default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID31st ICML 2014: Beijing, China
- Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014. JMLR Workshop and Conference Proceedings 32, JMLR.org 2014
Cycle 1 Papers
- Rajhans Samdani, Kai-Wei Chang, Dan Roth:
A Discriminative Latent Variable Model for Online Clustering. 1-9 - Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein Effect. 10-18 - Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo:
Demystifying Information-Theoretic Clustering. 19-27 - Zongzhang Zhang, David Hsu, Wee Sun Lee:
Covering Number for Efficient Heuristic-based POMDP Planning. 28-36 - Wenzhuo Yang, Melvyn Sim, Huan Xu:
The Coherent Loss Function for Classification. 37-45 - Wenliang Zhong, James Tin-Yau Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. 46-54 - Yuxin Chen, Hiroaki Shioi, Cesar Fuentes Montesinos, Lian Pin Koh, Serge A. Wich, Andreas Krause:
Active Detection via Adaptive Submodularity. 55-63 - Shai Shalev-Shwartz, Tong Zhang:
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. 64-72 - Qihang Lin, Lin Xiao:
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization. 73-81 - Pedro H. O. Pinheiro, Ronan Collobert:
Recurrent Convolutional Neural Networks for Scene Labeling. 82-90 - Ping Ma, Michael W. Mahoney, Bin Yu:
A Statistical Perspective on Algorithmic Leveraging. 91-99 - Aditya Gopalan, Shie Mannor, Yishay Mansour:
Thompson Sampling for Complex Online Problems. 100-108 - Souhaib Ben Taieb, Rob J. Hyndman:
Boosting multi-step autoregressive forecasts. 109-117 - Arun Rajkumar, Shivani Agarwal:
A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data. 118-126 - Timothy A. Mann, Shie Mannor:
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations. 127-135 - Odalric-Ambrym Maillard, Shie Mannor:
Latent Bandits. 136-144 - Trung V. Nguyen, Edwin V. Bonilla:
Fast Allocation of Gaussian Process Experts. 145-153 - Siddharth Gopal, Yiming Yang:
Von Mises-Fisher Clustering Models. 154-162 - Frédéric Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel:
Convergence rates for persistence diagram estimation in Topological Data Analysis. 163-171 - Fabian Gieseke, Justin Heinermann, Cosmin E. Oancea, Christian Igel:
Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. 172-180 - Anoop Korattikara Balan, Yutian Chen, Max Welling:
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. 181-189 - Jian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei, Ming Zhang:
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis. 190-198 - Maxim Rabinovich, David M. Blei:
The Inverse Regression Topic Model. 199-207 - Stanley H. Chan, Edoardo M. Airoldi:
A Consistent Histogram Estimator for Exchangeable Graph Models. 208-216 - Benjamin Letham, Wei Sun, Anshul Sheopuri:
Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data. 217-225 - Haipeng Luo, Robert E. Schapire:
Towards Minimax Online Learning with Unknown Time Horizon. 226-234 - Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry:
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. 235-243 - Aaditya Ramdas, Javier Peña:
Margins, Kernels and Non-linear Smoothed Perceptrons. 244-252 - Shike Mei, Jun Zhu, Jerry Zhu:
Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models. 253-261 - Mehryar Mohri, Andres Muñoz Medina:
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve. 262-270 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Low-density Parity Constraints for Hashing-Based Discrete Integration. 271-279 - Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori:
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. 280-287 - Tien-Vu Nguyen, Dinh Quoc Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Bui:
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts. 288-296 - Rémi Lajugie, Francis R. Bach, Sylvain Arlot:
Large-Margin Metric Learning for Constrained Partitioning Problems. 297-305 - Justin Solomon, Raif M. Rustamov, Leonidas J. Guibas, Adrian Butscher:
Wasserstein Propagation for Semi-Supervised Learning. 306-314 - Aonan Zhang, Jun Zhu, Bo Zhang:
Max-Margin Infinite Hidden Markov Models. 315-323 - Yong Liu, Shali Jiang, Shizhong Liao:
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function. 324-332 - Shashank Singh, Barnabás Póczos:
Generalized Exponential Concentration Inequality for Renyi Divergence Estimation. 333-341 - Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:
