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- NIPS 2017 Workshop Book
- Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
- Machine Learning Complexity of Learning
- Minimax PAC Bounds on the Sample Complexity of Reinforcement Learning with a Generative Model Mohammad Gheshlaghi Azar, Rémi Munos, Hilbert Kappen
- Semi-Supervised Clustering: Learning with Limited User Feedback The
- Arxiv:1902.01449V1 [Stat.ML] 4 Feb 2019 AE “Wants to Remember Everything a Classifier Wants to Forget”
- Reinforcement Learning Lecture 3: RL Problems, Sample Complexity and Regret
- Regularized Phasemax Achieves Optimal Sample Complexity
- NIPS 2018 Workshop Book Generated Thu Mar 07, 2019
- Rnns + PAC Learning
- Is Q-Learning Provably Efficient?
- The Sample Complexity of Teaching by Reinforcement on Q-Learning
- Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems
- Episodic Reinforcement Learning in Finite Mdps: Minimax Lower Bounds Revisited
- On the Computational Efficiency of Training Neural Networks
- Arxiv:1712.00409V1 [Cs.LG] 1 Dec 2017 Adoption, Which Drives Increased DL Development Investments in Existing and Emerging Application Domains
- The Sample-Complexity of General Reinforcement Learning
- On the Sample Complexity of Actor-Critic for Reinforcement Learning
- Learning Theory (Cont.) and Optimization (Start)
- Layered Sampling for Robust Optimization Problems
- Feature Purification: How Adversarial Training Performs Robust Deep Learning
- Analyzing Attention Mechanisms Through Lens of Sample Complexity and Loss Land
- Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
- Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
- Sample Complexity of Testing the Manifold Hypothesis
- On the Sample Complexity of Reinforcement Learning with a Generative Model
- The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
- Generalization Error in Deep Learning
- Size-Independent Sample Complexity of Neural Networks (Extended Abstract)
- Improved Sample Complexity for Incremental Autonomous Exploration in Mdps
- Model-Based Reinforcement Learning for Atari
- How Many Samples Are Needed to Estimate a Convolutional Neural Network?
- ICML 2020 Workshop Book
- Arxiv:1805.07883V3 [Stat.ML] 30 Jun 2019 Keywords: Convolutional Neural Networks, Recurrent Neural Networks, Sample-Complexity, Minimax Analysis
- 582: Generalization Error Bounds for Deep Unfolding Rnns
- A Case Study on Sample Complexity, Topology, and Interpolation in Neural Networks
- On the Sample Complexity of Reinforcement Learning
- Lecture 12: Reinforcement Learning
- Optimal Quantum Sample Complexity of Learning Algorithms
- Neural Network Learning: Testing Bounds on Sample Complexity
- Sample Complexity of Reinforcement Learning Using Linearly Combined Model Ensembles
- The Sample Complexity in Data(Sample)-Driven Optimization
- Introduction to Machine Learning
- Tight Sample Complexity of Learning One-Hidden-Layer Convolutional Neural Networks