A Bayesian interactive optimization approach to procedural animation design

By Eric Brochu, Tyson Brochu, Nando de Freitas. 2010

199 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

Deep Reinforcement Learning from Human Preferences (2017, background): “, 2014), and optimizing using human preferences in settings other than reinforcement learning (Machwe and Parmee, 2006; Secretan et al., 2008; Brochu et al., 2010; Sørensen et al., 2016).”
Scalable Bayesian Optimization Using Deep Neural Networks (2015, background): “For an in-depth review, see Lizotte (2008), Brochu et al. (2010) and Os-borne et al. (2009).”
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces (2014, neutral): “1 Introduction Bayesian optimization ( BO) is an efficient approach for solving blackbox optimization problems of the formargminx∈X f(x) (see [1] for a detailed overview), where f is expensive to evaluate.”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “A typical modelling choice is to use conjugacy to match the prior and likelihood so that the posterior (and often the normalizing constant) can be computed analytically.”
Input Warping for Bayesian Optimization of Non-Stationary Functions (2014, background): “Recent work in machine learning has revisited the idea of Bayesian optimization (e.g., Osborne et al., 2009; Brochu et al., 2010; Srinivas et al., 2010; Hutter et al., 2011; Bergstra et al., 2011; Bull, 2011; Snoek et al., 2012; Hennig and Schuler, 2012) in large part due to advances in the ability ”
Bayesian Optimization with Unknown Constraints (2014, methodology): “Following our formulation of the problem, we use the minimum expected value of the objective such that the probabilistic constraints are satisﬁed (see Section 1.5, Eq., 6).”
Sequential line search for efficient visual design optimization by crowds (2017, methodology): “Bayesian optimization is capable of taking such domain knowledge into account by incorporating them into the Gaussian process prior ( e.g., [Brochu et al. 2010a]).”
Programming by Feedback (2014, methodology): “Inter-estingly however, Brochu et al. (2010) note that ILO being a ”small data“ problem, does not provide enough evidence to accurately tune the GP hyper-parameters.”
Active learning of intuitive control knobs for synthesizers using gaussian processes (2014, methodology): “As in [19] and [2], our system learns in an iterative manner, and at each iteration we query the user with the point that maximizes the tool’s expected utility.”
An Entropy Search Portfolio for Bayesian Optimization (2014, methodology): “Major applications of these techniques include interactive user-interfaces [6, 4], robotics [18, 21], environmental monitoring [20], information extraction [33], combinatorial optimization [16, 34], sensor networks [10, 28], adaptive Monte Carlo [19, 32], experimental design [1], and reinforcement…”
Contextual Gaussian Process Bandit Optimization (2011, background): “For example, [27] consider sharing information across multiple sessions in a problem of parameter identification in animation design.”
Portfolio Allocation for Bayesian Optimization (2010, background): “Examples can be found in robot gait design [26], online path planning [28, 29], intelligent user interfaces for animation [6, 4], algorithm configuration [20], efficient MCMC [34], sensor placement [38, 33], and reinforcement learning [5].”
Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019, background): “preferences dates back to works at the interface of machine learning and graphics [10, 11].”
A Stratified Analysis of Bayesian Optimization Methods (2016, unknown): “Bayesian optimization is a powerful tool for optimizing objective functions which are very costly or slow to evaluate (Martinez-Cantin et al., 2007; Brochu et al., 2010; Snoek et al., 2012).”
Heteroscedastic Treed Bayesian Optimisation (2014, methodology): “Major applications of these techniques include interactive user interfaces (Brochu et al., 2010) robotics (Lizotte, 2008; Martinez–Cantin et al.”
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014, background): “…such as MCMC inference [Mahendran et al., 2012, Hamze et al., 2013, Wang et al., 2013a], deep learning [Bergstra et al., 2011], preference learning [Brochu et al., 2007, 2010a], reinforcement learning and control [Martinez-Cantin et al., 2007, Lizotte et al., 2012], and more [Snoek et al., 2012].”