Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that would address this problem. We present MolecularRNN, the graph recurrent generative model for molecular structures. Our model generates diverse realistic molecular graphs after likelihood pretraining on a big database of molecules. We perform an analysis of our pretrained models on large-scale generated datasets of 1 million samples. Further, the model is tuned with policy gradient algorithm, provided a critic that estimates the reward for the property of interest. We show a significant distribution shift to the desired range for lipophilicity, drug-likeness, and melting point outperforming state-of-the-art works. With the use of rejection sampling based on valency constraints, our model yields 100% validity. Moreover, we show that invalid molecules provide a rich signal to the model through the use of structure penalty in our reinforcement learning pipeline.Preprint. Under review.

In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function f . Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of f in a small but promising region and speedily identify the optimum. We formalise this task as a multi-fidelity bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour and achieves better bounds on the regret than strategies which ignore multi-fidelity information. Empirically, MF-GP-UCB outperforms such naive strategies and other multi-fidelity methods on several synthetic and real experiments. KANDASAMY, DASARATHY, OLIVA, SCHNEIDER, PÓCZOS n=300 n=3000 1. We present a formalism for multi-fidelity bandit optimisation using Gaussian process (GP) assumptions on f and its approximations. We develop a novel algorithm, Multi-Fidelity Gaussian Process Upper Confidence Bound (MF-GP-UCB) for this setting.2. Our theoretical analysis proves that MF-GP-UCB explores the space X at lower fidelities and uses the high fidelities in successively smaller regions to converge on the optimum. As lower fidelity queries are cheaper, MF-GP-UCB has better upper bounds on the regret than single fidelity strategies which have to rely on the expensive function to explore the entire space.3. We demonstrate that MF-GP-UCB outperforms single fidelity methods and other alternatives empirically, via a series of synthetic examples, three hyper-parameter tuning tasks and one inference problem in astrophysics. Our matlab implementation and experiments are available at github.com/kirthevasank/mf-gp-ucb. Related WorkSince the seminal work by Robbins [1952], the multi-armed bandit problem has been studied extensively in the K-armed setting. Recently, there has been a surge of interest in the optimism under uncertainty principle for K-armed bandits, typified by upper confidence bound (UCB) methods Cesa-Bianchi, 2012, Auer, 2003]. UCB strategies have also been used in bandit

A grand challenge of the 21 st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach in estimating the cosmological parameters is to use the large scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in three dimensions. Information about galaxy locations is typically summarized in a "single" function of scale, such as the galaxy correlation function or powerspectrum. We show that it is possible to estimate these cosmological parameters directly from the distribution of matter. This paper presents the application of deep 3D convolutional networks to volumetric representation of dark-matter simulations as well as the results obtained using a recently proposed distribution regression framework, showing that machine learning techniques are comparable to, and can sometimes outperform, maximum-likelihood point estimates using "cosmological models". This opens the way to estimating the parameters of our Universe with higher accuracy.

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We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation. MC expands on the modelagnostic meta-learner (MAML) by learning to transform the gradients in the inner optimization such that the transformed gradients achieve better generalization performance to a new task. For training large scale neural networks, we decompose the curvature matrix into smaller matrices in a novel scheme where we capture the dependencies of the model's parameters with a series of tensor products. We demonstrate the effects of our proposed method on several few-shot learning tasks and datasets. Without any task specific techniques and architectures, the proposed method achieves substantial improvement upon previous MAML variants and outperforms the recent state-of-the-art methods. Furthermore, we observe faster convergence rates of the meta-training process. Finally, we present an analysis that explains better generalization performance with the meta-trained curvature.