NeurIPS 2019顶会 70页pdf硬核笔记

David Abel 是美国布朗大学计算机科学专业的在读博士生，师从Michael Littman，研究重点是抽象概念及其在智能中的应用。同时还是牛津大学Future of Humanity Institute 的一名实习生。

个人主页 https://david-abel.github.io/

他从深度学习理论、强化学习、博弈论和元学习等主题出发记载参会的一些亮点与主要内容。

值得注意的是，整个参会笔记多达 70 页，他记载了很多新研究的背景、观点与解决方案，也是干货满满。

笔记摘要：

1. 深度学习理论

• Outstanding New Directions Paper: Uniform Convergence may be Unable to Explain Generalization in Deep Learning

• 论文地址：

• https://papers.nips.cc/paper/8722-distribution-independent-pac-learning-of-halfspaces-with-massart-noise

• On Exact Computation with an Innitely Wide Neural Net ，https://papers.nips.cc/paper/9025-on-exact-computation-with-an-infinitely-wide-neural-net

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• Generalization Bounds of SGD for Wide and Deep NNs

• Efficient and Accurate Estimation of Lipschitz Constants for DNNs

• Regularization Effect of Large Initial Learning Rate

• Data-Dependent Sample Complexity for Deep NNs

2. 强化学习

• Causal Confusion in Imitation Learning

• Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

• Learning to Control Self-Assembling Morpholgies

• A Structured Prediction Approach for Generalization in Cooperative MultiAgent RL

• Learning Compositional neural Programs with Recursive Tree Search

• Guided Meta-Policy Search

• Using a Logarithmic Mapping to Enable a Lower Discount Factor

• Better Exploration with Optimistic Actor Critic

• Robust Exploration in Linear Quadratic Reinforcement Learning

• Tight Regret bounds for Model-Based RL with Greedy Policies

• Hindsight Credit Assignment

• Weight Agnostic Neural Networks

• A Neurally Plausible Model Learns Successor Representations in Partially

• Observable Environments

• DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

• VIREL: A Variational Inference Framework for RL

• Unsupervised Curriculua for Visual Meta RL

• Policy Continuation with Hindsight Inverse Dynamics

• Learning Reward Machines for Partially Observable RL

3. 【Yoshua Bengio特邀报告NeurIPS2019】深度学习系统从1代到2代，36页ppt概述DL进展与未来

4. 博弈论与公平性

• Optimizing Generalized Rate Metrics with Three Players

• Modeling Conceptual Understanding in Image Reference Games

• This Looks Like That: Deep Learning for Interpretable Image Recognition

• Assessing Social and Intersectional Biases in Word Representations

• Paradoxes in Fair Machine Learning

5. 元学习

• Workshop: Meta-Learning

• Erin Grant on Meta-Learning as Hierarchical Modelling

• How Meta-Learning Could Help Us Accomplish our Grandest

• AI Ambitions

• Spotlight: Meta-Learning Contextual Bandit Exploration

• Spotlight: ES-MAML: Hessian Free Meta Learning

• Spotlight: Quantile Based Approach for Hyperparameter Transfer Learning

• Spotlight: Meta-World: Benchmark for Meta-RL

• Pieter Abbeel: Better Model-based RL through Meta-RL

• Panel Discussion: Erin Grant, Je Clune, Pieter Abbeel

• Raia Hadsell on Scalable Meta-Learning

• Spotlight: Meta-Learning with Warped Gradient Descent

• Spotlight: Meta-Pix: Few Shot Video Targeting

• Brenden Lake on Compositional Generalization in Minds and Machines

笔记资料，请关注公众号“深度学习技术前沿”，在后台回复“NeurIPS 2019” 即可以获取资料哈~