AAAI 2025 Papers

Recent advancement in text-to-image models and corresponding personalized technologies enables individuals to generate high-quality and imaginative images. However, they often suffer from limitations when generating images with resolutions outside of their trained domain. To overcome this limitation, we present the resolution adapter \textbf{(ResAdapter)}, a domain-consistent adapter designed for diffusion models to generate images with unrestricted resolutions and aspect ratios. Unlike other multi-resolution generation methods that process images of static resolution with complex post-process operations, ResAdapter directly generates images with the dynamical resolution. Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain. Comprehensive experiments demonstrate that ResAdapter with only 0.5M can process images with flexible resolutions for arbitrary diffusion models. More extended experiments demonstrate that ResAdapter is compatible with other modules for image generation across a broad range of resolutions, and can be integrated into other multi-resolution model for efficiently generating higher-resolution images.

Auction-based Federated Learning (AFL) has gained significant research interest due to its ability to incentivize data owners (DOs) to participate in FL model training tasks of data consumers (DCs) through economic mechanisms via the auctioneer. One of the critical research issues in AFL is decision support for the auctioneer. Existing approaches are based on the simplified assumption of a single, monopolistic AFL marketplace, which is unrealistic in real-world scenarios where multiple AFL marketplaces can co-exist and compete for the same pool of DOs. In this paper, we relax this assumption and frame the AFL auctioneer decision support problem from the perspective of helping them attract participants in a competitive AFL marketplace environment while safeguarding profit. To achieve this objective, we propose the Auctioneer Revenue Allocation Strategy for AFL (ARAS-AFL). We design the concepts of the attractiveness and competitiveness from the perspective of autioneer reputation. Based on the Lyapunov optimization, ARAS-AFL helps individual AFL auctioneer achieve the dual objective of balancing the reputation management costs and its own profit by designing a dynamic revenue allocation strategy. It takes into account both the auctioneer’s revenue and the changes in the number of participants on the AFL marketplace. Through extensive experiments on widely used benchmark datasets, ARAS-AFL demonstrates superior performance compared to state-of-the-art approaches. It outperforms the best baseline by 49.06%, 98.69%, 10.32%, and 4.77% in terms of total revenue, number of data owners, public reputation and accuracy of federated learning models, respectively.

Although Coordinate-MLP-based implicit neural representations have excelled in representing radiance fields, 3D shapes, and images, their application to audio signals remains underexplored. To fill this gap, we investigate existing implicit neural representations, from which we extract 3 types of positional encoding and 16 commonly used activation functions. Through combinatorial design, we establish the first benchmark for Coordinate-MLPs in audio signal representations. Our benchmark reveals that Coordinate-MLPs require complex hyperparameter tuning and frequency-dependent initialization, limiting their robustness. To address these issues, we propose Fourier-ASR, a novel framework based on the Fourier series theorem and the Kolmogorov-Arnold representation theorem. Fourier-ASR introduces Fourier Kolmogorov-Arnold Networks (Fourier-KAN), which leverage periodicity and strong nonlinearity to represent audio signals, eliminating the need for additional positional encoding. Furthermore, a Frequency-adaptive Learning Strategy (FaLS) is proposed to enhance the convergence of Fourier-KAN by capturing high-frequency components and preventing overfitting of low-frequency signals. Extensive experiments conducted on natural speech and music datasets reveal that: (1) well-designed positional encoding and activation functions in Coordinate-MLPs can effectively improve audio representation quality; and (2) Fourier-ASR can robustly represent complex audio signals without extensive hyperparameter tuning. Looking ahead, the continuity and infinite resolution of implicit audio representations make our research highly promising for tasks such as audio compression, synthesis, and generation.

The ability to reason at multiple levels of temporal abstraction is a fundamental aspect of intelligence. In reinforcement learning (RL), this attribute is often modelled through temporally extended courses of actions called options. In this talk, I will introduce a general framework for option discovery, which uses the agent's representation to discover useful options. By leveraging these options to generate a rich stream of experience, the agent can improve its representations and learn more effectively. This representation-driven option discovery approach creates a virtuous cycle of refinement, continuously improving both the representation and options, and it is particularly effective for problems where agents need to operate at varying levels of abstraction to succeed.

Tabular data, widely used across industries, remains underexplored in deep learning. Self-supervised learning (SSL) shows promise for pre-training deep neural networks (DNNs) on tabular data, but its potential is hindered by challenges in designing suitable augmentations. Unlike image and text data, where SSL leverages inherent spatial or semantic structures, tabular data lacks such explicit structure. This makes traditional input-level augmentations, like modifying or removing features, less effective due to difficulties in balancing critical information preservation with variability. To address these challenges, we propose RaTab, a novel method that shifts augmentation from input-level to representation-level using matrix factorization, specifically truncated SVD. This approach preserves essential data structures while generating diverse representations by applying dropout at various stages of the representation, thereby significantly enhancing SSL performance for tabular data.

Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations; e.g., that they be non-spurious, efficient, or disentangled. It can be challenging, however, to turn these intuitive desiderata into formal criteria that can be measured and enhanced based on observed data. In this paper, we take a causal perspective on representation learning, formalizing desiderata like non-spuriousness and demonstrating their practical utility.

The recognition of whether or not a predicate should be invented is an important problem in the domain of predicate invention. Despite its significance, existing research has yet to fully harness the rich data available in knowledge graphs. In this paper, we introduce a novel problem formulation, ReLPI (Representation Learning for Predicate Invention in Knowledge Graphs), marking a pioneering effort in this domain. To address the core issues of ReLPI, we devise a scoring function that informs the learning process. By optimizing embeddings towards this scoring function, we endow them with semantic meaning, crucial for capturing the nuances of predicate presence patterns. Furthermore, we present SEmPI (Semantic Embeddings for Predicate Invention), a framework that leverages predicate (relation) embeddings as a trainable medium. SEmPI uncovers latent patterns governing predicate occurrences in knowledge graphs, enabling the invention of novel predicates grounded in these discovered patterns. This approach represents a significant step forward in leveraging data-driven methods for predicate invention in knowledge graphs. We evaluate the proposed approach on FB15k and DRKG datasets, and the results demonstrate the effectiveness of SEmPI in discovering new predicates.

We study the problem of designing replication-proof bandit mechanisms when agents strategically register or replicate their own arms to maximize their payoff. Specifically, we consider Bayesian agents who only know the distribution from which their own arms' mean rewards are sampled, unlike the original setting of by Shin, Lee, and Ok AISTATS'22. Interestingly, with Bayesian agents in stark contrast to the previous work, analyzing the replication-proofness of an algorithm becomes significantly complicated even in a single-agent setting. We provide sufficient and necessary conditions for an algorithm to be replication-proof in the single-agent setting, and present an algorithm that satisfies these properties. These results center around several analytical theorems that focus on comparing the expected regret of multiple bandit instances, and therefore might be of independent interest since they have not been studied before to the best of our knowledge. We expand this result to the multi-agent setting, and provide a replication-proof algorithm for any problem instance. We finalize our result by proving its sublinear regret upper bound which matches that of Shin, Lee, and Ok AISTATS'22.

A core tension in the study of plurality elections is the clash between the classic Hotelling-Downs model, which predicts that two office-seeking candidates should cater to the median voter, and the empirical observation that democracies often have two major parties with divergent policies. Motivated in part by this tension, we introduce a dynamic model of candidate positioning based on a simple bounded rationality heuristic: candidates imitate the policy of previous winners. The resulting model is closely connected to evolutionary replicator dynamics. For uniformly-distributed voters, we prove in our model that with k = 2, 3, or 4 candidates per election, any symmetric candidate distribution converges over time to the center. With k ≥ 5 candidates per election, however, we prove that the candidate distribution does not converge to the center and provide an even stronger non-convergence result in a special case with no extreme candidates. Our conclusions are qualitatively unchanged if a small fraction of candidates are not winner-copiers and are instead positioned uniformly at random in each election. Beyond our theoretical analysis, we illustrate our results in extensive simulations; for five or more candidates, we find a tendency towards the emergence of two clusters, a mechanism suggestive of Duverger's Law, the empirical finding that plurality leads to two-party systems. Our simulations also explore several variations of the model, where we find the same general pattern: convergence to the center with four or fewer candidates, but not with five or more. Finally, we discuss the relationship between our replicator dynamics model and prior work on strategic equilibria of candidate positioning games.

Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test time one sees entities that were not present during training; the most performant models in the inductive setting have employed path encoding modules in addition to standard subgraph encoding modules. This work similarly focuses on KG completion in the inductive setting, without the explicit use of path encodings, which can be time-consuming and introduces several hyperparameters that require costly hyperparameter optimization. Our approach uses a Transformer-based subgraph encoding module only; we introduce connection-biased attention and entity role embeddings into the subgraph encoding module to eliminate the need for an expensive and time-consuming path encoding module. Evaluations on standard inductive KG completion benchmark datasets demonstrate that our Connection-Biased Link Prediction (CBLiP) model has superior performance to models that do not use path information. Compared to models that utilize path information, CBLiP shows competitive or superior performance while being faster. Additionally, to show that the effectiveness of connection-biased attention and entity role embeddings also holds in the transductive setting, we compare CBLiP's performance on the relation prediction task in the transductive setting.

