ulrike von Luxburg:
Shortest path distance in random k-nearest neighbor graphs. - Qiaoliang Xiang, Qi Mao, Kian Ming Adam Chai, Hai Leong Chieu, Ivor W. Tsang, Zhendong Zhao:
A Split-Merge Framework for Comparing Clusterings. - David Silver, Kamil Ciosek:
Compositional Planning Using Optimal Option Models. - Yuan Shi, Fei Sha:
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation. - Alexandre Passos, Piyush Rai, Jacques Wainer, Hal Daumé III:
Flexible Modeling of Latent Task Structures in Multitask Learning. - Luis Francisco Sánchez Merchante, Yves Grandvalet, Gérard Govaert:
An Efficient Approach to Sparse Linear Discriminant Analysis. - Zhixiang Eddie Xu, Kilian Q. Weinberger, Olivier Chapelle:
The Greedy Miser: Learning under Test-time Budgets. - Felix Bießmann, Jens-Michalis Papaioannou, Mikio L. Braun, Andreas Harth:
Canonical Trends: Detecting Trend Setters in Web Data. - Armand Joulin, Francis R. Bach:
A convex relaxation for weakly supervised classifiers. - Jesse Davis, Vítor Santos Costa, Elizabeth Berg, David Page, Peggy L. Peissig, Michael Caldwell:
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events. - Abhishek Kumar, Alexandru Niculescu-Mizil, Koray Kavukcuoglu, Hal Daumé III:
A Binary Classification Framework for Two-Stage Multiple Kernel Learning. - Kihyuk Sohn, Honglak Lee:
Learning Invariant Representations with Local Transformations. - Krzysztof Dembczynski, Wojciech Kotlowski, Eyke Hüllermeier:
Consistent Multilabel Ranking through Univariate Losses. - Francis R. Bach, Simon Lacoste-Julien, Guillaume Obozinski:
On the Equivalence between Herding and Conditional Gradient Algorithms. - John W. Paisley, David M. Blei, Michael I. Jordan:
Variational Bayesian Inference with Stochastic Search. - Gaël Varoquaux, Alexandre Gramfort, Bertrand Thirion:
Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering. - Bo Chen, Rui M. Castro, Andreas Krause:
Joint Optimization and Variable Selection of High-dimensional Gaussian Processes. - Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding. - Denis Deratani Mauá, Cassio Polpo de Campos:
Anytime Marginal MAP Inference. - Qiang Liu, Alexander Ihler:
Distributed Parameter Estimation via Pseudo-likelihood . - Dae Il Kim, Michael C. Hughes, Erik B. Sudderth:
The Nonparametric Metadata Dependent Relational Model. - Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Lambertian Networks. - Iftekhar Naim, Daniel Gildea:
Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients. - Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox:
A Joint Model of Language and Perception for Grounded Attribute Learning. - Bruno Castro da Silva, George Dimitri Konidaris, Andrew G. Barto:
Learning Parameterized Skills. - Teodor Mihai Moldovan, Pieter Abbeel:
Safe Exploration in Markov Decision Processes . - Doina Precup, Philip Bachman:
Improved Estimation in Time Varying Models. - Battista Biggio, Blaine Nelson, Pavel Laskov:
Poisoning Attacks against Support Vector Machines. - James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans:
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. - Lawrence C. McAfee, Kunle Olukotun:
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks. - Aurélien Bellet, Amaury Habrard, Marc Sebban:
Similarity Learning for Provably Accurate Sparse Linear Classification. - Gautham J. Mysore, Maneesh Sahani:
Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation. - Minhua Chen, William R. Carson, Miguel R. D. Rodrigues, Lawrence Carin, A. Robert Calderbank:
Communications Inspired Linear Discriminant Analysis. - David M. Mimno, Matthew D. Hoffman, David M. Blei:
Sparse stochastic inference for latent Dirichlet allocation. - Hua Ouyang, Alexander G. Gray:
Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure. - Pratik Jawanpuria, J. Saketha Nath:
A Convex Feature Learning Formulation for Latent Task Structure Discovery. - Rajhans Samdani, Dan Roth:
Efficient Decomposed Learning for Structured Prediction. - Freek Stulp, Olivier Sigaud:
Path Integral Policy Improvement with Covariance Matrix Adaptation. - Nan Ye, Kian Ming Adam Chai, Wee Sun Lee, Hai Leong Chieu:
Optimizing F-measure: A Tale of Two Approaches. - Adams Wei Yu, Hao Su, Li Fei-Fei:
Efficient Euclidean Projections onto the Intersection of Norm Balls. - Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. - Ali Jalali, Nathan Srebro:
