Optimizing Data Collection for Machine Learning

Over the last few decades, first-order (convex) minimization methods have gained popularity for modern machine learning and optimization problems due to their efficient per-iteration cost and global convergence properties.

Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-making process which estimates the utility of query points via an acquisition function and a prior over functions, such as a Gaussian process. Recently, however, a reformulation of BO via density-ratio estimation (BORE) allowed reinterpreting the acquisition function as a probabilistic binary classifier, removing the need for an explicit prior over functions and increasing scalability. In this paper, we present a theoretical analysis of BORE's regret and

GPU-compatible package unlocks potential for new algorithms to arise.

Because the workers only communicate with few neighbors without central coordination, these updates propagate progressively over the network.

It reports adjusted scores for the items by solving a convex optimization problem. Under certain conditions, I show that the owner's optimal strategy is to honestly report the true ranking of the items to her best knowledge in order to maximize the expected utility.

Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models.

Table C.2: Summary of ground-truth clusters used in the experiments