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Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization
Robert Gower, Filip Hanzely, Peter Richtarik, Sebastian U. Stich
Neural Information Processing Systems http://nips.cc/
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Neural Information Processing Systems http://nips.cc/
Genetic-Gated Networks for Deep Reinforcement Learning
Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak
Neural Information Processing Systems http://nips.cc/
NEON2: Finding Local Minima via First-Order Oracles
Neural Information Processing Systems http://nips.cc/
Learning Invariances using the Marginal Likelihood
Mark van der Wilk, Matthias Bauer, ST John, James Hensman
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations of the input that are known to be irrelevant (e.g.
Preference Based Adaptation for Learning Objectives
Neural Information Processing Systems http://nips.cc/
Reviews: Is Q-Learning Provably Efficient?
This paper studies the problem of efficient exploration in finite episodic MDPs. They present a variant of optimistic initialization tuned learning rates for Q-learning that recover a UCB-style algorithm. The main contribution of this work is a polynomial regret bound for perhaps one of the most iconic "model-free" algorithms. There are several things to like about this paper: - Q-learning is perhaps the classic intro to RL algorithms, so it's nice to see that we can recover sample efficient guarantees for a variant of this algorithm. The computational time is also particularly appealing compared to existing model-free algorithms with sqrt{T} *expected* (Bayesian) regret (such as RLSVI), which have much higher computational and memory requirements.
Is Q-Learning Provably Efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
On Learning Markov Chains
Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
Neural Information Processing Systems http://nips.cc/