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This paper studies decentralized optimization over a compact submanifold within a communication network of $n$ nodes, where each node possesses a smooth non-convex local cost function, and the goal is to jointly minimize the sum of these local costs. We focus particularly on the online setting, where local data is processed in real-time as it streams in, without the need for full data storage. We propose a decentralized projected Riemannian stochastic recursive momentum (DPRSRM) method that employs local hybrid stochastic gradient estimators and uses the network to track the global gradient. DPRSRM achieves an oracle complexity of  $\mathcal{O}(\epsilon^{-\frac{3}{2}})$, outperforming existing methods that have at most $\mathcal{O}(\epsilon^{-2})$ complexity. Our method requires only $\mathcal{O}(1)$ gradient evaluations per iteration for each local node and does not require restarting with a large batch gradient.   Furthermore, we demonstrate the effectiveness of our proposed methods compared to state-of-the-art ones through numerical experiments on principal component analysis problems and low-rank matrix completion.

AAAI 2025

Decentralized Projected Riemannian Stochastic Recursive Momentum Method for Nonconvex Optimization

distributed machine learning

federated learning

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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. 

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.



While Nash equilibria are guaranteed to exist, they may exhibit dense support, making them difficult to understand and execute in some applications. In this paper, we study $k$-sparse commitments in games where one player is restricted to mixed strategies with support size at most $k$. Finding $k$-sparse commitments is known to be computationally hard. We start by showing several structural properties of $k$-sparse solutions, including that the optimal support may vary dramatically as $k$ increases. These results suggest that naive greedy or double-oracle-based approaches are unlikely to yield practical algorithms. We then develop a simple approach based on mixed integer linear programs (MILPs) for zero-sum games, general-sum Stackelberg games, and various forms of structured sparsity. We also propose practical algorithms for cases where one or both players have large (i.e., practically innumerable) action sets, utilizing a combination of MILPs and incremental strategy generation. We evaluate our methods on synthetic and real-world scenarios based on security applications. In both settings, we observe that even for small support sizes, we can obtain more than $90\%$ of the true Nash value while maintaining a reasonable runtime, demonstrating the significance of our formulation and algorithms.

Commitment to Sparse Strategies in Two-Player Games

REST APIs have become key components of web services. However, they often contain logic flaws resulting in server side errors or security vulnerabilities. HTTP requests are used as test cases to find and mitigate such issues. Existing methods to modify requests, including those using deep learning, suffer from limited performance and precision, relying on undirected search or making limited usage of the contextual information. In this paper we propose APIRL, a fully automated deep reinforcement learning tool for testing REST APIs. A key novelty of our approach is the use of feedback from a transformer moudle pre-trained on JSON-structured data, akin to that used in API responses. This allows APIRL to learn the subtleties relating to test outcomes, and generalise to unseen API endpoints. We show APIRL can find significantly more bugs than the state-of-the-art in real world REST APIs while minimising the number of required test cases. We also study how reward functions, and other key design choices, affect learnt policies in a thorough ablation study.

APIRL: Deep Reinforcement Learning for REST API Fuzzing

These recent years have witnessed that convolutional neural network (CNN)-based methods for detecting infrared small targets have achieved outstanding performance. However, these methods typically employ standard convolutions, neglecting to consider the spatial characteristics of the pixel distribution of infrared small targets. Therefore, we propose a novel Pinwheel-shaped Convolution (PConv) as a replacement for standard convolutions in the lower layers of the backbone network. PConv better aligns with the pixel gaussian spatial distribution of dim small targets, enhances feature extraction, significantly increases the receptive field, and introduces only a minimal increase in parameters.
Additionally, while recent loss functions combine scale and location losses, they do not adequately account for the varying sensitivity of these losses across different target scales, limiting detection performance on dim-small targets. To overcome this, we propose a Scale-based Dynamic (SD) Loss that dynamically adjusts the influence of scale and location losses based on target size, improving the network’s ability to detect targets of varying scales.
We construct a new benchmark, SIRST-UAVB, which is the largest and most challenging dataset to date for real-shot single-frame infrared small target detection. Lastly, by integrating PConv and SD Loss into the latest small target detection algorithms, we achieved significant performance improvements on IRSTD-1K and our SIRST-UAVB dataset, validating the effectiveness and generalizability of our approach.

Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared Small Target Detection

3D occupancy perception accurately estimates the volumetric status and semantic labels of a scene, attracting significant attention in the field of autonomous driving.
However, enhancing the model's ability to generalize across different driving scenarios or sensing systems, often requires redesigning the model or extra-expensive annotations.
To this end, following a comprehensive analysis of the occupancy model architecture, we proposed the \textbf{UGOCC} method that utilizes domain adaptation to efficiently harness unlabeled autonomous driving data, thereby enhancing the model’s generalizability.
Specifically, we design the depth fusion module by employing self-supervised depth estimation, and propose a strategy based on semantic attention and domain adversarial learning to improve the generalizability of the learnable fusion module. Additionally, we propose an OCC-specific pseudo-label selection tailored for semi-supervised learning, which optimizes the overall network's generalizability.
Our experiment results on two challenging datasets nuScenes and Waymo, demonstrate that our method not only achieves state-of-the-art  generalizability but also enhances the model's perceptual capabilities within the source domain by utilizing unlabeled data.

