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Greedy Sampling for Approximate Clustering in the Presence of Outliers
Aditya Bhaskara, Sharvaree Vadgama, Hong Xu
Neural Information Processing Systems http://nips.cc/
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cedric Archambeau, Rodolphe Jenatton
Neural Information Processing Systems http://nips.cc/
Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric J. Horvitz, Debadeepta Dey
Neural Information Processing Systems http://nips.cc/
Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
In this paper, we propose an efficient search method of augmentation policies, called Fast AutoAugment,motivatedbyBayesianDA[36].
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
To this end, we first establish a new class of GNNs that can solve a strictly wider variety of problems than existing GNNs. Then, we bridge the gap between GNN theory and the theory of distributed local algorithms.
Sample Efficient Active Learning of Causal Trees
Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler
Neural Information Processing Systems http://nips.cc/
and
Successfully employing cutting planes can be challenging because there are infinitely many cuts to choose from and there are still many open questions about which cuts to employ when.