Boosting with Online Binary Learners for the Multiclass Bandit Problem. 342-350 - Tasuku Soma, Naonori Kakimura, Kazuhiro Inaba, Ken-ichi Kawarabayashi:
Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm. 351-359 - Hossein Azari Soufiani, David C. Parkes, Lirong Xia:
Computing Parametric Ranking Models via Rank-Breaking. 360-368 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade:
Tracking Adversarial Targets. 369-377 - Tianlin Shi, Jun Zhu:
Online Bayesian Passive-Aggressive Learning. 378-386 - David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller:
Deterministic Policy Gradient Algorithms. 387-395 - Wenzhao Lian, Vinayak A. Rao, Brian Eriksson, Lawrence Carin:
Modeling Correlated Arrival Events with Latent Semi-Markov Processes. 396-404 - Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. 405-413 - Ferdinando Cicalese, Eduardo Sany Laber, Aline Medeiros Saettler:
Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost. 414-422 - Chun-Liang Li, Hsuan-Tien Lin:
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. 423-431 - Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola:
On Measure Concentration of Random Maximum A-Posteriori Perturbations. 432-440 - Philip Thomas:
Bias in Natural Actor-Critic Algorithms. 441-448 - François Denis, Mattias Gybels, Amaury Habrard:
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. 449-457 - Zhixing Li, Siqiang Wen, Juanzi Li, Peng Zhang, Jie Tang:
On Modelling Non-linear Topical Dependencies. 458-466 - Benigno Uria, Iain Murray, Hugo Larochelle:
A Deep and Tractable Density Estimator. 467-475 - Prateek Jain, Abhradeep Guha Thakurta:
(Near) Dimension Independent Risk Bounds for Differentially Private Learning. 476-484 - Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. 485-493 - Nikos Karampatziakis, Paul Mineiro:
Discriminative Features via Generalized Eigenvectors. 494-502 - Ji Liu, Jieping Ye, Ryohei Fujimaki:
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint. 503-511 - Travis Dick, András György, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Changing Cost Sequences. 512-520 - Richard Combes, Alexandre Proutière:
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms. 521-529 - Arun Shankar Iyer, J. Saketha Nath, Sunita Sarawagi:
Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection. 530-538 - Azadeh Khaleghi, Daniil Ryabko:
Asymptotically consistent estimation of the number of change points in highly dependent time series. 539-547 - Uri Shalit, Gal Chechik:
Coordinate-descent for learning orthogonal matrices through Givens rotations. 548-556 - Anshumali Shrivastava, Ping Li:
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search. 557-565 - Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
A Divide-and-Conquer Solver for Kernel Support Vector Machines. 566-574 - Cho-Jui Hsieh, Peder A. Olsen:
Nuclear Norm Minimization via Active Subspace Selection. 575-583 - Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
Provable Bounds for Learning Some Deep Representations. 584-592 - Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit S. Dhillon:
Large-scale Multi-label Learning with Missing Labels. 593-601 - Rashish Tandon, Pradeep Ravikumar:
Learning Graphs with a Few Hubs. 602-610 - Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle:
Agnostic Bayesian Learning of Ensembles. 611-619 - Samaneh Azadi, Suvrit Sra:
Towards an optimal stochastic alternating direction method of multipliers. 620-628 - Shiwei Lan, Bo Zhou, Babak Shahbaba:
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions. 629-637 - Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté:
Efficient Continuous-Time Markov Chain Estimation. 638-646 - Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman
, Ning Zhang, Eric Tzeng, Trevor Darrell:
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. 647-655 - Dani Yogatama, Noah A. Smith:
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers. 656-664 - Misha Denil, David Matheson, Nando de Freitas:
Narrowing the Gap: Random Forests In Theory and In Practice. 665-673 - Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward:
Coherent Matrix Completion. 674-682 - David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Admixture of Poisson MRFs: A Topic Model with Word Dependencies. 683-691 - Harm van Seijen, Richard S. Sutton:
