United States

This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled molecules, the previous pre-training methods still rely on semantic subgraphs, i.e., functional groups. Only focusing on the semantic subgraphs could overlook the graph-level distinctions. The key challenge to build a pre-trained GNN on molecules is how to (1) generate well-distinguished graph-level representations and (2) automatically discover the functional groups without prior knowledge. To solve it, we propose a novel Subgraph-conditioned Graph Information Bottleneck for pre-training GNNs to recognize core subgraphs (graph cores) and significant subgraphs, named S-CGIB. The key idea is that the graph cores contain compressed and sufficient information that could generate well-distinguished graph-level representations and reconstruct the input graph conditioned on significant subgraphs across molecules. To discover significant subgraphs without prior knowledge about functional groups, we propose generating a set of functional group candidates, i.e., ego networks, and using an attention-based interaction between the graph core and the candidates. Despite being learned from self-supervised learning, our learned subgraphs match the real-world functional groups. Extensive experiments on various molecule datasets across four domains demonstrate the superiority of S-CGIB.

AAAI 2025

Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

graph based machine learning

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/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. AAAI-25 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.



Compositional generalization is crucial for artificial intelligence agents tackling intricate reasoning over vision and language (V\&L) problems. While neuro-symbolic methods have demonstrated potential in understanding compositional structures, they face challenges such as the need for symbolic domain representations that typically involve a set of predefined predicates, difficulties in deriving domain predicates from raw data, and the requirement for differentiable operations to compose primitive concepts. To address these issues, we propose NeSyCoCo, which is built on the existing neuro-symbolic frameworks that leverage large language models (LLMs) for obtaining symbolic representations of the domain and map them to differentiable neural computations for V\&L reasoning. Our approach a) augments the natural language inputs with their dependency structure to improve the accuracy of symbolic representations, b) utilizes distributed word representations for handling the variety of linguistically motivated logical predicates that are linked to neural modules, and c) utilizes soft composition of normalized predicate scores for better semantic alignment between symbolic compositions and differentiable operations. NeSyCoCo achieves state-of-the-art results on the ReaSCAN and CLEVR-CoGenT compositional generalization benchmarks, as well as the CLEVR vision-language benchmark. It also maintains high accuracy with new, similar concepts in the CLEVR-SYN benchmark.

NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization

The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably secure image steganography. 
However, existing methods face issues such as low quality of generated images and lack of semantic control in the generation process.
To leverage provably secure steganography with more effective and high-performance image generation models, and to ensure that stego images can accurately extract secret messages even after being uploaded to social networks and subjected to lossy processing such as JPEG compression, we propose a high-quality, provably secure, and robust image steganography method based on state-of-the-art autoregressive (AR) image generation models using Vector-Quantized (VQ) tokenizers. 
Additionally, we employ a cross-modal error-correction framework that generates stego text from stego images to aid in restoring lossy images, ultimately enabling the extraction of secret messages embedded within the images. Extensive experiments have demonstrated that the proposed method provides advantages in stego quality, embedding capacity, and robustness, while ensuring provable undetectability.

Provably Secure Image Robust Steganography via Cross-modal Error Correction

The offline datasets for imitation learning (IL) in multi-agent games typically contain player trajectories exhibiting diverse strategies, which necessitate measures to prevent learning algorithms from acquiring undesirable behaviors. Learning representations for these trajectories is an effective approach to depicting the strategies employed by each demonstrator. However, existing learning strategies often require player identification or rely on strong assumptions, which are not appropriate for multi-agent games. Therefore, in this paper, we introduce the Strategy Representation for Imitation Learning (STRIL) framework, which (1) effectively learns strategy representations in multi-agent games, (2) estimates proposed indicators based on these representations, and (3) filters out sub-optimal data using the indicators.
STRIL is a plug-in method that can be integrated into existing IL algorithms. We demonstrate the effectiveness of STRIL across competitive multi-agent scenarios, including Two-player Pong, Limit Texas Hold'em, and Connect Four. Our approach successfully acquires strategy representations and indicators, thereby identifying dominant trajectories and significantly enhancing existing IL performance across these environments.

