Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full justification that may limit their performance. For example, the theory in prior work uses stateless (i.e., history) functions, while the practical implementations use state information -- making the motivating theory a mismatch for the implementation. Also, methods have built off of previous approaches, inheriting their architectures without exploring other, potentially better ones. To address these concerns, we formally analyze the theory of using the state instead of the history in current methods -- reconnecting theory and practice. We then introduce DuelMIX, a factorization algorithm that learns distinct per-agent utility estimators to improve performance and achieve full expressiveness. Experiments on StarCraft II micromanagement and Box Pushing tasks demonstrate the benefits of our intuitions.

We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr\"odinger equation in configuration space. Traditional Configuration Interaction (CI) methods, while powerful, are computationally expensive due to the large number of determinants required. Our approach leverages RBMs to efficiently identify and sample the most significant determinants, accelerating convergence and reducing computational cost. This method achieves up to 99.99\% of the correlation energy even by four orders of magnitude less determinants compared to full CI calculations and up to two orders of magnitude less than previous state of the art works. Additionally, our study demonstrate that the RBM can learn the underlying quantum properties, providing more detail insights than other methods . This innovative data-driven approach offers a promising tool for quantum chemistry, enhancing both efficiency and understanding of complex systems.

This research aims to propose and evaluate a novel model named K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART) for improved estimation of Average Treatment Effects (ATE) and Conditional Average Treatment Effects (CATE). The study employs synthetic and semi-synthetic datasets, including the widely recognized Infant Health and Development Program (IHDP) benchmark dataset, to validate the model's performance. Despite promising results in synthetic scenarios, the IHDP dataset reveals that the proposed model is not state-of-the-art for ATE and CATE estimation. Nonetheless, the research provides several novel insights: 1. The ps-BART model is likely the preferred choice for CATE and ATE estimation due to better generalization compared to the other benchmark models - including the Bayesian Causal Forest (BCF) model, which is considered by many the current best model for CATE estimation, 2. The BCF model's performance deteriorates significantly with increasing treatment effect heterogeneity, while the ps-BART model remains robust, 3. Models tend to be overconfident in CATE uncertainty quantification when treatment effect heterogeneity is low, 4. A second K-Fold method is unnecessary for avoiding overfitting in CATE estimation, as it adds computational costs without improving performance, 5. Detailed analysis reveals the importance of understanding dataset characteristics and using nuanced evaluation methods, 6. The conclusion of Curth et al. (2021) that indirect strategies for CATE estimation are superior for the IHDP dataset is contradicted by the results of this research. These findings challenge existing assumptions and suggest directions for future research to enhance causal inference methodologies.

This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangeable setting. We frame policy optimization as maximum likelihood estimation in a non-Markovian state-space model, achieving (at most) $\mathcal{O}(T^2)$ computational complexity in the number of experiments. We provide theoretical convergence guarantees and introduce a backward sampling algorithm to reduce trajectory degeneracy. IO-NPF offers a practical, extensible, and provably consistent approach to sequential Bayesian experimental design, demonstrating improved efficiency over existing methods.

The development of autonomous vehicles has shown great potential to enhance the efficiency and safety of transportation systems. However, the decision-making issue in complex human-machine mixed traffic scenarios, such as unsignalized intersections, remains a challenge for autonomous vehicles. While reinforcement learning (RL) has been used to solve complex decision-making problems, existing RL methods still have limitations in dealing with cooperative decision-making of multiple connected autonomous vehicles (CAVs), ensuring safety during exploration, and simulating realistic human driver behaviors. In this paper, a novel and efficient algorithm, Multi-Agent Game-prior Attention Deep Deterministic Policy Gradient (MA-GA-DDPG), is proposed to address these limitations. Our proposed algorithm formulates the decision-making problem of CAVs at unsignalized intersections as a decentralized multi-agent reinforcement learning problem and incorporates an attention mechanism to capture interaction dependencies between ego CAV and other agents. The attention weights between the ego vehicle and other agents are then used to screen interaction objects and obtain prior hierarchical game relations, based on which a safety inspector module is designed to improve the traffic safety. Furthermore, both simulation and hardware-in-the-loop experiments were conducted, demonstrating that our method outperforms other baseline approaches in terms of driving safety, efficiency, and comfort.

Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When coping with such models, keeping a bound on the number of parameters might be important for multiple reasons. First, as these models are typically based on expert knowledge, a huge number of parameters to elicit might discourage practitioners from adopting them. Moreover, the number of model parameters affects the complexity of the inferences, while a fast computation of the queries is needed for real-time feedback. We advocate logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems. We discuss the semantics of the model parameters to elicit and the assumptions required to apply such approach in this domain. We also derive a dedicated inference scheme to speed up computations.

Outlier detection (OD) has a vast literature as it finds numerous applications in environmental monitoring, cybersecurity, finance, and medicine to name a few. Being an inherently unsupervised task, model selection is a key bottleneck for OD (both algorithm and hyperparameter selection) without label supervision. There is a long list of techniques to choose from -- both classical algorithms and deep neural architectures -- and while several studies report their hyperparameter sensitivity, the literature is quite slim on unsupervised model selection -- limiting the effective use of OD in practice. In this paper we present FoMo-0D, for zero/0-shot OD exploring a transformative new direction that bypasses the hurdle of model selection altogether (!), thus breaking new ground. The fundamental idea behind FoMo-0D is the Prior-data Fitted Networks, recently introduced by Muller et al.(2022), which trains a Transformer model on a large body of synthetically generated data from a prior data distribution. In essence, FoMo-0D is a pretrained Foundation Model for zero/0-shot OD on tabular data, which can directly predict the (outlier/inlier) label of any test data at inference time, by merely a single forward pass -- making obsolete the need for choosing an algorithm/architecture, tuning its associated hyperparameters, and even training any model parameters when given a new OD dataset. Extensive experiments on 57 public benchmark datasets against 26 baseline methods show that FoMo-0D performs statistically no different from the top 2nd baseline, while significantly outperforming the majority of the baselines, with an average inference time of 7.7 ms per test sample.

In the Naive Bayes classification model the class conditional densities are estimated as the products of their marginal densities along the cardinal basis directions. We study the problem of obtaining an alternative basis for this factorisation with the objective of enhancing the discriminatory power of the associated classification model. We formulate the problem as a projection pursuit to find the optimal linear projection on which to perform classification. Optimality is determined based on the multinomial likelihood within which probabilities are estimated using the Naive Bayes factorisation of the projected data. Projection pursuit offers the added benefits of dimension reduction and visualisation. We discuss an intuitive connection with class conditional independent components analysis, and show how this is realised visually in practical applications. The performance of the resulting classification models is investigated using a large collection of (162) publicly available benchmark data sets and in comparison with relevant alternatives. We find that the proposed approach substantially outperforms other popular probabilistic discriminant analysis models and is highly competitive with Support Vector Machines.

Machine learning (ML) has seen exponential growth in recent years, finding applications in various domains such as finance, medicine, and cybersecurity. Malware remains a significant threat to modern computing, frequently used by attackers to compromise systems. While numerous machine learning-based approaches for malware detection achieve high performance, they often lack transparency and fail to explain their predictions. This is a critical drawback in malware analysis, where understanding the rationale behind detections is essential for security analysts to verify and disseminate information. Explainable AI (XAI) addresses this issue by maintaining high accuracy while producing models that provide clear, understandable explanations for their decisions. In this survey, we comprehensively review the current state-of-the-art ML-based malware detection techniques and popular XAI approaches. Additionally, we discuss research implementations and the challenges of explainable malware analysis. This theoretical survey serves as an entry point for researchers interested in XAI applications in malware detection. By analyzing recent advancements in explainable malware analysis, we offer a broad overview of the progress in this field, positioning our work as the first to extensively cover XAI methods for malware classification and detection.

Probability estimation of tree topologies is one of the fundamental tasks in phylogenetic inference. The recently proposed subsplit Bayesian networks (SBNs) provide a powerful probabilistic graphical model for tree topology probability estimation by properly leveraging the hierarchical structure of phylogenetic trees. However, the expectation maximization (EM) method currently used for learning SBN parameters does not scale up to large data sets. In this paper, we introduce several computationally efficient methods for training SBNs and show that variance reduction could be the key for better performance. Furthermore, we also introduce the variance reduction technique to improve the optimization of SBN parameters for variational Bayesian phylogenetic inference (VBPI). Extensive synthetic and real data experiments demonstrate that our methods outperform previous baseline methods on the tasks of tree topology probability estimation as well as Bayesian phylogenetic inference using SBNs.