True Online TD(lambda). 692-700 - Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. 701-709 - Amirmohammad Rooshenas, Daniel Lowd:
Learning Sum-Product Networks with Direct and Indirect Variable Interactions. 710-718 - Jascha Sohl-Dickstein, Mayur Mudigonda, Michael Robert DeWeese:
Hamiltonian Monte Carlo Without Detailed Balance. 719-726 - Jacob Steinhardt, Percy Liang:
Filtering with Abstract Particles. 727-735 - Taiji Suzuki:
Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers. 736-744 - Jian Zhou, Olga G. Troyanskaya:
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction. 745-753 - Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An Efficient Approach for Assessing Hyperparameter Importance. 754-762
Cycle 2 Papers
- Ke Sun, Stéphane Marchand-Maillet:
An Information Geometry of Statistical Manifold Learning. 1-9 - Masrour Zoghi, Shimon Whiteson, Rémi Munos, Maarten de Rijke:
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem. 10-18 - Raffay Hamid, Ying Xiao, Alex Gittens, Dennis DeCoste:
Compact Random Feature Maps. 19-27 - Aryeh Kontorovich:
Concentration in unbounded metric spaces and algorithmic stability. 28-36 - Daniel J. Hsu, Sivan Sabato:
Heavy-tailed regression with a generalized median-of-means. 37-45 - Michal Valko, Rémi Munos, Branislav Kveton, Tomás Kocák:
Spectral Bandits for Smooth Graph Functions. 46-54 - Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Lei Zhang:
Robust Principal Component Analysis with Complex Noise. 55-63 - Qi-Xing Huang, Yuxin Chen, Leonidas J. Guibas:
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation. 64-72 - Cun Mu, Bo Huang, John Wright, Donald Goldfarb:
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery. 73-81 - Sanmay Das, Allen Lavoie:
Automated inference of point of view from user interactions in collective intelligence venues. 82-90 - Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye:
Rank-One Matrix Pursuit for Matrix Completion. 91-99 - Yuxin Chen, Leonidas J. Guibas, Qi-Xing Huang:
Near-Optimal Joint Object Matching via Convex Relaxation. 100-108 - Dmitry Malioutov, Nikolai Slavov:
Convex Total Least Squares. 109-117 - Pratik Jawanpuria, Manik Varma, J. Saketha Nath:
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection. 118-126 - Xiaotong Yuan, Ping Li, Tong Zhang:
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization. 127-135 - Jean Honorio, Tommi S. Jaakkola:
A Unified Framework for Consistency of Regularized Loss Minimizers. 136-144 - Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye:
Geodesic Distance Function Learning via Heat Flow on Vector Fields. 145-153 - Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, Andreas Krause:
Near-Optimally Teaching the Crowd to Classify. 154-162 - Walid Krichene, Benjamin Drighès, Alexandre M. Bayen:
On the convergence of no-regret learning in selfish routing. 163-171 - Jérémie Mary, Philippe Preux, Olivier Nicol:
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques. 172-180 - Aviv Tamar, Shie Mannor, Huan Xu:
Scaling Up Robust MDPs using Function Approximation. 181-189 - Wei Ping, Qiang Liu, Alexander Ihler:
Marginal Structured SVM with Hidden Variables. 190-198 - Yariv Dror Mizrahi, Misha Denil, Nando de Freitas:
Linear and Parallel Learning of Markov Random Fields. 199-207 - Yarin Gal, Zoubin Ghahramani:
Pitfalls in the use of Parallel Inference for the Dirichlet Process. 208-216 - Yuan Zhou, Xi Chen, Jian Li:
Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing. 217-225 - Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski:
Deep Generative Stochastic Networks Trainable by Backprop. 226-234 - Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, Jieping Ye:
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models. 235-243 - Yudong Chen, Jiaming Xu:
Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting. 244-252 - Emile Contal, Vianney Perchet, Nicolas Vayatis:
Gaussian Process Optimization with Mutual Information. 253-261 - Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek:
Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. 262-270 - Mathias Niepert, Pedro M. Domingos:
Exchangeable Variable Models. 271-279 - Shai Ben-David, Nika Haghtalab:
Clustering in the Presence of Background Noise. 280-288 - Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye:
Safe Screening with Variational Inequalities and Its Application to Lasso. 289-297 - Shan-Hung Wu, Hao-Heng Chien, Kuan-Hua Lin, Philip S. Yu:
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks. 298-306 - Joan Bruna Estrach, Arthur Szlam, Yann LeCun:
Signal recovery from Pooling Representations. 307-315 - Emma Brunskill, Lihong Li:
PAC-inspired Option Discovery in Lifelong Reinforcement Learning. 316-324 - Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang:
Multi-label Classification via Feature-aware Implicit Label Space Encoding. 325-333 - Sébastien Bratières, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani:
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications. 334-342 - Stéphan Clémençon, Sylvain Robbiano:
Anomaly Ranking as Supervised Bipartite Ranking. 343-351 - Gunnar E. Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra:
Hierarchical Quasi-Clustering Methods for Asymmetric Networks. 352-360 - Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Rectangular Tiling Process. 361-369 - Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis:
Two-Stage Metric Learning. 370-378 - José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. 379-387 - Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for High-Dimensional Linear Regression. 388-396 - Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. 397-405 - Yuan Fang, Kevin Chen-Chuan Chang, Hady Wirawan Lauw:
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically. 406-414 - Chengtao Li, Jun Zhu, Jianfei Chen:
Bayesian Max-margin Multi-Task Learning with Data Augmentation. 415-423 - Zhiwei Qin, Weichang Li, Firdaus Janoos:
Sparse Reinforcement Learning via Convex Optimization. 424-432 - Filipe Rodrigues, Francisco C. Pereira, Bernardete Ribeiro:
Gaussian Process Classification and Active Learning with Multiple Annotators. 433-441 - Hongyu Su, Aristides Gionis, Juho Rousu:
Structured Prediction of Network Response. 442-450 - Gavin Taylor, Connor Geer, David Piekut:
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. 451-459 - Zhirong Yang, Jaakko Peltonen, Samuel Kaski:
Optimization Equivalence of Divergences Improves Neighbor Embedding. 460-468 - Ji Liu, Stephen J. Wright, Christopher Ré, Victor Bittorf, Srikrishna Sridhar:
An Asynchronous Parallel Stochastic Coordinate Descent Algorithm. 469-477 - Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf:
Consistency of Causal Inference under the Additive Noise Model. 478-486 - Alexander G. Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun:
Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm. 487-495 - Alan Malek, Yasin Abbasi-Yadkori, Peter L. Bartlett:
Linear Programming for Large-Scale Markov Decision Problems. 496-504 - Feiping Nie, Yizhen Huang, Heng Huang:
Linear Time Solver for Primal SVM. 505-513 - Seong-Hwan Jun, Alexandre Bouchard-Côté:
Memory (and Time) Efficient Sequential Monte Carlo. 514-522 - Jie Wang, Peter Wonka, Jieping Ye:
Scaling SVM and Least Absolute Deviations via Exact Data Reduction. 523-531 - Xin Li, Yuhong Guo:
Latent Semantic Representation Learning for Scene Classification. 532-540 - Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant:
Least Squares Revisited: Scalable Approaches for Multi-class Prediction. 541-549 - Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski:
Local algorithms for interactive clustering. 550-558 - Ngo Anh Vien, Marc Toussaint:
Model-Based Relational RL When Object Existence is Partially Observable. 559-567 - Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt:
A new Q(lambda) with interim forward view and Monte Carlo equivalence. 568-576 - MohamadAli Torkamani, Daniel Lowd:
On Robustness and Regularization of Structural Support Vector Machines. 577-585 - Oscar Beijbom, Mohammad J. Saberian, David J. Kriegman, Nuno Vasconcelos:
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting. 586-594 - Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel:
Multimodal Neural Language Models. 595-603 - Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli:
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. 604-612 - Xinyang Yi, Constantine Caramanis, Sujay Sanghavi:
Alternating Minimization for Mixed Linear Regression. 613-621 - Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal:
Stochastic Neighbor Compression. 622-630 - Junfeng Wen, Chun-Nam Yu, Russell Greiner:
