optimisation
No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation
Black-box optimisation is one of the important areas in optimisation.
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Optimistic Meta-Gradients
We study the connection between gradient-based meta-learning and convex optimisation. We observe that gradient descent with momentum is a special case of meta-gradients, and building on recent results in optimisation, we prove convergence rates for meta-learning in the single task setting.
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A loss framework for calibrated anomaly detection
Neural Information Processing Systems http://nips.cc/
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Sample-efficient Bayesian Optimisation Using Known Invariances Theodore Brown
Bayesian optimisation (BO) is a powerful framework for global optimisation of costly functions, using predictions from Gaussian process models (GPs).
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- Research Report > Experimental Study (0.93)
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Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions
Chris Russell, Matteo Toso, Neill Campbell
Neural Information Processing Systems http://nips.cc/
Multi-objective Bayesian optimisation with preferences over objectives
Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/
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- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.68)
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Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Gregory Farquhar, Shimon Whiteson, Jakob Foerster
Neural Information Processing Systems http://nips.cc/
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DeepSetPredictionNetworks
Concretely,wecontributethefollowing: 1. Wepropose amodel (section 3, Algorithm 1) that can predict aset from afeature vector (vector-to-set) while properly taking the structure of sets into account.
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