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Inductive Representation Learning on Large Graphs
Will Hamilton, Zhitao Ying, Jure Leskovec
The two first authors made equal contributions.
Self-Normalizing Neural Networks
Gรผnter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
Cross-Spectral Factor Analysis
Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson
We also introduce a semi-supervised approach, termed discriminative CSFA (dCSFA).
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening
Kevin Lin, James L. Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani
This was proposed and named by Tibshirani et al. (2005),
Variance-based Regularization with Convex Objectives
Hongseok Namkoong, John C. Duchi
To describe our approach, we require a few definitions.
Renyi Differential Privacy Mechanisms for Posterior Sampling
Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
As data analysis continues to expand and permeate ever more facets of life, the concerns over the privacy of one's data grow too.
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Raymond Yeh, Jinjun Xiong, Wen-Mei Hwu, Minh Do, Alexander Schwing
Textual grounding is an important but challenging task for human-computer interaction, robotics and knowledge mining.
A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle
Neural Information Processing Systems http://nips.cc/
Action Centered Contextual Bandits
Kristjan Greenewald, Ambuj Tewari, Susan Murphy, Predag Klasnja
Furthermore, if the linear model is true, we get very strong performance guarantees.