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification. 631-639 - Le Song, Animashree Anandkumar, Bo Dai, Bo Xie:
Nonparametric Estimation of Multi-View Latent Variable Models. 640-648 - Chris J. Maddison, Daniel Tarlow:
Structured Generative Models of Natural Source Code. 649-657 - Jinfeng Yi, Lijun Zhang, Jun Wang, Rong Jin, Anil K. Jain:
A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data. 658-666 - Panagiotis Toulis, Edoardo M. Airoldi, Jason Rennie:
Statistical analysis of stochastic gradient methods for generalized linear models. 667-675 - Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
Coding for Random Projections. 676-684 - Marco Cuturi, Arnaud Doucet:
Fast Computation of Wasserstein Barycenters. 685-693 - Fredrik D. Johansson, Vinay Jethava, Devdatt P. Dubhashi, Chiranjib Bhattacharyya:
Global graph kernels using geometric embeddings. 694-702 - Zhiyuan Chen, Bing Liu:
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data. 703-711 - Alon Vinnikov, Shai Shalev-Shwartz:
K-means recovers ICA filters when independent components are sparse. 712-720 - Yuekai Sun, Stratis Ioannidis, Andrea Montanari:
Learning Mixtures of Linear Classifiers. 721-729 - Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani:
The Falling Factorial Basis and Its Statistical Applications. 730-738 - Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli:
Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. 739-747 - Andreas Argyriou, Francesco Dinuzzo:
A Unifying View of Representer Theorems. 748-756 - Claudio Gentile, Shuai Li, Giovanni Zappella:
Online Clustering of Bandits. 757-765 - Neil Houlsby, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Cold-start Active Learning with Robust Ordinal Matrix Factorization. 766-774 - Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm:
Multivariate Maximal Correlation Analysis. 775-783 - Yasuhiro Fujiwara, Go Irie:
Efficient Label Propagation. 784-792 - Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schölkopf:
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. 793-801 - Ling Yan, Wu-Jun Li, Gui-Rong Xue, Dingyi Han:
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising. 802-810 - Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry P. Vetrov:
Putting MRFs on a Tensor Train. 811-819 - Lijun Zhang, Jinfeng Yi, Rong Jin:
Efficient Algorithms for Robust One-bit Compressive Sensing. 820-828 - Sergey Levine, Vladlen Koltun:
Learning Complex Neural Network Policies with Trajectory Optimization. 829-837 - Ting Zhang, Chao Du, Jingdong Wang:
Composite Quantization for Approximate Nearest Neighbor Search. 838-846 - Yoshikazu Terada, Ulrike von Luxburg:
Local Ordinal Embedding. 847-855 - Nir Ailon, Zohar Shay Karnin, Thorsten Joachims:
Reducing Dueling Bandits to Cardinal Bandits. 856-864 - Chang Xu, Dacheng Tao, Chao Xu, Yong Rui:
Large-margin Weakly Supervised Dimensionality Reduction. 865-873 - Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy:
Joint Inference of Multiple Label Types in Large Networks. 874-882 - Zohar Shay Karnin, Elad Hazan:
Hard-Margin Active Linear Regression. 883-891 - Aryeh Kontorovich, Roi Weiss:
Maximum Margin Multiclass Nearest Neighbors. 892-900 - Tian Lin, Bruno D. Abrahao, Robert D. Kleinberg, John C. S. Lui, Wei Chen:
Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications. 901-909 - Mélanie Rey, Volker Roth, Thomas J. Fuchs:
Sparse meta-Gaussian information bottleneck. 910-918 - Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
Nonparametric Estimation of Renyi Divergence and Friends. 919-927 - Jun-Kun Wang, Shou-de Lin:
Robust Inverse Covariance Estimation under Noisy Measurements. 928-936 - Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham:
Bayesian Optimization with Inequality Constraints. 937-945 - Felix X. Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang:
Circulant Binary Embedding. 946-954 - Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page:
Multiple Testing under Dependence via Semiparametric Graphical Models. 955-963 - Bojun Tu, Zhihua Zhang, Shusen Wang, Hui Qian:
Making Fisher Discriminant Analysis Scalable. 964-972 - Dongwoo Kim, Alice Oh:
Hierarchical Dirichlet Scaling Process. 973-981 - Issei Sato, Hiroshi Nakagawa:
Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process. 982-990 - Anastasia Pentina, Christoph H. Lampert:
A PAC-Bayesian bound for Lifelong Learning. 991-999 - Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. 1000-1008 - Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer:
Concept Drift Detection Through Resampling. 1009-1017 - David F. Gleich, Michael W. Mahoney:
Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow. 1018-1025 - Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon, Fabrizio Ruggeri:
A Bayesian Wilcoxon signed-rank test based on the Dirichlet process. 1026-1034 - Siamak Ravanbakhsh, Christopher Srinivasa, Brendan J. Frey, Russell Greiner:
Min-Max Problems on Factor Graphs. 1035-1043 - Sungjin Ahn, Babak Shahbaba, Max Welling:
Distributed Stochastic Gradient MCMC. 1044-1052 - Anoop Cherian:
Nearest Neighbors Using Compact Sparse Codes. 1053-1061 - Feiping Nie, Jianjun Yuan, Heng Huang:
Optimal Mean Robust Principal Component Analysis. 1062-1070 - Róbert Busa-Fekete, Eyke Hüllermeier, Balázs Szörényi:
Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. 1071-1079 - Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Yijun Zhao, Orrin Devinsky, Ruben Kuzniecky, Carla E. Brodley:
Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations. 1080-1088 - Jonathan Scholz, Martin Levihn, Charles Lee Isbell Jr., David Wingate:
A Physics-Based Model Prior for Object-Oriented MDPs. 1089-1097 - Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi:
Outlier Path: A Homotopy Algorithm for Robust SVM. 1098-1106 - Naiyan Wang, Dit-Yan Yeung:
Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data. 1107-1115 - Issei Sato, Hisashi Kashima, Hiroshi Nakagawa:
Latent Confusion Analysis by Normalized Gamma Construction. 1116-1124 - Aaron Defazio, Justin Domke, Tibério S. Caetano:
Finito: A faster, permutable incremental gradient method for big data problems. 1125-1133 - Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri:
Ensemble Methods for Structured Prediction. 1134-1142 - Simone Romano, James Bailey, Xuan Vinh Nguyen, Karin Verspoor:
Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance. 1143-1151 - Jason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth:
Preserving Modes and Messages via Diverse Particle Selection. 1152-1160 - Liming Wang, Abolfazl Razi, Miguel R. D. Rodrigues, A. Robert Calderbank, Lawrence Carin:
Nonlinear Information-Theoretic Compressive Measurement Design. 1161-1169 - Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. 1170-1178 - Corinna Cortes, Mehryar Mohri, Umar Syed:
Deep Boosting. 1179-1187 - Quoc V. Le, Tomás Mikolov:
Distributed Representations of Sentences and Documents. 1188-1196 - Robert McGibbon, Bharath Ramsundar, Mohammad Sultan, Gert Kiss, Vijay S. Pande:
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models. 1197-1205 - Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor:
Online Multi-Task Learning for Policy Gradient Methods. 1206-1214 - Jason Weston, Ron J. Weiss, Hector Yee:
Affinity Weighted Embedding. 1215-1223 - Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Benjamin Taskar:
Learning the Parameters of Determinantal Point Process Kernels. 1224-1232 - Elad Eban, Elad Mezuman, Amir Globerson:
Discrete Chebyshev Classifiers. 1233-1241 - Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra:
Deep AutoRegressive Networks. 1242-1250 - Peng Sun, Tong Zhang, Jie Zhou:
A Convergence Rate Analysis for LogitBoost, MART and Their Variant. 1251-1259 - Uri Heinemann, Amir Globerson:
Inferning with High Girth Graphical Models. 1260-1268 - Zhaoshi Meng, Brian Eriksson, Alfred O. Hero III:
Learning Latent Variable Gaussian Graphical Models. 1269-1277 - Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra:
Stochastic Backpropagation and Approximate Inference in Deep Generative Models. 1278-1286 - Yevgeny Seldin, Aleksandrs Slivkins:
One Practical Algorithm for Both Stochastic and Adversarial Bandits. 1287-1295 - Joachim Giesen, Sören Laue, Patrick Wieschollek:
Robust and Efficient Kernel Hyperparameter Paths with Guarantees. 1296-1304 - Xuezhi Wang, Tzu-Kuo Huang, Jeff G. Schneider:
Active Transfer Learning under Model Shift. 1305-1313 - Bruno Scherrer:
Approximate Policy Iteration Schemes: A Comparison. 1314-1322 - Tsung-Han Lin, H. T. Kung:
Stable and Efficient Representation Learning with Nonnegativity Constraints. 1323-1331 - Robert C. Grande, Thomas J. Walsh, Jonathan P. How:
