AISTATS 2018
216 papers
A Generic Approach for Escaping Saddle Points
Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola Accelerated Stochastic Power Iteration
Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré AdaGeo: Adaptive Geometric Learning for Optimization and Sampling
Gabriele Abbati, Alessandra Tosi, Michael A. Osborne, Seth R. Flaxman Adaptive Sampling for Coarse Ranking
Sumeet Katariya, Lalit K. Jain, Nandana Sengupta, James Evans, Robert Nowak An Analysis of Categorical Distributional Reinforcement Learning
Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh Approximate Ranking from Pairwise Comparisons
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer Bayesian Approaches to Distribution Regression
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman Bayesian Structure Learning for Dynamic Brain Connectivity
Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo Benefits from Superposed Hawkes Processes
Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin Best Arm Identification in Multi-Armed Bandits with Delayed Feedback
Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon Boosting Variational Inference: An Optimization Perspective
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch Catalyst for Gradient-Based Nonconvex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui Cause-Effect Inference by Comparing Regression Errors
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf Combinatorial Semi-Bandits with Knapsacks
Karthik Abinav Sankararaman, Aleksandrs Slivkins Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluç, Dmitriy Morozov, Leonid Oliker, Katherine A. Yelick, Sang-Yun Oh Comparison Based Learning from Weak Oracles
Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi Contextual Bandits with Stochastic Experts
Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai Crowdclustering with Partition Labels
Junxiang Chen, Yale Chang, Peter J. Castaldi, Michael H. Cho, Brian D. Hobbs, Jennifer G. Dy Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön Differentially Private Regression with Gaussian Processes
Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence Dropout as a Low-Rank Regularizer for Matrix Factorization
Jacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal Efficient Weight Learning in High-Dimensional Untied MLNs
Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal Factor Analysis on a Graph
Masayuki Karasuyama, Hiroshi Mamitsuka Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-Term HIV Therapy
Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi, Francesca Incardona, Yaara Goldschmidt FLAG N' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien Gauged Mini-Bucket Elimination for Approximate Inference
Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller Gradient Diversity: A Key Ingredient for Scalable Distributed Learning
Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett Group Invariance Principles for Causal Generative Models
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida Iterative Spectral Method for Alternative Clustering
Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David R. Kaeli, Jennifer G. Dy Learning Hidden Quantum Markov Models
Siddarth Srinivasan, Geoffrey J. Gordon, Byron Boots Learning Priors for Invariance
Eric T. Nalisnick, Padhraic Smyth Learning to Round for Discrete Labeling Problems
Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar Matrix-Normal Models for fMRI Analysis
Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore L. Willke, Jonathan D. Cohen Medoids in Almost-Linear Time via Multi-Armed Bandits
Vivek Kumar Bagaria, Govinda M. Kamath, Vasilis Ntranos, Martin J. Zhang, David Tse Metrics for Deep Generative Models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt Multi-Scale Nystrom Method
Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams Near-Optimal Machine Teaching via Explanatory Teaching Sets
Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue Nested CRP with Hawkes-Gaussian Processes
Xi Tan, Vinayak A. Rao, Jennifer Neville Nonlinear Weighted Finite Automata
Tianyu Li, Guillaume Rabusseau, Doina Precup Nonparametric Preference Completion
Julian Katz-Samuels, Clayton Scott One-Shot Coresets: The Case of K-Clustering
Olivier Bachem, Mario Lucic, Silvio Lattanzi Online Regression with Partial Information: Generalization and Linear Projection
Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi Optimal Cooperative Inference
Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto Parallelised Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos Personalized and Private Peer-to-Peer Machine Learning
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi Post Selection Inference with Kernels
Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi Product Kernel Interpolation for Scalable Gaussian Processes
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson Proximity Variational Inference
Jaan Altosaar, Rajesh Ranganath, David M. Blei Random Subspace with Trees for Feature Selection Under Memory Constraints
Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock Regional Multi-Armed Bandits
Zhiyang Wang, Ruida Zhou, Cong Shen Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham Robust Active Label Correction
Jan Kremer, Fei Sha, Christian Igel Robust Locally-Linear Controllable Embedding
Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi Scalable Generalized Dynamic Topic Models
Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt Semi-Supervised Prediction-Constrained Topic Models
Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez Smooth and Sparse Optimal Transport
Mathieu Blondel, Vivien Seguy, Antoine Rolet Solving Lp-Norm Regularization with Tensor Kernels
Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens Sparse Linear Isotonic Models
Sheng Chen, Arindam Banerjee Stochastic Zeroth-Order Optimization in High Dimensions
Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh Structured Factored Inference for Probabilistic Programming
Avi Pfeffer, Brian E. Ruttenberg, William Kretschmer, Alison O'Connor Structured Optimal Transport
David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka Submodularity on Hypergraphs: From Sets to Sequences
Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi Symmetric Variational Autoencoder and Connections to Adversarial Learning
Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin Teacher Improves Learning by Selecting a Training Subset
Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu The Emergence of Spectral Universality in Deep Networks
Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli The Geometry of Random Features
Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller The Power Mean Laplacian for Multilayer Graph Clustering
Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein Topic Compositional Neural Language Model
Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin Transfer Learning on fMRI Datasets
Hejia Zhang, Po-Hsuan Chen, Peter J. Ramadge VAE with a VampPrior
Jakub M. Tomczak, Max Welling Variational Rejection Sampling
Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon Variational Sequential Monte Carlo
Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei