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Learning in Games with Lossy Feedback
Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye
Even though the underlying stage game is fixed (i.e.
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data
The assumption that data are independently and identically distributed (IID) is staple in statistical machine learning. It suggests that a hypothesis selected by an algorithm, after observing several training samples, should perform effectively on test samples from the same unknown distribution.
da18c47118a2d09926346f33bebde9f4-Paper-Conference.pdf
GPand JPCaresupportedbythe Simons Foundation, McKnight Foundation, Grossman Centerforthe Statisticsof Mind, and Gatsby Charitable Trust.
5aad86aa2a3c00b70c71e19bc4780319-Paper-Conference.pdf
Behavior is shaped by various factors operating across different timescales.
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney
Neural Information Processing Systems http://nips.cc/
DenoiseRep: DenoisingModelfor RepresentationLearning
Experimental results on various discriminative vision tasks, including re-identification (Market-1501, DukeMTMC-reID, MSMT17, CUHK-03,vehicleID),imageclassification(ImageNet,UB200,Oxford-Pet,Flowers), object detection (COCO), image segmentation (ADE20K) show stability and impressive improvements.