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Graph convolutional networks (GCNs) have been grabbing widespread attentions due to their encouraging performance and powerful generalizations. However, few works provide a general view to interpret various GCNs and guide GCNs&#39; designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centered optimization framework, in which by building appropriate regularizers we could interpret most existing GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture both topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we borrow the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.

AAAI 2023

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

unified framework.

interpretable deep learning

graph convolutional networks

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Graph convolutional networks (GCNs) have been grabbing widespread attentions due to their encouraging performance and powerful generalizations. However, few works provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centered optimization framework, in which by building appropriate regularizers we could interpret most existing GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture both topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we borrow the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.

poster

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. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


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.

Since rain streaks exhibit diverse geometric appearances and irregular overlapped phenomena, these complex characteristics challenge the design of an effective single image deraining model. To this end, rich local-global information representations are increasingly indispensable for better satisfying rain removal. In this paper, we propose a lightweight Hybrid CNN-Transformer Feature Fusion Network (dubbed as HCT-FFN) in a stage-by-stage progressive manner, which can harmonize these two architectures to help image restoration by leveraging their individual learning strengths. Specifically, we stack a sequence of the degradation-aware mixture of experts (DaMoE) modules in the CNN-based stage, where appropriate local experts adaptively enable the model to emphasize spatially-varying rain distribution features. As for the Transformer-based stage, a background-aware vision Transformer (BaViT) module is employed to complement spatially-long feature dependencies of images, so as to achieve global texture recovery while preserving the required structure. Considering the indeterminate knowledge discrepancy among CNN features and Transformer features, we introduce an interactive fusion branch at adjacent stages to further facilitate the reconstruction of high-quality deraining results. Extensive evaluations show the effectiveness and extensibility of our developed HCT-FFN. The source code is available at https://github.com/cschenxiang/HCT-FFN.

Hybrid CNN-Transformer Feature Fusion for Single Image Deraining

The generalization ability often determines the success of machine learning algorithms in practice. Therefore, it is of great theoretical and practical importance to understand and bound the generalization error of machine learning algorithms. In this paper, we provide the first generalization results of the popular stochastic gradient descent (SGD) algorithm in the distributed asynchronous decentralized setting. Our analysis is based on the uniform stability tool, where stable means that the learned model does not change much in small variations of the training set. Under some mild assumptions, we perform a comprehensive generalizability analysis of the asynchronous decentralized SGD, including generalization error and excess generalization error bounds for the strongly convex, convex, and non-convex cases. Our theoretical results reveal the effects of the learning rate, training data size, training iterations, decentralized communication topology, and asynchronous delay on the generalization performance of the asynchronous decentralized SGD. We also study the optimization error regarding the objective function values and investigate how the initial point affects the excess generalization error. Finally, we conduct extensive experiments on MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets to validate the theoretical findings.

Stability-Based Generalization Analysis of the Asynchronous Decentralized SGD

Vaccine delivery in under-resourced locations with security
risks is not just challenging but also life threatening. The
COVID pandemic and the need to vaccinate added even more
urgency to this issue. Motivated by this problem, we propose
a general framework to set-up limited temporary (vaccination)
centers that balance physical security and desired (vaccine)
service coverage with limited resources. We set-up the
problem as a Stackelberg game between the centers operator
(defender) and an adversary, where the set of centers is not
fixed a priori but is part of the decision output. This results in
a mixed combinatorial and continuous optimization problem.
As part of our scalable approximation solution, we provide a
fundamental contribution by identifying general duality conditions
of switching max and min when both discrete and continuous
variables are involved. Via detailed experiments, we
show that the solution proposed is scalable in practice.

Securing Lifelines: Safe Delivery of Critical Services in Areas with Volatile Security Situation via a Stackelberg Game Approach

The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the performance of prediction drops dramatically when being removed. Though being explainable, most rationale extraction methods for multi-hop fact verification explore the semantic information within each piece of evidence individually, while ignoring the topological information interaction among different pieces of evidence. Intuitively, a faithful rationale bears complementary information being able to extract other rationales through the multi-hop reasoning process. To tackle such disadvantages, we cast explainable multi-hop fact verification as subgraph extraction, which can be solved based on graph convolutional network (GCN) with salience-aware graph learning. In specific, GCN is utilized to incorporate the topological interaction information among multiple pieces of evidence for learning evidence representation. Meanwhile, to alleviate the influence of noisy evidence, the salience-aware graph perturbation is induced into the message passing of GCN. Moreover, the multi-task model with three diagnostic properties of rationale is elaborately designed to improve the quality of an explanation without any explicit annotations. Experimental results on the FEVEROUS benchmark show significant gains over previous state-of-the-art methods for both rationale extraction and fact verification.

Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning

Ad hoc teamwork refers to collaboration in a team of agents without prior coordination. State of the art methods for this problem often include a data-driven component that uses a long history of prior observations to model the behaviour of other agents (or agent types) and to determine the ad hoc agent’s behaviour. In many practical domains, it is challenging to find large training datasets, and necessary to understand and incrementally extend the existing models to account for changes in team composition or domain attributes. Our architecture combines the principles of knowledge-based reasoning and data-driven learning. It enables the ad hoc agent to perform non-monotonic logical reasoning with prior commonsense domain knowledge and incrementally-updated simple predictive models of other agents’ behaviour. We use the benchmark simulated multiagent collaboration domain Fort Attack to demonstrate that our architecture enables an ad hoc agent to adapt to unforeseen changes, learn and revise its models of other agents’ behaviour from limited samples, support transparency in its decision making, and provide better performance than a data-driven baseline.

Back to the Future: Toward a Hybrid Architecture for Ad Hoc Teamwork

Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network pruning is considered the main cornerstone of model compression for reducing the costs of computation and memory. Unfortunately, pruning a generative model has not been extensively explored, and all existing pruning algorithms suffer from excessive weight-training costs, performance degradation, limited generalizability, or complicated training. To address these problems, we propose to find a strong lottery ticket via moment-matching scores. Our experimental results show that the discovered subnetwork can perform similarly or better than the trained dense model even when only 10% of the weights remain. To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably. Our code and supplementary materials are publicly available.

Can We Find Strong Lottery Tickets in Generative Models?

Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnam). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL  (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.


MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

Neural networks (NN) are an increasingly important representation of action policies pi. Recent work has extended predicate abstraction to prove safety of such pi, through policy predicate abstraction (PPA) which over-approximates the state space subgraph induced by pi. The advantage of PPA is that reasoning about the NN – calls to SMT solvers – is required only locally, at individual abstract state transitions, in contrast to bounded model checking (BMC) where SMT must reason globally about sequences of NN decisions. Indeed, it has been shown that PPA can outperform a simple BMC implementation. However, the abstractions underlying these results (i.e., the abstraction predicates) were supplied manually. Here we automate this step. We extend counterexample guided abstraction refinement (CEGAR) to PPA. This involves dealing with a new source of spuriousness in abstract unsafe paths, pertaining not to transition behavior but to the decisions of the neural network pi. We introduce two methods tackling this issue based on the states involved, and we show that global SMT calls deciding spuriousness exactly can be avoided. We devise algorithmic enhancements leveraging incremental computation and heuristic search. We show empirically that the resulting verification tool has significant advantages over an encoding into the state-of-the-art model checker nuXmv. In particular, ours is the only approach in our experiments that succeeds in proving policies safe.

Neural Policy Safety Verification via Predicate Abstraction: CEGAR

Fairness in influence maximization has been a very active research topic recently. Most works in this context study the question of how to find seeding strategies (deterministic or probabilistic) such that nodes or communities in the network get their fair share of coverage. Different fairness criteria have been used in this context. All these works assume that the entity that is spreading the information has an inherent interest in spreading the information fairly, otherwise why would they want to use the developed fair algorithms? This assumption may however be flawed in reality -- the spreading entity may be purely efficiency-oriented. In this paper we propose to study two optimization problems with the goal to modify the network structure by adding links in such a way that efficiency-oriented information spreading becomes automatically fair. We study the proposed optimization problems both from a theoretical and experimental perspective, that is, we give several hardness and hardness of approximation results, provide efficient algorithms for some special cases, and more importantly provide heuristics for solving one of the problems in practice. In our experimental study we then first compare the proposed heuristics against each other and establish the most successful one. In a second experiment, we then show that our approach can be very successful in practice. That is, we show that already after adding a few edges to the networks the greedy algorithm that purely maximizes spread surpasses all fairness-tailored algorithms in terms of ex-post fairness. Maybe surprisingly, we even show that our approach achieves ex-post fairness values that are comparable or even better than the ex-ante fairness values of the currently most efficient algorithms that optimize ex-ante fairness.

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Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

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