A key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding. Here, we study this question using a segregated graph (Shpitser, 2015) representation of these mechanisms, and examine how uncertainty about the true underlying mechanism impacts downstream computation of network causal effects, particularly under full interference -- settings where we only have a single realization of a network and each unit may depend on any other unit in the network. Under certain assumptions about asymptotic growth of the network, we derive likelihood ratio tests that can be used to identify whether different sets of variables -- confounders, treatments, and outcomes -- across units exhibit dependence due to contagion or latent confounding. We then propose network causal effect estimation strategies that provide unbiased and consistent estimates if the dependence mechanisms are either known or correctly inferred using our proposed tests. Together, the proposed methods allow network effect estimation in a wider range of full interference scenarios that have not been considered in prior work. We evaluate the effectiveness of our methods with synthetic data and the validity of our assumptions using real-world networks.

In today's data-intensive landscape, where high-dimensional datasets are increasingly common, reducing the number of input features is essential to prevent overfitting and improve model accuracy. Despite numerous efforts to tackle dimensionality reduction, most approaches apply a universal set of features across all classes, potentially missing the unique characteristics of individual classes. This paper presents the Explainable Class-Specific Naive Bayes (XNB) classifier, which introduces two critical innovations: 1) the use of Kernel Density Estimation to calculate posterior probabilities, allowing for a more accurate and flexible estimation process, and 2) the selection of class-specific feature subsets, ensuring that only the most relevant variables for each class are utilized. Extensive empirical analysis on high-dimensional genomic datasets shows that XNB matches the classification performance of traditional Naive Bayes while drastically improving model interpretability. By isolating the most relevant features for each class, XNB not only reduces the feature set to a minimal, distinct subset for each class but also provides deeper insights into how the model makes predictions. This approach offers significant advantages in fields where both precision and explainability are critical.

In-context learning (ICL) is a powerful technique for getting language models to perform complex tasks with no training updates. Prior work has established strong correlations between the number of in-context examples provided and the accuracy of the model's predictions. In this paper, we seek to explain this correlation by showing that ICL approximates a Bayesian learner. This perspective gives rise to a family of novel Bayesian scaling laws for ICL. In experiments with \mbox{GPT-2} models of different sizes, our scaling laws exceed or match existing scaling laws in accuracy while also offering interpretable terms for task priors, learning efficiency, and per-example probabilities. To illustrate the analytic power that such interpretable scaling laws provide, we report on controlled synthetic dataset experiments designed to inform real-world studies of safety alignment. In our experimental protocol, we use SFT to suppress an unwanted existing model capability and then use ICL to try to bring that capability back (many-shot jailbreaking). We then experiment on real-world instruction-tuned LLMs using capabilities benchmarks as well as a new many-shot jailbreaking dataset. In all cases, Bayesian scaling laws accurately predict the conditions under which ICL will cause the suppressed behavior to reemerge, which sheds light on the ineffectiveness of post-training at increasing LLM safety.

Hawkes processes are a class of past-dependent point processes, widely used in many applications such as seismology [Ogata, 1988], criminology [Olinde and Short, 2020] and neuroscience [Reynaud-Bouret et al., 2013] for their ability to capture complex dependence structures. In their multidimensional version [Ogata, 1988], Hawkes processes can model pairwise interactions between different types of events, allowing to recover a connectivity graph between different features. Originally developed by Hawkes [1971] in order to model self-exciting phenomena, where each event increases the probability of a new event occurring, many extensions have been proposed ever since. In particular, nonlinear Hawkes processes have been introduced notably to detect inhibiting interactions, when an event can decrease the probability of another one appearing. Hawkes processes with inhibition are notoriously more complicated to handle due to the loss of many properties of linear Hawkes processes such as the cluster representation and the branching structure of the process [Hawkes and Oakes, 1974]. Since the first article on nonlinear Hawkes processes [Brémaud and Massoulié, 1996] proving in particular their existence, many works have focused on inhibition in the past few years. Among them, limit theorems have been established in [Costa et al., 2020] while Duval et al. [2022] obtained mean-field results on the behaviour of two neuronal populations. Regarding statistical inference, in the frequentist setting we can mention the exact maximum likelihood procedure of Bonnet et al. [2023], the least-squares approach by Bacry et al. [2020] and the nonparametric approach based on Bernstein-type polynomials by Lemonnier and Vayatis [2014]. While the first one proposes an exact inference procedure, it is restricted to exponential kernels.

Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is modeled as a stream of tasks drawn from a task distribution. We propose EPIC (\underline{E}mpirical \underline{P}AC-Bayes that \underline{I}mproves \underline{C}ontinuously), a novel algorithm designed for lifelong RL using PAC-Bayes theory. EPIC learns a shared policy distribution, referred to as the \textit{world policy}, which enables rapid adaptation to new tasks while retaining valuable knowledge from previous experiences. Our theoretical analysis establishes a relationship between the algorithm's generalization performance and the number of prior tasks preserved in memory. We also derive the sample complexity of EPIC in terms of RL regret. Extensive experiments on a variety of environments demonstrate that EPIC significantly outperforms existing methods in lifelong RL, offering both theoretical guarantees and practical efficacy through the use of the world policy.

Large language models (LLMs) have shown remarkable versatility across tasks, but aligning them with individual human preferences remains challenging due to the complexity and diversity of these preferences. Existing methods often overlook the fact that preferences are multi-objective, diverse, and hard to articulate, making full alignment difficult. In response, we propose an active preference learning framework that uses binary feedback to estimate user preferences across multiple objectives. Our approach leverages Bayesian inference to update preferences efficiently and reduces user feedback through an acquisition function that optimally selects queries. Additionally, we introduce a parameter to handle feedback noise and improve robustness. We validate our approach through theoretical analysis and experiments on language generation tasks, demonstrating its feedback efficiency and effectiveness in personalizing model responses.

Credit card fraud detection is a critical concern for financial institutions, intensified by the rise of contactless payment technologies. While deep learning models offer high accuracy, their lack of explainability poses significant challenges in financial settings. This paper explores the integration of fuzzy logic into Deep Symbolic Regression (DSR) to enhance both performance and explainability in fraud detection. We investigate the effectiveness of different fuzzy logic implications, specifically {\L}ukasiewicz, G\"odel, and Product, in handling the complexity and uncertainty of fraud detection datasets. Our analysis suggest that the {\L}ukasiewicz implication achieves the highest F1-score and overall accuracy, while the Product implication offers a favorable balance between performance and explainability. Despite having a performance lower than state-of-the-art (SOTA) models due to information loss in data transformation, our approach provides novelty and insights into into integrating fuzzy logic into DSR for fraud detection, providing a comprehensive comparison between different implications and methods.

Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences. We revisit SR from a novel information-theoretic perspective and find that conventional sequential modeling methods fail to adequately capture the randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the DDSR model, which uses fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests. Formally based on diffusion transition processes in discrete state spaces, which is unlike common diffusion models such as DDPM that operate in continuous domains. It is better suited for discrete data, using structured transitions instead of arbitrary noise introduction to avoid information loss. Additionally, to address the inefficiency of matrix transformations due to the vast discrete space, we use semantic labels derived from quantization or RQ-VAE to replace item IDs, enhancing efficiency and improving cold start issues. Testing on three public benchmark datasets shows that DDSR outperforms existing state-of-the-art methods in various settings, demonstrating its potential and effectiveness in handling SR tasks.

Abstract--Teleoperation plays a crucial role in enabling robot operations in challenging environments, yet existing limitations in effectiveness and accuracy necessitate the development of innovative strategies for improving teleoperated tasks. This article introduces a novel approach that utilizes mixed reality and assistive autonomy to enhance the efficiency and precision of humanoid robot teleoperation. By leveraging Probabilistic Movement Primitives, object detection, and Affordance Templates, the assistance combines user motion with autonomous capabilities, achieving task efficiency while maintaining humanlike robot motion. Experiments and feasibility studies on the Nadia robot confirm the effectiveness of the proposed framework. Supplementary video available at https://youtu.be/oN-FD6YnF2c.

As a prominent category of imitation learning methods, adversarial imitation learning (AIL) has garnered significant practical success powered by neural network approximation. However, existing theoretical studies on AIL are primarily limited to simplified scenarios such as tabular and linear function approximation and involve complex algorithmic designs that hinder practical implementation, highlighting a gap between theory and practice. In this paper, we explore the theoretical underpinnings of online AIL with general function approximation. We introduce a new method called optimization-based AIL (OPT-AIL), which centers on performing online optimization for reward functions and optimism-regularized Bellman error minimization for Q-value functions. Theoretically, we prove that OPT-AIL achieves polynomial expert sample complexity and interaction complexity for learning near-expert policies. To our best knowledge, OPT-AIL is the first provably efficient AIL method with general function approximation. Practically, OPT-AIL only requires the approximate optimization of two objectives, thereby facilitating practical implementation. Empirical studies demonstrate that OPT-AIL outperforms previous state-of-the-art deep AIL methods in several challenging tasks.