Papers
Multi-view Metric Learning in Vector-valued Kernel Spaces
Riikka Huusari, Hachem Kadri, Cécile Capponi
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
Nearly second-order optimality of online joint detection and estimation via one-sample update schemes
Yang Cao, Liyan Xie, Yao Xie et al.
Near-Optimal Machine Teaching via Explanatory Teaching Sets
Yuxin Chen, Oisin Mac Aodha, Shihan Su et al.
Nested CRP with Hawkes-Gaussian Processes
Xi Tan, Vinayak Rao, Jennifer Neville
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding
Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang
Nonlinear Weighted Finite Automata
Tianyu Li, Guillaume Rabusseau, Doina Precup
Nonparametric Bayesian sparse graph linear dynamical systems
Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Mathieu Sinn, Ambrish Rawat
Nonparametric Preference Completion
Julian Katz-Samuels, Clayton Scott
Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization
Seung-Jean Kim, Johan Lim, Joong-Ho Won
On denoising modulo 1 samples of a function
Mihai Cucuringu, Hemant Tyagi
One-shot Coresets: The Case of k-Clustering
Olivier Bachem, Mario Lucic, Silvio Lattanzi
Online Boosting Algorithms for Multi-label Ranking
Young Hun Jung, Ambuj Tewari
Online Continuous Submodular Maximization
Lin Chen, Hamed Hassani, Amin Karbasi
Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments
Yanning Shen, Tianyi Chen, Georgios Giannakis
Online Learning with Non-Convex Losses and Non-Stationary Regret
Xiand Gao, Xiaobo Li, Shuzhong Zhang
Online Regression with Partial Information: Generalization and Linear Projection
Shinji Ito, Daisuke Hatano, Hanna Sumita et al.
On Statistical Optimality of Variational Bayes
Debdeep Pati, Anirban Bhattacharya, Yun Yang
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul Krishnan, Dawen Liang, Matthew Hoffman
On the Statistical Efficiency of Compositional Nonparametric Prediction
Yixi Xu, Jean Honorio, Xiao Wang
On Truly Block Eigensolvers via Riemannian Optimization
Zhiqiang Xu, Xin Gao
Optimal Cooperative Inference
Scott Cheng-Hsin Yang, Yue Yu, arash Givchi et al.
Optimality of Approximate Inference Algorithms on Stable Instances
Hunter Lang, David Sontag, Aravindan Vijayaraghavan