Clustering using Max-norm Constrained Optimization. - Manuel Gomez-Rodriguez, Bernhard Schölkopf:
Submodular Inference of Diffusion Networks from Multiple Trees. - Marek Petrik:
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds. - Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton:
Modelling transition dynamics in MDPs with RKHS embeddings. - Mark McCartin-Lim, Andrew McGregor, Rui Wang:
Approximate Principal Direction Trees. - Kendrick Boyd, Jesse Davis, David Page, Vítor Santos Costa:
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. - Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha:
Marginalized Denoising Autoencoders for Domain Adaptation. - Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. - Junming Yin, Xi Chen, Eric P. Xing:
Group Sparse Additive Models. - Dotan Di Castro, Aviv Tamar, Shie Mannor:
Policy Gradients with Variance Related Risk Criteria. - Alexander G. Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun:
Efficient Structured Prediction with Latent Variables for General Graphical Models. - Tamir Hazan, Tommi S. Jaakkola:
On the Partition Function and Random Maximum A-Posteriori Perturbations. - Zenglin Xu, Feng Yan, Yuan (Alan) Qi:
Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis. - Esther Salazar, Lawrence Carin:
Inferring Latent Structure From Mixed Real and Categorical Relational Data. - Chris Bracegirdle, David Barber:
Bayesian Conditional Cointegration. - Huahua Wang, Arindam Banerjee:
Online Alternating Direction Method. - Mohammad Gheshlaghi Azar, Rémi Munos, Bert Kappen:
On the Sample Complexity of Reinforcement Learning with a Generative Model . - Kamalika Chaudhuri, Daniel J. Hsu:
Convergence Rates for Differentially Private Statistical Estimation. - Abhishek Kumar, Hal Daumé III:
Learning Task Grouping and Overlap in Multi-task Learning. - Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
High Dimensional Semiparametric Gaussian Copula Graphical Models. - Yiteng Zhai, Mingkui Tan, Ivor W. Tsang, Yew-Soon Ong:
Discovering Support and Affiliated Features from Very High Dimensions. - Ofer Dekel, Ambuj Tewari, Raman Arora:
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret. - Bernardo Ávila Pires, Csaba Szepesvári:
Statistical linear estimation with penalized estimators: an application to reinforcement learning. - Young-Jun Ko, Matthias W. Seeger:
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models. - Sungjin Ahn, Anoop Korattikara Balan, Max Welling:
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring. - Gábor Bartók, Navid Zolghadr, Csaba Szepesvári:
An adaptive algorithm for finite stochastic partial monitoring. - Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan:
The Big Data Bootstrap. - Or Sheffet, Nina Mishra, Samuel Ieong:
Predicting Consumer Behavior in Commerce Search. - Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil:
Conditional mean embeddings as regressors. - Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio:
A Generative Process for Contractive Auto-Encoders. - Borja Balle, Ariadna Quattoni, Xavier Carreras:
Local Loss Optimization in Operator Models: A New Insight into Spectral Learning. - Jiashi Feng, Huan Xu, Shuicheng Yan:
Robust PCA in High-dimension: A Deterministic Approach. - Julien Mairal, Bin Yu:
Complexity Analysis of the Lasso Regularization Path. - Elad Hazan, Satyen Kale:
Projection-free Online Learning. - Kiri Wagstaff:
Machine Learning that Matters. - Clément Farabet, Camille Couprie, Laurent Najman, Yann LeCun:
Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers. - Elad Hazan, Tomer Koren:
Linear Regression with Limited Observation. - Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:
An Online Boosting Algorithm with Theoretical Justifications. - Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent:
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription. - Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, Matthieu Geist:
Approximate Modified Policy Iteration. - Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan:
Nonparametric Link Prediction in Dynamic Networks. - Stéphane Ross, Drew Bagnell:
Agnostic System Identification for Model-Based Reinforcement Learning.
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last updated on 2024-05-31 18:55 CEST by the dblp team
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