Sample Efficient Reinforcement Learning with Gaussian Processes. 1332-1340 - Farhad Pourkamali-Anaraki, Shannon M. Hughes:
Memory and Computation Efficient PCA via Very Sparse Random Projections. 1341-1349 - Timothy A. Mann, Daniel J. Mankowitz, Shie Mannor:
Time-Regularized Interrupting Options (TRIO). 1350-1358 - David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. 1359-1367 - Yujia Li, Richard S. Zemel:
High Order Regularization for Semi-Supervised Learning of Structured Output Problems. 1368-1376 - Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Transductive Learning with Multi-class Volume Approximation. 1377-1385 - Borja Balle, William L. Hamilton, Joelle Pineau:
Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison. 1386-1394 - Nicolas Chapados:
Effective Bayesian Modeling of Groups of Related Count Time Series. 1395-1403 - Sergey Bartunov, Dmitry P. Vetrov:
Variational Inference for Sequential Distance Dependent Chinese Restaurant Process. 1404-1412 - Scott W. Linderman, Ryan P. Adams:
Discovering Latent Network Structure in Point Process Data. 1413-1421 - Kacper Chwialkowski, Arthur Gretton:
A Kernel Independence Test for Random Processes. 1422-1430 - Scott E. Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee:
Learning to Disentangle Factors of Variation with Manifold Interaction. 1431-1439 - Elham Azizi, Edoardo M. Airoldi, James E. Galagan:
Learning Modular Structures from Network Data and Node Variables. 1440-1448 - Yusuke Mukuta, Tatsuya Harada:
Probabilistic Partial Canonical Correlation Analysis. 1449-1457 - Marc G. Bellemare, Joel Veness, Erik Talvitie:
Skip Context Tree Switching. 1458-1466 - Christopher Tosh, Sanjoy Dasgupta:
Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians. 1467-1475 - Minmin Chen, Kilian Q. Weinberger, Fei Sha, Yoshua Bengio:
Marginalized Denoising Auto-encoders for Nonlinear Representations. 1476-1484 - David Barber, Yali Wang:
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations. 1485-1493 - Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes:
Fast Multi-stage Submodular Maximization. 1494-1502 - Marc Schoenauer, Riad Akrour, Michèle Sebag, Jean-Christophe Souplet:
Programming by Feedback. 1503-1511 - José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Probabilistic Matrix Factorization with Non-random Missing Data. 1512-1520 - Lili Dworkin, Michael J. Kearns, Yuriy Nevmyvaka:
Pursuit-Evasion Without Regret, with an Application to Trading. 1521-1529 - Sven Kurras, Ulrike von Luxburg, Gilles Blanchard:
The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation. 1530-1538 - Mingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan:
Riemannian Pursuit for Big Matrix Recovery. 1539-1547 - Leonidas Lefakis, François Fleuret:
Dynamic Programming Boosting for Discriminative Macro-Action Discovery. 1548-1556 - Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill:
Online Stochastic Optimization under Correlated Bandit Feedback. 1557-1565 - Yudong Chen, Shiau Hong Lim, Huan Xu:
Weighted Graph Clustering with Non-Uniform Uncertainties. 1566-1574 - Philip Thomas:
GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results. 1575-1583 - Qinxun Bai, Henry Lam, Stan Sclaroff:
A Bayesian Framework for Online Classifier Ensemble. 1584-1592 - Jacob Steinhardt, Percy Liang:
Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm. 1593-1601 - Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. 1602-1610 - Hyun Oh Song, Ross B. Girshick, Stefanie Jegelka, Julien Mairal, Zaïd Harchaoui, Trevor Darrell:
On learning to localize objects with minimal supervision. 1611-1619 - Risi Kondor, Nedelina Teneva, Vikas K. Garg:
Multiresolution Matrix Factorization. 1620-1628 - Li-Ping Liu, Thomas G. Dietterich:
Learnability of the Superset Label Learning Problem. 1629-1637 - Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire:
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. 1638-1646 - Roni Mittelman, Benjamin Kuipers, Silvio Savarese, Honglak Lee:
Structured Recurrent Temporal Restricted Boltzmann Machines. 1647-1655 - Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson:
Scalable and Robust Bayesian Inference via the Median Posterior. 1656-1664 - Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. 1665-1673 - Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-Stationary Functions. 1674-1682 - Tianqi Chen, Emily B. Fox, Carlos Guestrin:
