On the other hand, for goal-oriented tasks, humans create mental models of the environment for planning their actions to achieve their goals [1,2]. In AI systems, recent research has shown that usersformmentalmodelsoftheAI'sstateandbehaviour[ 3,4].

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid ones. Applied to the large-scale IEEE 14-Bus system, our method achieves a 60% reduction in simulation time compared to conventional simulations, while maintaining the same level of accuracy through error control mechanisms.

We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.

We thank the reviewers for the detailed and insightful reviews. As the reviewers noted, our work 1) contributes to "a Thank you for the valuable feedback on this section -- we will incorporate this in our next revision. The intuition for the proof of Theorem 3.3 is that the optimization problem is convex over the space of probability By weak regularization, we refer to the fact that λ 0 for our Theorem 4.1 to hold. The difficulty with ReLU networks is that if the gradient flow pushes neurons towards 0, issues of differentiability arise. One potential approach to circumvent this issue is arguing that with correct initialization, the iterates will never reach 0. This is an interesting direction for future work and we thank the reviewer for this suggestion.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper describes a framework particularly useful for semi-supervised learning based on Fredholm kernels. The classical supervised learning optimization problem solved in kernel-based methods is extended to incorporate unlabeled information leading to discretized version of the Fredholm integral equation. Quality The paper has high technical quality with well-supported claims by theoretical analysis and convincing experimental results. The proposed formulation leads to a new data-dependent kernel that incorporates unlabeled information.

We study a wide class of non-convex non-concave min-max games that generalizes over standard bilinear zero-sum games.