Optimization
Computational Separations between Sampling and Optimization
Recent work [Ma et al., 2019] shows that in the non-convex case, sampling
Counting the Optimal Solutions in Graphical Models
Decision Diagram (AOMDD) which represents all its optimal solutions.
Blended Matching Pursuit
Cyrille Combettes, Sebastian Pokutta
Neural Information Processing Systems http://nips.cc/
Certifying Geometric Robustness of Neural Networks
To address this issue, one would ideally like to prove that a given network is free of such geometric adversarial examples.
Adaptive Influence Maximization with Myopic Feedback
Influence maximization and its variants have applications in viral marketing, rumor control, etc. and
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
In this work, we use the same setting as Saxe et al. [2018]: a regression problem with least-squares
Statistical Model Aggregation via Parameter Matching
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
Neural Information Processing Systems http://nips.cc/
The Randomized Midpoint Method for Log-Concave Sampling
Neural Information Processing Systems http://nips.cc/
Graph Structured Prediction Energy Networks Colin Graber Alexander Schwing
To address this shortcoming, we introduce'Graph Structured Prediction Energy Networks,' for which we develop inference techniques that allow to both model explicit local and implicit