暂无分享,去创建一个
Misha Denil | Sergio Gomez Colmenarejo | Matthew W. Hoffman | Nando de Freitas | Yutian Chen | Timothy P. Lillicrap | T. Lillicrap | N. D. Freitas | Misha Denil | Yutian Chen
[1] W. R. Thompson. ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES , 1933 .
[2] Lewis B. Ward. Reminiscence and rote learning. , 1937 .
[3] H. Harlow,et al. The formation of learning sets. , 1949, Psychological review.
[4] Harold J. Kushner,et al. A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise , 1964 .
[5] E. Kehoe. A layered network model of associative learning: learning to learn and configuration. , 1988, Psychological review.
[6] J. Mockus,et al. The Bayesian approach to global optimization , 1989 .
[7] Yoshua Bengio,et al. Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[8] Richard J. Mammone,et al. Meta-neural networks that learn by learning , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[9] Jürgen Schmidhuber,et al. Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks , 1992, Neural Computation.
[10] Richard S. Sutton,et al. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta , 1992, AAAI.
[11] J. Schmidhuber,et al. A neural network that embeds its own meta-levels , 1993, IEEE International Conference on Neural Networks.
[12] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[13] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[14] Nicol N. Schraudolph,et al. Local Gain Adaptation in Stochastic Gradient Descent , 1999 .
[15] Magnus Thor Jonsson,et al. Evolution and design of distributed learning rules , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.
[16] Sepp Hochreiter,et al. Learning to Learn Using Gradient Descent , 2001, ICANN.
[17] Donald R. Jones,et al. A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..
[18] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[19] Samy Bengio,et al. On the search for new learning rules for ANNs , 1995, Neural Processing Letters.
[20] Katherine D. Kinzler,et al. Core knowledge. , 2007, Developmental science.
[21] Yoshua Bengio,et al. On the Optimization of a Synaptic Learning Rule , 2007 .
[22] Ron Kohavi,et al. Controlled experiments on the web: survey and practical guide , 2009, Data Mining and Knowledge Discovery.
[23] Eric Walter,et al. An informational approach to the global optimization of expensive-to-evaluate functions , 2006, J. Glob. Optim..
[24] Rémi Munos,et al. Pure Exploration in Multi-armed Bandits Problems , 2009, ALT.
[25] Nando de Freitas,et al. New inference strategies for solving Markov Decision Processes using reversible jump MCMC , 2009, UAI.
[26] Andreas Krause,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.
[27] Nando de Freitas,et al. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.
[28] Steven L. Scott,et al. A modern Bayesian look at the multi-armed bandit , 2010 .
[29] Alessandro Lazaric,et al. Multi-Bandit Best Arm Identification , 2011, NIPS.
[30] Lihong Li,et al. An Empirical Evaluation of Thompson Sampling , 2011, NIPS.
[31] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[32] Philipp Hennig,et al. Entropy Search for Information-Efficient Global Optimization , 2011, J. Mach. Learn. Res..
[33] Aurélien Garivier,et al. On Bayesian Upper Confidence Bounds for Bandit Problems , 2012, AISTATS.
[34] Alessandro Lazaric,et al. Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence , 2012, NIPS.
[35] Jasper Snoek,et al. Multi-Task Bayesian Optimization , 2013, NIPS.
[36] Shipra Agrawal,et al. Thompson Sampling for Contextual Bandits with Linear Payoffs , 2012, ICML.
[37] Jasper Snoek,et al. Input Warping for Bayesian Optimization of Non-Stationary Functions , 2014, ICML.
[38] Benjamin Van Roy,et al. Learning to Optimize via Posterior Sampling , 2013, Math. Oper. Res..
[39] Nando de Freitas,et al. Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters , 2014, ArXiv.
[40] Nando de Freitas,et al. Bayesian Multi-Scale Optimistic Optimization , 2014, AISTATS.
[41] Nando de Freitas,et al. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning , 2014, AISTATS.
[42] Thomas B. Schön,et al. Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models , 2015, ArXiv.
[43] Matthew W. Hoffman,et al. Predictive Entropy Search for Bayesian Optimization with Unknown Constraints , 2015, ICML.
[44] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[45] Daan Wierstra,et al. Meta-Learning with Memory-Augmented Neural Networks , 2016, ICML.
[46] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[47] Sergio Gomez Colmenarejo,et al. Hybrid computing using a neural network with dynamic external memory , 2016, Nature.
[48] Jitendra Malik,et al. Learning to Poke by Poking: Experiential Learning of Intuitive Physics , 2016, NIPS.
[49] Peter L. Bartlett,et al. RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning , 2016, ArXiv.
[50] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[51] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[52] Razvan Pascanu,et al. Learning to Navigate in Complex Environments , 2016, ICLR.
[53] T. L. Lai Andherbertrobbins. Asymptotically Efficient Adaptive Allocation Rules , 2022 .