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Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
Inthis paper we propose aframework to systematically investigate bias and generalization in deep generative models of images. Inspired byexperimental methods fromcognitivepsychology,weprobe each learning algorithm with carefully designed training datasets tocharacterize when and howexisting models generate novelattributes and their combinations.
Learning Hawkes Processes from a handful of events
Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran
Neural Information Processing Systems http://nips.cc/
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma
Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the Neural Tangent Kernel (NTK).
Single-Agent Policy Tree Search With Guarantees
Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
Neural Information Processing Systems http://nips.cc/
SCRREAM: SCan, Register, REnder And Map: A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
Indoor tasks are heavily related to human activities.
Regularizing by the Variance of the Activations' Sample-Variances
Neural Information Processing Systems http://nips.cc/
Reinforcement Learning with Convex Constraints
In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints.