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GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney
Neural Information Processing Systems http://nips.cc/
On the Local Minima of the Empirical Risk
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
Don Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain
Neural Information Processing Systems http://nips.cc/
Constraint-based Causal Structure Learning with Consistent Separating Sets
This paper concerns, more specifically, the inconsistency of separating sets used to remove dispensable edges, iteratively, based on conditional independence tests.
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds Roi Livni Department Electrical Engineering Tel Aviv University rlivni@tauex.tau.ac.il
But nonetheless, they tend to perform well on unseen test data.
Adversarial Regularizers in Inverse Problems
Sebastian Lunz, Carola Schoenlieb, Ozan Öktem
Neural Information Processing Systems http://nips.cc/