Singapore

Combinatorial and topological structures, such as graphs, simplicial complexes, and cell complexes, form the foundation of geometric and topological deep learning (GDL and TDL) architectures. These models aggregate signals over such domains, integrate local features, and generate representations for diverse real-world applications. However, the distribution and diffusion behavior of GDL and TDL features during training remains an open and underexplored problem. Motivated by this gap, we introduce a cellular sheaf theoretic framework for modeling and analyzing the local consistency and harmonicity of node features and edge weights in graph-based architectures. By tracking local feature alignments and agreements through sheaf structures, the framework offers a topological perspective on feature diffusion and aggregation. Furthermore, a multiscale extension inspired by topological data analysis (TDA) is proposed to capture hierarchical feature interactions in graph models. This approach enables a joint characterization of GDL and TDL architectures based on their underlying geometric and topological structures and the learned signals defined on them, providing insights for future studies on conventional tasks such as node classification, substructure detection, and community detection.

AAAI 2026

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models

sheaf theory

topological deep learning

topological data analysis

geometric deep learning

attention mechanisms

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Diffusion probabilistic models have been successfully used to generate high resolution data from noise. However, limited interpretability and computationally expensive implementation renders most existing diffusion models unsuitable for applications where the goal is to achieve theoretically accurate results with a fair trade-off on the quality of generated samples. In this work, we propose an adaptive random feature architecture for training a diffusion model that can generate samples from the input distribution using limited parameters, and theoretically quantify the generalization bounds. Our results demonstrate the possibility of building interpretable diffusion model architectures. We validate the obtained theory using numerical results, which perform better (or comparably) to an U-Net architecture having same number of trainable parameters.

Generalization Bound for Diffusion Models using a Adaptive Random Features (DARF)

In this paper, we propose SubLoRA, a rank determination method for Low-Rank Adaptation (LoRA) based on submodular function maximization. In contrast to prior approaches, such as AdaLoRA, that rely on first-order (linearized) approximations of the loss function, SubLoRA utilizes second-order information to capture the potentially complex loss landscape by incorporating the Hessian matrix. We show that the linearization becomes inaccurate and ill-conditioned when the LoRA parameters have been well optimized, motivating the need for a more reliable and nuanced second-order formulation. To this end, we reformulate the rank determination problem as a combinatorial optimization problem with a quadratic objective. However, solving this problem exactly is NP-hard in general. To overcome the computational challenge, we introduce a submodular function maximization framework and devise a greedy algorithm with approximation guarantees. We derive a sufficient and necessary condition under which the rank-determination objective becomes submodular, and construct a closed-form projection of the Hessian matrix that satisfies this condition while maintaining computational efficiency. Our method combines solid theoretical foundations, second-order accuracy, and practical computational efficiency. We further extend SubLoRA to a joint optimization setting, alternating between LoRA parameter updates and rank determination under a rank budget constraint. Extensive experiments on fine-tuning physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) demonstrate the effectiveness of our approach. Results show that SubLoRA outperforms existing methods in both rank determination and joint training performance.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization

Mixture-of-Experts (MoE) layers have achieved notable success across various deep learning applications. However, the impact of the number of experts on MoE performance across different task settings remains poorly understood. In this work, we investigate the impact of expert quantity within the MoE architecture composed of multilayer perceptron (MLP) experts. Concretely, we develop a formal MoE model with MLP experts, derive a range of expert counts that optimizes performance and load balance, and validate it on synthetic data. By systematically varying the number of experts, we demonstrate that balancing specialization and effective expert routing is key to maximizing performance.

On the Impact of Expert Count in Mixture of Experts

While existing formal verification tools are increasingly effective at verifying local robustness properties, more complex ones, such as those involving multiple neural networks, remain beyond their capabilities: these properties cannot currently be expressed in their specification languages, nor can they be directly verified. We present CAISAR, an open-source platform for specifying and verifying properties of machine learning models, with particular focus on neural networks and support vector machines. CAISAR provides a high-level language for specifying complex properties and integrates several state-of-the-art verifiers for their automatic verification. Through concrete use cases, we show how CAISAR leverages automated graph-editing techniques to translates high-level specifications into queries for the supported verifiers, bridging the embedding gap between user specifications and the corresponding ones that are actually verified.