Stochastic Gradient Hamiltonian Monte Carlo. 1683-1691 - George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Björn W. Schuller:
A Deep Semi-NMF Model for Learning Hidden Representations. 1692-1700 - Ruiliang Zhang, James T. Kwok:
Asynchronous Distributed ADMM for Consensus Optimization. 1701-1709 - Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson:
Spectral Regularization for Max-Margin Sequence Tagging. 1710-1718 - Gaurav Pandey, Ambedkar Dukkipati:
Learning by Stretching Deep Networks. 1719-1727 - Megasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Nonnegative Sparse PCA with Provable Guarantees. 1728-1736 - Bruno Castro da Silva, George Dimitri Konidaris, Andrew G. Barto:
Active Learning of Parameterized Skills. 1737-1745 - Oren Rippel, Michael A. Gelbart, Ryan P. Adams:
Learning Ordered Representations with Nested Dropout. 1746-1754 - Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. 1755-1763 - Alex Graves, Navdeep Jaitly:
Towards End-To-End Speech Recognition with Recurrent Neural Networks. 1764-1772 - Jinli Hu, Amos J. Storkey:
Multi-period Trading Prediction Markets with Connections to Machine Learning. 1773-1781 - Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. 1782-1790 - Andriy Mnih, Karol Gregor:
Neural Variational Inference and Learning in Belief Networks. 1791-1799 - Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin:
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors. 1800-1808 - Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani:
Beta Diffusion Trees. 1809-1817 - Cícero Nogueira dos Santos, Bianca Zadrozny:
Learning Character-level Representations for Part-of-Speech Tagging. 1818-1826 - Adams Wei Yu, Fatma Kilinç-Karzan, Jaime G. Carbonell:
Saddle Points and Accelerated Perceptron Algorithms. 1827-1835 - Hua Wang, Feiping Nie, Heng Huang:
Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization. 1836-1844 - Kareem Amin, Hoda Heidari, Michael J. Kearns:
Learning from Contagion (Without Timestamps). 1845-1853 - Matthew James Johnson, Alan S. Willsky:
Stochastic Variational Inference for Bayesian Time Series Models. 1854-1862 - Jan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber:
A Clockwork RNN. 1863-1871 - Arun Tejasvi Chaganty, Percy Liang:
Estimating Latent-Variable Graphical Models using Moments and Likelihoods. 1872-1880 - Srinadh Bhojanapalli, Prateek Jain:
Universal Matrix Completion. 1881-1889 - Dimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis:
Finding Dense Subgraphs via Low-Rank Bilinear Optimization. 1890-1898 - Jan A. Botha, Phil Blunsom:
Compositional Morphology for Word Representations and Language Modelling. 1899-1907 - Alexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang:
Learning Polynomials with Neural Networks. 1908-1916 - Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh:
Exponential Family Matrix Completion under Structural Constraints. 1917-1925 - Philip Bachman, Amir-massoud Farahmand, Doina Precup:
Sample-based approximate regularization. 1926-1934 - Brooks Paige, Frank D. Wood:
A Compilation Target for Probabilistic Programming Languages. 1935-1943 - James Neufeld, András György, Csaba Szepesvári, Dale Schuurmans:
Adaptive Monte Carlo via Bandit Allocation. 1944-1952 - Safiye Celik, Benjamin A. Logsdon, Su-In Lee:
Efficient Dimensionality Reduction for High-Dimensional Network Estimation. 1953-1961 - E. Busra Celikkaya, Christian R. Shelton:
Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes. 1962-1970 - Michalis K. Titsias, Miguel Lázaro-Gredilla:
Doubly Stochastic Variational Bayes for non-Conjugate Inference. 1971-1979 - Daryl Lim, Gert R. G. Lanckriet:
Efficient Learning of Mahalanobis Metrics for Ranking. 1980-1988 - Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal:
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare. 1989-1997 - David A. Knowles, Zoubin Ghahramani, Konstantina Palla:
A reversible infinite HMM using normalised random measures. 1998-2006 - Benjamin D. Haeffele, Eric Young, René Vidal:
Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing. 2007-2015 - Nan Du, Yingyu Liang, Maria-Florina Balcan, Le Song:
Influence Function Learning in Information Diffusion Networks. 2016-2024
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-03 20:31 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
