S1. INTRODUCTION Traditionally, property-guided optimization has relied on expert intuition [1] and several rounds of trial, error, and human-inspired optimization, occasionally supported by computer simulations. Alternatively, computer-assisted approaches have employed virtual screening [2] or optimization algorithms such as genetic algorithms (GAs) [3-5]. More recently, with the surge of deep learning, deep generative models have emerged, specifically designed to operate in chemical space and tackle inverse molecular design [6-8]. This has led to the development of numerous algorithmic approaches for this purpose, with the most popular including variational autoencoders (VAEs) [9, 10], generative adversarial networks (GANs) [11, 12], and reinforcement learning (RL) [13, 14]. METHODSOVERVIEW In this section, we provide an overview of the molecular generative models employed throughout this work and summarize the associated design choices we needed to make during their implementation. The molecular design algorithms we considered are VAEs, long short-term memory hill climbing (LSTM-HC) models [15-17], REINVENT [18], JANUS [19], and a graph-based genetic algorithm (GB-GA) [20]. At the core of the majority of these approaches are molecular string representations, the most commonly used of which is the Simplified Molecular Input Line Entry System (SMILES) [21]. Accordingly, many of the algorithms tested rely on predicting subsequent characters from partial strings to propose structures. However, algorithms based on SMILES can regularly produce invalid strings that do not represent molecules, which is problematic both in terms of robustness and interpretability of the corresponding methodologies [22, 23]. Consequently, this issue was addressed systematically by introducing Self-Referencing Embedded Strings (SELFIES) [22], a molecular string representation that guarantees validity. Thus, unlike for SMILES, every arbitrary combination of SELFIES characters represents a molecule. Nevertheless, its impact on structure optimization has not yet been evaluated systematically [23]. To address this issue, we modify some of the existing generative models relying on SMILES to be also compatible with SELFIES and test their performance depending on representation, similar to how it has been done recently [24]. Among the models tested, REINVENT, the VAEs, and the LSTM-HC models use recurrent neural networks (RNNs) [25] to learn the conditional probability distributions of the characters that represent molecules. RNNs are a class of artificial neural networks (ANNs) that utilize sequential information from their previous predictions and states.

Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics. We introduce a model-free random search algorithm for training static, linear policies for continuous control problems. Common evaluation methodology shows that our method matches state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Nonetheless, more rigorous evaluation reveals that the assessment of performance on these benchmarks is optimistic. We evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. This extensive evaluation is possible because of the small computational footprint of our method. Our simulations highlight a high variability in performance in these benchmark tasks, indicating that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms. Our results stress the need for new baselines, benchmarks and evaluation methodology for RL algorithms.

Section A.2 shows results using a chat-style verbalized numeric Section A.3 shows results on four extra benchmark tasks made available with Finally, Section A.5 presents and discusses results on feature In this section, we evaluate risk score calibration on the income prediction task across different subpopulations, such as typically done as part of a fairness audit. Figures A1-A2 show group-conditional calibration curves for all models on the ACSIncome task, evaluated on three subgroups specified by the race attribute in the ACS data. We show the three race categories with largest representation. The'Mixtral 8x22B' and'Yi 34B' models shown are the worst offenders, where samples belonging to the'Black' population see consistently lower scores for the same positive label probability when compared to the'Asian' or'White' populations. On average, the'Mixtral 8x22B (it)' model classifies a Black individual with a In fact, this score bias can be reversed for some base models, overestimating scores from Black individuals compared with other subgroups.

Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations--an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible.

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model over-fitting.