The recently proposed Differentiable AIS (DAIS) (Geffner & Domke, 2021; Zhang et al., 2021) enables efficient optimization of the transition kernels of AIS and of the distributions. However, we observe a low effective sample size in DAIS, indicating degenerate distributions. We thus propose to extend DAIS by a resampling step inspired by Sequential Monte Carlo. Surprisingly, we find empirically--and can explain theoretically--that it is not necessary to differentiate through the resampling step, which avoids gradient variance issues observed in similar approaches for Particle Filters (Maddison et al., 2017a; Naesseth et al., 2018; Le et al., 2018). Figure 1: ESS for DAIS and Related Work Differentiable PFs construct a lower bound on the DSMCS at epochs 100 and log marginal likelihood utilizing the filtering distribution (e.g.

Latent Class Models (LCMs) are used to cluster multivariate categorical data (e.g. group participants based on survey responses). Traditional LCMs assume a property called conditional independence. This assumption can be restrictive, leading to model misspecification and overparameterization. To combat this problem, we developed a novel Bayesian model called a Dependent Latent Class Model (DLCM), which permits conditional dependence. We verify identifiability of DLCMs. We also demonstrate the effectiveness of DLCMs in both simulations and real-world applications. Compared to traditional LCMs, DLCMs are effective in applications with time series, overlapping items, and structural zeroes.

We present a computationally-efficient strategy to initialise the hyperparameters of a Gaussian process (GP) avoiding the computation of the likelihood function. Our strategy can be used as a pretraining stage to find initial conditions for maximum-likelihood (ML) training, or as a standalone method to compute hyperparameters values to be plugged in directly into the GP model. Motivated by the fact that training a GP via ML is equivalent (on average) to minimising the KL-divergence between the true and learnt model, we set to explore different metrics/divergences among GPs that are computationally inexpensive and provide hyperparameter values that are close to those found via ML. In practice, we identify the GP hyperparameters by projecting the empirical covariance or (Fourier) power spectrum onto a parametric family, thus proposing and studying various measures of discrepancy operating on the temporal and frequency domains. Our contribution extends the variogram method developed by the geostatistics literature and, accordingly, it is referred to as the generalised variogram method (GVM). In addition to the theoretical presentation of GVM, we provide experimental validation in terms of accuracy, consistency with ML and computational complexity for different kernels using synthetic and real-world data.

We seek to understand fundamental tradeoffs between the accuracy of prior information that a learner has on a given problem and its learning performance. We introduce the notion of prioritized risk, which differs from traditional notions of minimax and Bayes risk by allowing us to study such fundamental tradeoffs in settings where reality does not necessarily conform to the learner's prior. We present a general reduction-based approach for extending classical minimax lower-bound techniques in order to lower bound the prioritized risk for statistical estimation problems. We also introduce a novel generalization of Fano's inequality (which may be of independent interest) for lower bounding the prioritized risk in more general settings involving unbounded losses. We illustrate the ability of our framework to provide insights into tradeoffs between prior information and learning performance for problems in estimation, regression, and reinforcement learning.

Demand for battery systems is increasing rapidly as efforts Prognosis (i.e., future prediction) in this framework is to decarbonise electricity grids and electrify mobility gather achieved using a separate model for the evolution of parameters pace [1]. Due to their long lifetime and high energy density, over battery lifetime, and this can range from a random Li-ion cells have become the workhorse in battery systems walk [8]-[10] to semi-empirical curve fits of trajectories that [2]. Although the cost of these has dramatically decreased in may be re-parameterised over lifetime using adaptive methods the last decade [3], the economics of storage needs to further such as particle filtering [13], [14], a Bayesian approach improve to increase take-up, notably in applications where that also provides parameter uncertainty estimates. Modeldriven battery systems are not yet competitive in terms of levelized approaches tend to use rather simple equivalent-circuit cost [4]. Also, given the risks of Li-ion cell demand outpacing models because they have relatively few parameters that need the supply of the required raw materials [5], it is crucial that to be fitted, whereas parameterising physics-based models, the performance of existing systems, especially in terms of such as those within the Doyle-Fuller-Newman framework lifetime, is maximised. A key element in improving the overall [15], [16], is plagued by poor identifiability [17]. This is cost-effectiveness of Li-ion batteries is accurate estimation mainly due to a lack of reference electrodes in commercial and prediction of battery state-of-health (SOH), which can cells which means that decoupling the positive and negative improve lifetime, warranty and insurance costs, system safety half-cell potentials is very difficult.

Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) require a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time-consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, we present a significant augmentation of the current models used in simulators for driver behavior. In this paper, we present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. In addition, we decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human-interpretable system that can be tuned to represent different classes of drivers. Additionally, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of various human-specific and system-specific factors, studying their effect on traffic network performance and safety.

The need for fully human-understandable models is increasingly being recognised as a central theme in AI research. The acceptance of AI models to assist in decision making in sensitive domains will grow when these models are interpretable, and this trend towards interpretable models will be amplified by upcoming regulations. One of the killer applications of interpretable AI is medical practice, which can benefit from accurate decision support methodologies that inherently generate trust. In this work, we propose FPT, (MedFP), a novel method that combines probabilistic trees and fuzzy logic to assist clinical practice. This approach is fully interpretable as it allows clinicians to generate, control and verify the entire diagnosis procedure; one of the methodology's strength is the capability to decrease the frequency of misdiagnoses by providing an estimate of uncertainties and counterfactuals. Our approach is applied as a proof-of-concept to two real medical scenarios: classifying malignant thyroid nodules and predicting the risk of progression in chronic kidney disease patients. Our results show that probabilistic fuzzy decision trees can provide interpretable support to clinicians, furthermore, introducing fuzzy variables into the probabilistic model brings significant nuances that are lost when using the crisp thresholds set by traditional probabilistic decision trees. We show that FPT and its predictions can assist clinical practice in an intuitive manner, with the use of a user-friendly interface specifically designed for this purpose. Moreover, we discuss the interpretability of the FPT model.

We propose TR0N, a highly general framework to turn pre-trained unconditional generative models, such as GANs and VAEs, into conditional models. The conditioning can be highly arbitrary, and requires only a pre-trained auxiliary model. For example, we show how to turn unconditional models into class-conditional ones with the help of a classifier, and also into text-to-image models by leveraging CLIP. TR0N learns a lightweight stochastic mapping which "translates" between the space of conditions and the latent space of the generative model, in such a way that the generated latent corresponds to a data sample satisfying the desired condition. The translated latent samples are then further improved upon through Langevin dynamics, enabling us to obtain higher-quality data samples. TR0N requires no training data nor fine-tuning, yet can achieve a zero-shot FID of 10.9 on MS-COCO, outperforming competing alternatives not only on this metric, but also in sampling speed -- all while retaining a much higher level of generality. Our code is available at https://github.com/layer6ai-labs/tr0n.

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety.

For example, the total sales of a product in a country can be divided into regions and then into sub-regions. Forecasts of hierarchical time series should be coherent; for instance, the sum of the forecasts of the different regions should be equal to the forecast for the entire country. Forecasts are incoherent if they do not satisfy such constraints. Reconciliation methods [13, 31] compute coherent forecasts by combining the base forecasts generated independently for each time series, possibly incorporating non-negativity constraints [32]. Reconciled forecasts are generally more accurate than the base forecasts; indeed, forecast reconciliation is related to forecast combination [9, 6]. A special case of reconciliation is constituted by temporal hierarchies [1], which reconcile base forecasts computed for the same variable at different frequencies (e.g., monthly, quarterly and yearly); they generally improve the forecasts [19] of smooth and intermittent time series. As for probabilistic reconciliation, [25] proposed a seminal framework which interprets reconciliation as a projection.