Active Preference Learning with Discrete Choice Data

By Eric Brochu, Nando de Freitas, Abhijeet Ghosh. 2007

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “[28] E.”
A Framework for Data-Driven Robotics (2019, background): “For example, interactive learning and optimization with human preferences has been applied to animation [49]–[51].”
APRIL: Active Preference-learning based Reinforcement Learning (2012, positive): “In order for Ppl to apply the abovementioned Bayesian approaches used in interactive optimization [4] or online recommendation [30]”
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions (2014, methodology): “esian optimization has successfully been used in robotics to adjust the parameters of a robot’s controller to maximize gait speed and smoothness [16] as well as parameter tuning for computer graphics [6]. Another example application in drug discovery is to ﬁnd the chemical derivative of a particular”
Bayesian Multi-Scale Optimistic Optimization (2014, methodology): “Bayesian optimization (BO) is a popular sequential design strategy for global optimization; see Brochu et al. (2009) for an introductory treatment.”
Programming by Feedback (2014, background): “In (Brochu et al., 2007), the utility function is learned as a GP, thus providing both a utility estimate and the confidence thereof. Interestingly 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.”
Gaussian Process Preference Elicitation (2010, methodology): “[14] proposes an active preference learning method for discrete choice data.”
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (2012, background): “We refer the reader to (Močkus, 1982; Schonlau et al., 1998; Gramacy et al., 2004; Brochu et al., 2007; Lizotte, 2008; Martinez–Cantin et al., 2009; Garnett et al., 2010) for practical examples.”
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…”
Preference-Based Policy Learning (2011, unknown): “The generation of new policies relies on black-box optimization; expected-global improvement methods [6] and gradient methods [20] are not applicable since the policy return estimate is not defined on the parametric representation space Θ.”
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making (2018, methodology): “tter points per query, which explains the superior performance of ranking queries. 5.3 Results: Query Types In this section, we compare our proposed elicitation strategies to the pairwise approach of [8] in terms of approximating the utility function, and attaining a high utility. Figure 5 shows, pe”
A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot (2009, background): “learning literature (Lizotte et al. 2007; Brochu et al. 2007 ).”
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].”
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014, background): “Bayesian optimization has enjoyed success in a broad range of optimization tasks; see the work of Brochu et al. [2010b] for a broad overview.”