Learning Strategy Representation for Imitation Learning in Multi-Agent Games

Deep Neuro-Fuzzy Inference Systems (DNFIS) seamlessly fuse neural networks with the fuzzy inference system enabling intricate decision-making and knowledge representation, while upholding a commendable degree of adaptability and interpretability. However, the challenge of privacy-preserving inference (PI) over DNFIS has remained largely uncharted, with no prior research addressing this critical issue. In this paper, we embark on an exploration of this issue. We introduce an efficient and secure PI framework for DNFIS, named $\mathsf{PrivDNFIS}$, which leverages the well-established cryptographic primitive lattice-based homomorphic encryption as the building block to implement secure computation protocols for PI over DNFIS. Our work incorporates several non-trivial performance enhancements. Firstly, it consolidates multiple elements of input feature vectors into a single message, reducing encryption/decryption overhead. Secondly, building upon this novel encoding approach, $\mathsf{PrivDNFIS}$ can perform ciphertext aggregation and vector-vector inner production without necessitating time-consuming ciphertext rotation operations. Thirdly, we replace the softmax function in the DNFIS layer with a quadratic function to further enhance inference efficiency, without compromising the inference accuracy. Under the semi-honest threat model, we provide formal security proof for $\mathsf{PrivDNFIS}$. In comprehensive experimental results, $\mathsf{PrivDNFIS}$ is demonstrated to deliver roughly $1.9\sim 4.4 \times$ reduction in end-to-end time cost compared to the benchmark.

PrivDNFIS: Privacy-preserving and Efficient Deep Neuro-Fuzzy Inference System

Real-time object detection is critical for the decision-making process for many real-world applications, such as collision avoidance and path planning in autonomous driving. This work presents an innovative real-time streaming perception method, Transtreaming, which addresses the challenge of real-time object detection with dynamic computational delay. The core innovation of Transtreaming lies in its adaptive delay-aware transformer, which can concurrently predict multiple future frames and select the output that best matches the real-world present time, compensating for any system-induced computation delays.
The proposed model outperforms the existing state-of-the-art methods, even in single-frame detection scenarios, by leveraging a transformer-based methodology. It demonstrates robust performance across a range of devices, from powerful V100 to modest 2080Ti, achieving the highest level of perceptual accuracy on all platforms. Unlike most state-of-the-art methods that struggle to complete computation within a single frame on less powerful devices, Transtreaming meets the stringent real-time processing requirements on all kinds of devices. The experimental results emphasize the system's adaptability and its potential to significantly improve the safety and reliability for many real-world systems, such as autonomous driving.
Our code is open-sourced on https://anonymous.4open.science/r/Transtreaming-7333.

Transtreaming: Adaptive Delay-aware Transformer for Real-time Streaming Perception

Recent vision-language foundation models still frequently produce outputs misaligned with their inputs, evidenced by object hallucination in captioning and prompt misalignment in the text-to-image generation model. To build a reliable system, recent research has begun to explore methods to identify misaligned elements to enhance both interpretability and improve model performance. However, current approaches mainly rely on large foundation models or human annotations, limiting scalability due to substantial computational and resource costs. This work proposes a novel approach for detecting dense misalignments from pre-trained CLIP, specifically focusing on pinpointing misaligned words between images and text. We carefully revamp the gradient-based attribution computation method, enabling negative gradients of individual text tokens to indicate misalignment. Then, we propose F-CLIPScore, which aggregates misaligned attributions to detect fine-grained misalignments. We evaluate our method on various dense misalignment detection benchmarks, covering various image and text domains and misalignment types. Our experiments show that our method demonstrates state-of-the-art performance among zero-shot models and competitive performance with fine-tuned models while maintaining superior efficiency. Our qualitative examples show that our method has a unique strength to detect entity-level objects, intangible objects, and attributes that can not be easily detected for existing works. We present comprehensive ablation studies and analyses to elucidate the strengths and limitations of our proposed approach.