Enhancing Generalizability via Utilization of Unlabeled Data for Occupancy Perception

Most existing just noticeable difference (JND) methods primarily integrate specific masking effects in a single domain. However, these single-domain JND methods struggle with the structural discrepancies in multi-source content images, limiting their effectiveness in visual redundancy estimation. To address this  issue, we propose a dual domain encoder that combines spatial and frequency features to comprehensively capture visual patterns. Our design includes spatial pattern balance and frequency detail correction modules to balance global and local patterns and correct low- and high-frequency  distributions. Additionally, we develop a dual domain decoder to effectively extract multi-scale pattern redundancies and integrate them with detail redundancies in the frequency domain. Experiments demonstrate the effectiveness and robustness of our proposed method in handling structural discrepancies in multi-source content images.

DDJND: Dual Domain Just Noticeable Difference in Multi-Source Content Images with Structural Discrepancy

Anomaly detection on attributed networks has applications in various domains such as finance and email spam detection, thus gaining substantial attention. However, the current mainstream anomaly detection methods are mainly designed for centralized architectures, which may bring the risk of privacy leakage in some sensitive scenarios. As an emerging technology, federated graph learning aims to realize model training of graph data through distributed collaboration, while preserving data privacy. Nevertheless, when graph data is scattered across multiple clients, federated graph learning may not manage to harness the full potential of distributed data, yielding suboptimal performance. Taking into account this insight, we propose a federated graph anomaly detection framework utilizing contrastive self-supervised learning (FedCLGN). First, we put forward an augmentation method to maintain global negative pairs on the server side. This includes initial identification of anomaly nodes using pseudo-labels and extraction of node neighbor embedding data from clients. Then, we adopt graph diffusion to enhance the feature representation of nodes, capturing the global structure and local connection patterns. This strategy can strengthen the differentiation between positive and negative instance pairs. Finally, the effectiveness of our approach is verified by experimental results on four real graph datasets.

Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs

Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities. Despite these achievements, LLMs still encounter significant challenges when applied in industrial scenarios, particularly due to the increased complexity of reasoning required with real-world tabular data, underscoring a notable disparity between academic benchmarks and practical applications. To address this discrepancy, we conduct a detailed investigation into the application of tabular data in industrial scenarios and propose a comprehensive and complex benchmark TableBench, including 18 fields within four major categories of table question answering (TableQA) capabilities. Furthermore, we introduce TableLLM, trained on our meticulously constructed training set TableInstruct, achieving comparable performance with GPT-3.5. Massive experiments conducted on TableBench indicate that both open-source and proprietary LLMs still have significant room for improvement to meet real-world demands, where the most advanced model, GPT-4, achieves only a modest score compared to humans.

TableBench: A Comprehensive and Complex Benchmark for Table Question Answering

Despite the advancements in large language models (LLMs) for mathematical reasoning, solving competition-level math problems remains a significant challenge, especially for open-source LLMs without external tools. We introduce the MMIQC dataset, comprising a mixture of processed web data and synthetic question-response pairs, aimed at enhancing the mathematical reasoning capabilities of base language models. Models fine-tuned on MMIQC consistently surpass their counterparts in performance on the MATH benchmark across various model sizes. Notably, Qwen-72B-MMIQC achieves a 45.0% accuracy, exceeding the previous open-source state-of-the-art by 8.2% and outperforming the initial version GPT-4 released in 2023. Extensive evaluation results on Hungarian high school finals suggest that such improvement can generalize to unseen data. Our ablation study on MMIQC reveals that a large part of the improvement can be attributed to our novel augmentation method, Iterative Question Composing (IQC), which involves iteratively composing new questions from seed problems using an LLM and applying rejection sampling through another LLM.

Augmenting Math Word Problems via Iterative Question Composing

In recent years, Federated Domain Generalization (FedDG) has succeeded in generalizing to unknown clients (domains).
However, current methods only utilize training data, and when there is a significant difference between the unknown client and source client domains (domain shift), these methods cannot ensure model performance. This limitation appears to have caused research in FedDG to reach a bottleneck. On the other hand, test data is a resource that can help models adapt while previous FedDG approaches have not taken this into account. In this paper, we introduce a new framework TTA-FedDG to address the FedDG problem, which leverages test-time adaptation (TTA) to adapt across different domains, thereby enhancing the generalization of the model. We propose the method Federated domain generalization based on select Strong Pseudo Label (FedSPL), which combines fast feature matching and knowledge distillation. Our method consists of two parts. Firstly, we use fast feature reordering for feature mixing during local updates on the client side, improving the robustness of the global model and enhancing its generalization ability to mitigate domain shift. Secondly, we
employ a teacher-student model with contrastive learning and label selection during the testing phase, enabling the global model to better adapt to the distribution of the target client,thereby alleviating domain shift. Extensive experiments havedemonstrated the effectiveness of FedSPL in handling domain shift, outperforming existing FedDG methods across multiple datasets and model architectures.

TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization

Ordinal regression (also known as ordinal classification) classifies an object to belong to a class out of a given set of possible classes, where labels possess a natural order. It is relevant
to a wide array of domains including risk assessment, sentiment analysis, image ranking, and recommender systems.
Like common classification, the primary goal of ordinal regression is accuracy. Yet, in this context, the severity of prediction errors varies, e.g., in risk assessment, Critical Risk
assessment is more urgent than High risk and significantly
more urgent than No risk. This leads to a modified objective
of ensuring that the model’s output is as close as possible to
the correct class, considering the order of labels. Therefore,
ordinal regression models can use a specialized ordinal loss
for training. In this work, we focus on two properties of ordinal losses, namely monotonicity and balance sensitivity. We
show that existing ordinal loss functions lack these properties and introduce SLACE, a novel loss function that provably satisfy said properties. We demonstrate empirically that
SLACE outperforms the state-of-the-art ordinal loss functions on most tabular ordinal regression benchmarks.

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Commitment to Sparse Strategies in Two-Player Games

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