Papers
Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees
Jinchun Zhan, Brian Lois, Han Guo et al.
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks
Abdullah Rashwan, Han Zhao, Pascal Poupart
Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation
Dejiao Zhang, Laura Balzano
Supervised Neighborhoods for Distributed Nonparametric Regression
Adam Bloniarz, Ameet Talwalkar, Bin Yu et al.
Multiresolution Matrix Compression
Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor
Chained Gaussian Processes
Alan D. Saul, James Hensman, Aki Vehtari et al.
Spectral M-estimation with Applications to Hidden Markov Models
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments
Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert
NYTRO: When Subsampling Meets Early Stopping
Raffaello Camoriano, Tomás Angles, Alessandro Rudi et al.
Loss Bounds and Time Complexity for Speed Priors
Daniel Filan, Jan Leike, Marcus Hutter
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions
Loic Landrieu, Guillaume Obozinski
Back to the Future: Radial Basis Function Networks Revisited
Qichao Que, Mikhail Belkin
Streaming Kernel Principal Component Analysis
Mina Ghashami, Daniel J. Perry, Jeff Phillips
Active Learning Algorithms for Graphical Model Selection
Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan et al.
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization
Zhao Song, Ricardo Henao, David Carlson et al.
A Fast and Reliable Policy Improvement Algorithm
Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright
Efficient Sampling for k-Determinantal Point Processes
Chengtao Li, Stefanie Jegelka, Suvrit Sra
Parallel Markov Chain Monte Carlo via Spectral Clustering
Guillaume Basse, Aaron Smith, Natesh Pillai
Private Causal Inference
Matt J. Kusner, Yu Sun, Karthik Sridharan et al.
Communication Efficient Distributed Agnostic Boosting
Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau
Towards Stability and Optimality in Stochastic Gradient Descent
Panos Toulis, Dustin Tran, Edo Airoldi
On Lloyd’s Algorithm: New Theoretical Insights for Clustering in Practice
Cheng Tang, Claire Monteleoni
Robust Covariate Shift Regression
Xiangli Chen, Mathew Monfort, Anqi Liu et al.
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation
Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar