Bayesian Optimization
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that doesn’t require derivatives. Since the objective function is unknown, the Bayesian strategy is to treat it as a random function and place a prior over it. The prior captures our beliefs about the behaviour of the function. After gathering the function evaluations, which are treated as data, the prior is updated to form the posterior distribution over the objective function. The posterior distribution, in turn, is used to construct an acquisition function (often also referred to as infill sampling criteria) that determines what the next query point should be. …

Sufficient-Component Cause Model (SCC)
In 1976 Ken Rothman, who is a member of the epidemiology faculty at BUSPH, proposed a conceptual model of causation known as the ‘sufficient-component cause model’ in an attempt to provide a practical view of causation which also had a sound theoretical basis. The model has similarities to the ‘web of causation’ theory described above, but is more developed in the sense that it simultaneously provides a general model for the conditions necessary to cause (and prevent) disease in a single individual and for the epidemiological study of the causes of disease among groups of individuals.
The Sufficient-Component Cause Model …

Deep group-feature selection using Knockoff (Deep-gKnock)
Feature selection is central to contemporary high-dimensional data analysis. Grouping structure among features arises naturally in various scientific problems. Many methods have been proposed to incorporate the grouping structure information into feature selection. However, these methods are normally restricted to a linear regression setting. To relax the linear constraint, we combine the deep neural networks (DNNs) with the recent Knockoffs technique, which has been successful in an individual feature selection context. We propose Deep-gKnock (Deep group-feature selection using Knockoffs) as a methodology for model interpretation and dimension reduction. Deep-gKnock performs model-free group-feature selection by controlling group-wise False Discovery Rate (gFDR). Our method improves the interpretability and reproducibility of DNNs. Experimental results on both synthetic and real data demonstrate that our method achieves superior power and accurate gFDR control compared with state-of-the-art methods. …

Deep Counterfactual Regret Minimization (Deep CFR)
Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It iteratively traverses the game tree in order to converge to a Nash equilibrium. In order to deal with extremely large games, CFR typically uses domain-specific heuristics to simplify the target game in a process known as abstraction. This simplified game is solved with tabular CFR, and its solution is mapped back to the full game. This paper introduces Deep Counterfactual Regret Minimization (Deep CFR), a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in the benchmark game of heads-up no-limit Texas hold’em poker. This is the first successful use of function approximation in CFR for large games. …