ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES

By W. R. Thompson. 1933

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Citations in the corpus

Degenerate Feedback Loops in Recommender Systems (2019, neutral): “• Thompson Sampling Multi-armed Bandit Algorithm ( TS ) (Thompson, 1933): We initialize α 0 ( a ) = 1 , β 0 ( a ) = 1 for any new item a .”
Neural Approaches to Conversational AI (2018, positive): “information leads to a natural exploration strategy, inspired by Thompson Sampling (Thompson, 1933; Chapelle and Li, 2012; Russo et al., 2018).”
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (2015, methodology): “With uncertainty estimates over the agent’s Q-value function, techniques such as Thompson sampling (Thompson, 1933) can be used to learn much faster.”
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (2019, methodology): “The next architecture is then chosen by optimizing an acquisition function such as expected improvement (EI) (Močkus 1975) or Thompson sampling (TS) (Thompson 1933).”
Bayesian Learning of Neural Network Architectures (2019, methodology): “This iterative approach is called Thompson sampling [Thompson, 1933] and was developed as an ef-ﬁcient way to tradeoﬀ exploration for exploitation in the framework of Bayesian decision making.”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “For example, in [34] each sigmoidal layer of an L layer neural network is de-ﬁned as L (cid:96) ( x ) := σ ( W (cid:96) x + B (cid:96) ) where σ is some sigmoidal non-linearity, and W (cid:96) and B (cid:96) are the layer parameters.”
Balanced Linear Contextual Bandits (2018, methodology): “Thompson Sampling (Thompson 1933), (Scott 2010), (Agrawal and Goyal 2012), (Russo et al. 2018) and Upper Conﬁ-dence Bounds (UCB) (Lai and Robbins 1985), (Auer, Cesa-Bianchi, and Fischer 2002) are two different methods which are highly effective in dealing with the exploration vs. exploitation trade-”
Estimation Considerations in Contextual Bandits (2017, methodology): “Thompson sampling [61, 55, 5, 52] and upper confidence bound (UCB) [40, 12] are two different methods for assigning contexts to arms which are highly effective in dealing with the exploration-exploitation trade-off.”
Learning to Learn for Global Optimization of Black Box Functions (2016, neutral): “Here again we encounter a huge variety of strategies, including Thompson sampling [Thompson, 1933, Scott, 2010, Chapelle and Li, 2011, Agrawal and Goyal, 2013], information gain [Villemonteix et al., 2009, Hennig and Schuler, 2012, Russo and Van Roy, 2014, Shahriari et al., 2014, Hern ´…”
Neural Processes (2018, neutral): “D function using Thompson sampling (Thompson, 1933).”
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling (2018, background): “Thompson Sampling is a classic algorithm (Thompson, 1933) which requires only that one can sample from the posterior distribution over plausible problem instances (for example, values or rewards).”
The Uncertainty Bellman Equation and Exploration (2017, methodology): “If we had access to the true posterior distribution over the Q-values then we could take actions that lead to states with higher uncertainty by, for example, using Thompson sampling (Thompson, 1933; Strens, 2000), or constructing Q-values that are high probability upper bounds on the true Q-values a”
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? (2016, methodology): “An alternative approach, based on Thompson sampling (Thompson, 1933), involves sampling a statistically plausibly set of action values and selecting the maximizing action.”
Deep Exploration via Bootstrapped DQN (2016, background): “Perhaps the oldest heuristic for balancing exploration with exploitation is given by Thompson sampling (Thompson, 1933).”
Variational Bayesian Reinforcement Learning with Regret Bounds (2018, background): “Approximations to the optimal Bayesian policy exist, one of the most successful being Thompson sampling [53, 55] wherein the agent samples from the posterior over value functions and acts greedily with respect to that sample [38, 42, 27, 41].”