Oracle inequalities for computationally budgeted model selection

By Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard. 2011

34 citations recorded by the source corpus.

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Citations in the corpus

DeepArchitect: Automatically Designing and Training Deep Architectures (2017, background): “There has been a wide variety of theoretical and empirical research in this area [2, 4, 5, 21], including Bayesian optimization methods [12, 14, 24].”
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (2016, methodology): “Domhan et al. (2015); Swersky et al. (2013, 2014); Gy¨orgy and Kocsis (2011); Agarwal et al. (2011) propose methods that make parametric assumptions on the convergence behavior of training algorithms, providing theoretical performance guarantees under these assumptions.”
Population Based Training of Neural Networks (2017, methodology): “…of optimisation steps required for each optimisation process and to better explore the space of promising hyperparameters (Gy¨orgy & Kocsis, 2011; Agarwal et al., 2011; Sabharwal et al., 2016; Swersky et al., 2013, 2014; Domhan et al., 2015; Klein et al., 2016; Snoek et al., 2015; Springenberg…”
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012, neutral): “Another direction for nonlinear stochastic bandits was recently investigated in [Agarwal et al., 2011b].”
Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017, background): “Recent work has studied multi-ﬁdelity meth-ods for speciﬁc problems such as hyper-parameter tuning, active learning and reinforcement learning (Agarwal et al., 2011; Cutler et al., 2014; Li et al., 2016; Sabharwal et al., 2015; Zhang & Chaudhuri, 2015).”
A System for Massively Parallel Hyperparameter Tuning (2018, methodology): “Previous methods like Gy¨orgy & Kocsis (2011); Agarwal et al. (2011); Sabharwal et al. (2016) provide theoretical guarantees under strong assumptions on the convergence behavior of intermediate losses.”
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits (2016, background): “, 2015; Agarwal et al., 2011; Krueger et al., 2015). With the exception of Krueger et al. (2015), these methods also assume that the underlying learning methods are iterative in nature, and require access to intermediate results.”
Structured Prediction Cascades (2010, background): “In more recent theoretical work, Agarwal et al. (2011) also address the issue of estimation time by incorporating computational constraints into the classical empirical risk minimization framework.”
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations (2016, background): “Some literature have analysed multi-ﬁdelity methods in speciﬁc contexts such as hyper-parameter tuning, active learning and reinforcement learning [1, 5, 26, 33].”
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization (2016, methodology): “Several works propose methods that make strong assumptions on the convergence behavior of training algorithms, providing theoretical performance guarantees under these assumptions (Gy¨orgy & Kocsis, 2011; Agarwal et al., 2011; Swersky et al., 2013; 2014; Domhan et al., 2015; Sabharwal et al., 2016).”
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions (2018, background): “Multi-fidelity optimization has had a rich history in many settings (Agarwal et al., 2011; Forrester et al., 2007; Huang et al., 2006a; Klein et al., 2016; Lam et al., 2015; Li et al., 2016; Poloczek et al., 2016; Sabharwal et al., 2015; Zhang & Chaudhuri, 2015), with those that are not applications”