Extract Free Dense Misalignment from CLIP

In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been proposed to train models locally at each client without sharing their sensitive local data. Most of these approaches share either local model parameters or probabilistic predictions (soft labels) on a public dataset, or a combination of both. However, this still discloses private information and restricts local models to those that can be trained using gradient-based methods. To reduce the amount of shared information, we propose sharing only definitive class labels (hard labels) on a public unlabeled dataset. Clients then use a consensus of these shared labels as pseudo-labels for their local training. This federated co-training approach empirically enhances privacy without compromising model quality. Additionally, it allows the use of local models that are not suited for parameter aggregation in traditional federated learning, such as gradient-boosted decision trees, rule ensembles, and random forests.

Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning

Today we rely on networks that are created and maintained by smart devices. For such networks, there is no governing central authority but instead  the network structure is shaped by the decisions of selfish intelligent agents. A key property of such communication networks is that they should be easy to navigate for routing data. For this, a common approach is greedy routing, where every device simply routes data to a neighbor that is closer to the respective destination.    

Networks of intelligent agents can be analyzed via a game-theoretic approach and in the last decades many variants of network creation games have been proposed and analyzed. In this paper we present the first game-theoretic network creation model that incorporates greedy routing, i.e., the strategic agents in our model are embedded in some metric space and strive for creating a network among themselves where all-pairs greedy routing is enabled. Besides this, the agents optimize their connection quality within the created network by aiming for greedy routing paths with low stretch.

For our model, we analyze the existence of (approximate)-equilibria and the computational hardness in different underlying metric spaces. E.g., we characterize the set of equilibria in 1-2-metrics and tree metrics and show that Nash equilibria always exist. For Euclidean space, the setting which is most relevant in practice, we prove that equilibria are not guaranteed to exist but that the well-known $\Theta$-graph construction yields networks having a low stretch that are game-theoretically almost stable. For general metric spaces, we show that approximate equilibria exist where the approximation factor depends on the cost of maintaining any link.

Strategic Network Creation for Enabling Greedy Routing

Explainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic.Previous works perform rating prediction and explanation generation in a multi-task manner. However, these works suffer from incoherence between predicted ratings and explanations.To address the issue, we propose a novel framework that employs a large language model (LLM) to generate a rating, transforms it into a rating vector, and finally generates an explanation based on the rating vector and user-item information. Moreover, we propose utilizing publicly available LLMs and pre-trained sentiment analysis models to automatically evaluate the coherence without human annotations.Extensive experimental results on three datasets of explainable recommendation show that the proposed framework is effective, outperforming state-of-the-art baselines with improvements of 7.3% in explainability and 4.4% in text quality. The source code is available at https://anonymous.4open.science/r/CIER-EB6D.

Coherency Improved Explainable Recommendation via Large Language Model

Learning optimal policies in multi-agent cooperative settings with visual observations is significant and challenging. Agents must first perform state representation learning for their image observations and then learn policies in the abstracted state space. Aiming at this problem, we propose a novel model-based MARL method named Contrastive Latent World for Policy Optimization (CLWPO). In CLWPO, we first design a state representation model to facilitate learning in the latent state space. With the support of this model, we construct the latent world and introduce a contrastive variational bound (CVB) to optimize it. Subsequently, we develop a heuristic policy optimization (HPO) scheme, incorporating model-free learning with model-based planning to obtain robust policies that predict future behaviors. In particular, in the planning, we maintain a queue of teammate models and calculate an adaptive rollout length for each agent to support its self-imagination and reduce the model-based return discrepancy. Finally, we conducted extensive experiments in the PettingZoo benchmark, and results show that CLWPO significantly enhances learning efficiency and improves agent performance compared to state-of-the-art MARL methods.

Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

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NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization

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