The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification

SHapley Additive exPlanations (SHAP) is a key tool for interpreting decision tree ensembles by assigning contribution values to features. The most accurate variant, Background SHAP, uses a background dataset to estimate feature distributions. We introduce WOODELF, a SHAP algorithm that unifies decision trees, game theory, and Boolean logic within a single framework. For each instance, WOODELF constructs a pseudo-Boolean formula that captures their feature values, the structure of the decision tree ensemble, and the entire background dataset. It then leverages this representation to compute Background SHAP in linear time. WOODELF can also compute Shapley interaction values, Banzhaf values, and Banzhaf interaction values. WOODELF runs efficiently on both CPU and GPU. We demonstrate these advantages empirically on two large, commonly used datasets, where WOODELF achieves at least an order-of-magnitude speedup over state-of-the-art methods.

From Decision Trees to Boolean Logic: A Fast and Unified SHAP Algorithm

Case-based reasoning networks are models that make predictions based on similarity between the input and prototypical parts of training samples. Such models explain each decision by pointing to the prototypes that contributed the most to the final outcome. While promising, such explanations are sometimes misleading, which hampers their usefulness in safety-critical contexts. Several instances may lead to different predictions and yet have the same explanation. Drawing inspiration from formal eXplainable AI, we propose Abductive Latent Explanations (ALEs), a formalism to express sufficient conditions on the latent representation of the instance that imply the prediction. Our approach combines the inherent interpretability of case-based reasoning models and the guarantees provided by formal XAI. We propose a solver-free algorithm for generating ALEs based on three distinct paradigms, compare them, and present the feasibility of our approach on diverse datasets for image classification.

Formal Abductive Latent Explanations for Prototype-Based Networks

In reinforcement learning (RL), it is advantageous to consider additional constraints on the action space to ensure safety. Existing work on such action-constrained RL often lacks expressive policy updates, computational efficiency, and predictable runtime. Recent work proposes to use truncated normal distributions for stochastic policy gradient methods. However, the computation of key characteristics, such as the entropy, log-probability, and their gradients, becomes intractable. Hence, prior work approximates these using the non-truncated distributions, which severely degrades performance. We argue that accurate estimation of these characteristics is crucial in the action-constrained RL setting, and propose efficient numerical approximations for them. We also provide an efficient sampling strategy for truncated policy distributions and validate our approach on three benchmark environments, which demonstrate significant performance improvements when using accurate estimations.

Improving Stochastic Action-Constrained Reinforcement Learning via Truncated Distributions

Robust Markov Decision Processes generalize classical MDPs that consider uncertainties in transition probabilities by defining a set of possible transition functions. An objective is a set of runs (or infinite trajectories) of the RMDP, and the value for an objective is the maximal probability that the agent can guarantee against the adversarial environment. We consider (a) reachability objectives, where given a target set of states, the goal is to eventually arrive at one of them; and (b) parity objectives, which are a canonical representation for $\omega$-regular objectives. In this work, we study the qualitative problem for reachability and parity objectives on RMDPs without making any assumption over the structures of the RMDPs, e.g., unichain or aperiodic. We present efficient algorithms with oracle access to uncertainty sets that solve qualitative problems of reachability and parity objectives, together with experimental results.

Qualitative Analysis of $\omega$-Regular Objectives on Robust MDPs

Python has become the dominant language for general-purpose programming, yet it lacks robust tools for formal verification. In contrast, programmers working in languages such as C benefit from mature model checkers, for example CBMC, which enable exhaustive symbolic reasoning and fault localisation. In this paper, we propose PyVeritas, a novel framework that leverages Large Language Models (LLMs) for high-level transpilation from Python to C, followed by bounded model checking and MaxSAT-based fault localisation in the generated C code. PyVeritas enables verification and bug localisation for Python code using existing model checking tools for C. Our empirical evaluation demonstrates that LLM-based transpilation can achieve a high degree of accuracy, up to 90% for some LLMs, enabling effective development environment that supports assertion-based verification and interpretable fault diagnosis for small yet non-trivial Python programs.

PyVeritas: On Verifying Python via LLM-Based Transpilation and Bounded Model Checking for C

Privacy leakage in AI-based decision processes poses significant risks, particularly when sensitive information can be inferred. We propose a formal framework to audit privacy leakage using abductive explanations, which identifies minimal sufficient evidence justifying model decisions and determines whether sensitive information disclosed. Our framework formalizes both individual and system-level leakage, introducing the notion of Potentially Applicable Explanations (PAE) to identify individuals whose outcomes can shield those with sensitive features. This approach provides rigorous privacy guarantees while producing human-understandable explanations, a key requirement for auditing tools. Experimental evaluation on the German Credit Dataset illustrates how the importance of sensitive literal in the model decision process affects privacy leakage.

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