Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach.

Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAGs) from observational data. However, the intricate acyclicity constraint still challenges the efficient exploration of the vast space of DAGs in existing methods. In this study, we introduce ALIAS (reinforced dAg Learning wIthout Acyclicity conStraints), a novel approach to causal discovery powered by the RL machinery. Our method features an efficient policy for generating DAGs in just a single step with an optimal quadratic complexity, fueled by a novel parametrization of DAGs that directly translates a continuous space to the space of all DAGs, bypassing the need for explicitly enforcing acyclicity constraints. This approach enables us to navigate the search space more effectively by utilizing policy gradient methods and established scoring functions. In addition, we provide compelling empirical evidence for the strong performance of ALIAS in comparison with state-of-the-arts in causal discovery over increasingly difficult experiment conditions on both synthetic and real datasets.

Density deconvolution addresses the estimation of the unknown (probability) density function $f$ of a random signal from data that are observed with an independent additive random noise. This is a classical problem in statistics, for which frequentist and Bayesian nonparametric approaches are available to deal with static or batch data. In this paper, we consider the problem of density deconvolution in a streaming or online setting where noisy data arrive progressively, with no predetermined sample size, and we develop a sequential nonparametric approach to estimate $f$. By relying on a quasi-Bayesian sequential approach, often referred to as Newton's algorithm, we obtain estimates of $f$ that are of easy evaluation, computationally efficient, and with a computational cost that remains constant as the amount of data increases, which is critical in the streaming setting. Large sample asymptotic properties of the proposed estimates are studied, yielding provable guarantees with respect to the estimation of $f$ at a point (local) and on an interval (uniform). In particular, we establish local and uniform central limit theorems, providing corresponding asymptotic credible intervals and bands. We validate empirically our methods on synthetic and real data, by considering the common setting of Laplace and Gaussian noise distributions, and make a comparison with respect to the kernel-based approach and a Bayesian nonparametric approach with a Dirichlet process mixture prior.

Interpretability research aims to provide a human-understandable explanation for model outputs and behaviors based on the input and model's internal structure [Doshi-Velez and Kim, 2017]. The field's goal is to generate mechanistic explanations of how neural networks perform computations and produce behaviors [Nanda et al., 2023, Olsson et al., 2022], which could help predict the behavior of such networks across a wide range of scenarios and possibly solve notable problems of AI systems, such as hallucination and toxic output [Ji et al., 2023]. Being able to interpret AI systems is therefore a key capability to be able to understand whether models are appropriately fair, reliable, robust, and worthy of user trust [Doshi-Velez and Kim, 2017]. However, understanding the computations of frontier AI systems with hundreds of billions of parameters presents many technical challenges, from the curse of dimensionality [Zhao et al., 2024, Altman and Krzywinski, 2018] to finding a suitable unit of analysis [Olah et al., 2020, Zou et al., 2023]. These challenges are par for the course when studying complex systems. In particular, many challenges around artificial neural networks (ANN) interpretability are intimately familiar to another group of researchers: neuroscientists. Neuroscience (often in partnership with cognitive science and psychology) investigates how neurons, their connections, and their activity patterns give rise to cognition and behavior. Similar to how deep learning researchers have recognized, neuroscientists have realized that simply examining activity profiles of individual neurons in response to a particular input is often insufficient for understanding how the system performs computation. Instead, complex neural systems are best understood across multiple levels of analysis - considering behavior alongside the brain's connectome, population codes, and codes of single neurons to gain a holistic understanding of the inner workings of the brain

Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and resilience to unseen or out-of-distribution inputs over their non- Bayesian counterparts, their practical use has faded to near insignificance. In this study, we introduce an innovative framework to mitigate the computational burden of Bayesian neural networks (BNNs). Our approach follows the principle of Bayesian techniques based on deep ensembles, but significantly reduces their cost via multiple low-rank perturbations of parameters arising from a pre-trained neural network. Both vanilla version of ensembles as well as more sophisticated schemes such as Bayesian learning with Stein Variational Gradient Descent (SVGD), previously deemed impractical for large models, can be seamlessly implemented within the proposed framework, called Bayesian Low-Rank LeArning (Bella). In a nutshell, i) Bella achieves a dramatic reduction in the number of trainable parameters required to approximate a Bayesian posterior; and ii) it not only maintains, but in some instances, surpasses the performance of conventional Bayesian learning methods and non-Bayesian baselines. Our results with large-scale tasks such as ImageNet, CAMELYON17, DomainNet, VQA with CLIP, LLaVA demonstrate the effectiveness and versatility of Bella in building highly scalable and practical Bayesian deep models for real-world applications.

A long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating a Recurrent Neural Network (RNN) to handle non-linear dynamics. Loss-based generalized Bayesian inference with Sequential Monte Carlo is employed for model estimation and sequential prediction in RNN HAR. The empirical analysis is conducted using daily closing prices and realized measures from 2000 to 2022 across 31 market indices. The proposed models one step ahead VaR forecasting performance is compared against a basic HAR model and its extensions. The results demonstrate that the proposed RNN-HAR model consistently outperforms all other models considered in the study.

Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these designers use A/B experiments to evaluate each intervention; however, it is costly and time consuming to run such studies. To address this issue, we explore how computational models of learning might support designers in reasoning causally about alternative interventions within a fractions tutor. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Next, we conduct simulations to generate counterfactual predictions of performance and learning for two students (high and low performing) in different versions of the fractions tutor. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments.

Gibbs sampling is one of the most commonly used Markov Chain Monte Carlo (MCMC) algorithms due to its simplicity and efficiency. It cycles through the latent variables, sampling each one from its distribution conditional on the current values of all the other variables. Conventional Gibbs sampling is based on the systematic scan (with a deterministic order of variables). In contrast, in recent years, Gibbs sampling with random scan has shown its advantage in some scenarios. However, almost all the analyses of Gibbs sampling with the random scan are based on uniform selection of variables. In this paper, we focus on a random scan Gibbs sampling method that selects each latent variable non-uniformly. Firstly, we show that this non-uniform scan Gibbs sampling leaves the target posterior distribution invariant. Then we explore how to determine the selection probability for latent variables. In particular, we construct an objective as a function of the selection probability and solve the constrained optimization problem. We further derive an analytic solution of the selection probability, which can be estimated easily. Our algorithm relies on the simple intuition that choosing the variable updates according to their marginal probabilities enhances the mixing time of the Markov chain. Finally, we validate the effectiveness of the proposed Gibbs sampler by conducting a set of experiments on real-world applications.

Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the iid case and in the non-iid case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.

We present a probabilistic model for stochastic iterative algorithms with the use case of optimization algorithms in mind. Based on this model, we present PAC-Bayesian generalization bounds for functions that are defined on the trajectory of the learned algorithm, for example, the expected (non-asymptotic) convergence rate and the expected time to reach the stopping criterion. Thus, not only does this model allow for learning stochastic algorithms based on their empirical performance, it also yields results about their actual convergence rate and their actual convergence time. We stress that, since the model is valid in a more general setting than learning-to-optimize, it is of interest for other fields of application, too. Finally, we conduct five practically relevant experiments, showing the validity of our claims.