Modeling uncertainty in deep neural networks, despite recent important advances, is still an open problem. Bayesian neural networks are a powerful solution, where the prior over network weights is a design choice, often a normal distribution or other distribution encouraging sparsity. However, this prior is agnostic to the generative process of the input data, which might lead to unwarranted generalization for out-of-distribution tested data. We suggest the presence of a confounder for the relation between the input data and the discriminative function given the target label. We propose an approach for modeling this confounder by sharing neural connectivity patterns between the generative and discriminative networks. This approach leads to a new deep architecture, where networks are sampled from the posterior of local causal structures, and coupled into a compact hierarchy. We demonstrate that sampling networks from this hierarchy, proportionally to their posterior, is efficient and enables estimating various types of uncertainties. Empirical evaluations of our method demonstrate significant improvement compared to state-of-the-art calibration and out-of-distribution detection methods.

As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability. In addition, these methods are also computationally inefficient, and require significant hyper-parameter tuning. In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty. We instantiate this framework to obtain Bayesian versions of LIME and KernelSHAP which output credible intervals for the feature importances, capturing the associated uncertainty. The resulting explanations not only enable us to make concrete inferences about their quality (e.g., there is a 95% chance that the feature importance lies within the given range), but are also highly consistent and stable. We carry out a detailed theoretical analysis that leverages the aforementioned uncertainty to estimate how many perturbations to sample, and how to sample for faster convergence. This work makes the first attempt at addressing several critical issues with popular explanation methods in one shot, thereby generating consistent, stable, and reliable explanations with guarantees in a computationally efficient manner. Experimental evaluation with multiple real world datasets and user studies demonstrate that the efficacy of the proposed framework.

In imperfect-information games, agents must make decisions based on partial knowledge of the game state. The Belief Stochastic Game model addresses this challenge by delegating state estimation to the game model itself. This allows agents to operate on externally provided belief states, thereby reducing the need for game-specific inference logic. This paper investigates two approaches to represent beliefs in games with hidden piece identities: a constraint-based model using Constraint Satisfaction Problems and a probabilistic extension using Belief Propagation to estimate marginal probabilities. We evaluated the impact of both representations using general-purpose agents across two different games. Our findings indicate that constraint-based beliefs yield results comparable to those of probabilistic inference, with minimal differences in agent performance. This suggests that constraint-based belief states alone may suffice for effective decision-making in many settings.

We truly appreciate helpful comments from all three reviewers. Note our model is not designed to predict the occurrence of new nodes. Eq. 4. Note that the posterior of A more comprehensive related work section will be added.

Here are my comments for the paper: - B2N, RAI, and GGT abbreviations are never defined in the paper; the have been just cited from previous works (minor). A short background section on these methods can also include their full name. As far as I understand, the proposed method is B2N with B-RAI instead of RAI which was originally proposed in [25]. This allows the model to sample multiple generative and discriminative structures, and as a result create an ensemble of networks with possibly different structures and parameters. Maybe a better way for structuring the paper is to have a background section on B-RAI and B2N, and a separate section on BRAINet in which the distinction with other works and contribution is clearly written.

This paper proposes BRAINet as to combine Bayesian structure learning and Bayesian neural networks. In detail, the method assumes a confounder on the input features X and the discriminative network parameters \phi, where this confounder is defined as the generative graph structure on X, and the discriminative network shares the same structure as the generative one. Given observations X and Y, the approach first sample the generative graph structure from the posterior given X, then train the parameters of the corresponding discriminative network in order to fit the posterior distribution of phi given X and Y. Experiments are performed on calibration and OOD tasks, with MC-dropout and deep Ensembles as the main comparing baselines. Reviewers include experts in Bayesian structure learning and Bayesian neural networks. They read the author feedback carefully and engaged in post-rebuttal discussion actively.

As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability. In addition, these methods are also computationally inefficient, and require significant hyper-parameter tuning. In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty. We instantiate this framework to obtain Bayesian versions of LIME and KernelSHAP which output credible intervals for the feature importances, capturing the associated uncertainty.

Modeling uncertainty in deep neural networks, despite recent important advances, is still an open problem. Bayesian neural networks are a powerful solution, where the prior over network weights is a design choice, often a normal distribution or other distribution encouraging sparsity. However, this prior is agnostic to the generative process of the input data, which might lead to unwarranted generalization for out-of-distribution tested data. We suggest the presence of a confounder for the relation between the input data and the discriminative function given the target label. We propose an approach for modeling this confounder by sharing neural connectivity patterns between the generative and discriminative networks.

This series is a brief introduction to modeling uncertainty using TensorFlow Probability library. I wrote it as a supplementary material to my PyData Global 2021 talk on uncertainty estimation in neural networks. We went a long way so far! We're going to use all the knowledge we've gained and apply it to a new -- more challenging -- dataset. Let's get our hands dirty!