Statistical Learning
Mutually Regressive Point Processes
Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
Neural Information Processing Systems http://nips.cc/
Learning Layer-wise Equivariances Automatically using Gradients Tycho F.A. van der Ouderaa
First, it requires efficient and flexible parameterisations of layer-wise equivari-ances.
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity
This is, e.g., the typical setting of many ERM
Maximum-Entropy Fine Grained Classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
Neural Information Processing Systems http://nips.cc/
Harnessing small projectors and multiple views for efficient vision pretraining
We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.
Discriminative Topic Modeling with Logistic LDA
Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis
Neural Information Processing Systems http://nips.cc/
Binary Rating Estimation with Graph Side Information
Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh
Neural Information Processing Systems http://nips.cc/
On the Power and Limitations of Random Features for Understanding Neural Networks
Considering the one-hidden-layer example above, this corresponds to learning linear predictors over a fixed representation (chosen obliviously and randomly at initialization).