With the development of large language models (LLMs), numerous online applications based on these models have emerged. As system prompts significantly influence the performance of LLMs, many such applications conceal their system prompts and regard them as intellectual property. Consequently, numerous efforts have been made to steal these system prompts. However, for applications that do not publicly disclose their system prompts, previously stolen prompts have low confidence. This is because previous methods rely on confirmation from application developers, which is unrealistic since developers may be unwilling to acknowledge that their system prompts have been leaked. We observed a phenomenon: when an LLM performs repetitive tasks, it accurately repeats based on the context rather than relying on its internal model parameters. We validated this phenomenon by comparing the results of two different inputs—repetitive tasks and knowledge-based tasks—under conditions of normal execution, contaminated execution, and partially restored execution. By contaminating the input nouns and then partially restoring them using data from the normal execution's intermediate layers, we measured the accuracies of both task types across these three execution processes. Based on this phenomenon, we propose a high-confidence leakage method called RepeatLeakage. By specifying the range that the model needs to repeat and encouraging the model not to change the format, we manage to extract its system prompt and conversation contexts. We validated the repetition phenomenon on multiple open-source models and successfully designed prompts using RepeatLeakage to leak contents from the actual system prompts of GPT-Store and publicly available ChatGPT conversation contexts. Finally, we tested RepeatLeakage in real environments such as ChatGPT web, successfully leaking their system prompts and conversation contexts.

Face recognition has made remarkable strides, driven by the expanding scale of datasets, advancements in various backbone and discriminative losses. However, face recognition performance is heavily affected by the label noise, especially closed-set noise. While numerous studies have focused on handling label noise, addressing closed-set noise still poses challenges. This paper identifies this challenge as training isn't robust to noise at the early-stage training, and necessitating an appropriate learning strategy for samples with low confidence, which are often misclassified as closed-set noise in later training phases. To address these issues, we propose a new framework to stabilize the training at early stages and split the samples into clean, ambiguous and noisy groups which are devised with separate training strategies. Initially, we employ generated auxiliary closed-set noisy samples to enable the model to identify noisy data at the early stages of training. Subsequently, we introduce how samples are split into clean, ambiguous and noisy groups by their similarity to the positive and nearest negative centers. Then we perform label fusion for ambiguous samples by incorporating accumulated model predictions. Finally, we apply label smoothing within the closed set, adjusting the label to a point between the nearest negative class and the initially assigned label. Extensive experiments validate the effectiveness of our method on mainstream face datasets, achieving state-of-the-art results.

Object detection in Unmanned Aerial Vehicle (UAV) images has emerged as a focal area of research, which presents two significant challenges: i) objects are typically small and dense within vast images; ii) computational resource constraints render most models unsuitable for real-time deployment. Current real-time object detectors are not optimized for UAV images, and complex methods designed for small object detection often lack real-time capabilities. To address these challenges, we propose a novel detector, RemDet (Reparameter efficient multiplication Detector). Our contributions are as follows: 1) Rethinking the challenges of existing detectors for small and dense UAV images, and proposing information loss as a design guideline for efficient models. 2) We introduce the ChannelC2f module to enhance small object detection performance, demonstrating that high-dimensional representations can effectively mitigate information loss. 3) We design the GatedFFN module to provide not only strong performance but also low latency, effectively addressing the challenges of real-time detection. Our research reveals that GatedFFN, through the use of multiplication, is more cost-effective than feed-forward networks for high-dimensional representation. 4) We propose the CED module, which combines the advantages of ViT and CNN downsampling to effectively reduce information loss. It specifically enhances context information for small and dense objects. Extensive experiments on large UAV datasets, Visdrone and UAVDT, validate the real-time efficiency and superior performance of our methods. On the challenging UAV dataset VisDrone, our methods not only provided state-of-the-art results, improving detection by more than 3.4%, but also achieve 110 FPS on a single 4090.

Unsupervised visible-infrared person re-identification (USL-VI-ReID) is of great research and practical significance yet remains challenging due to the absence of annotations. Existing approaches aim to learn modality-invariant representations in an unsupervised setting. However, these methods often encounter label noise within and across modalities due to suboptimal clustering results and considerable modality discrepancies, which impedes effective training. To address these challenges, we propose a straightforward yet effective solution for USL-VI-ReID by mitigating universal label noise using neighbor information. Specifically, we introduce the Neighbor-guided Universal Label Calibration (N-ULC) module, which replaces explicit hard pseudo labels in both homogeneous and heterogeneous spaces with soft labels derived from neighboring samples to reduce label noise. Additionally, we present the Neighbor-guided Dynamic Weighting (N-DW) module to enhance training stability by minimizing the influence of unreliable samples. Extensive experiments on the RegDB and SYSU-MM01 datasets demonstrate that our method outperforms existing USL-VI-ReID approaches, despite its simplicity.

Deploying machine learning (ML) models in high-stakes domains such as healthcare and autonomous systems requires reliable uncertainty quantification (UQ) to ensure safe and accurate decision-making. Conformal prediction (CP) offers a robust, distribution-agnostic framework for UQ, providing valid prediction sets that guarantee a specified coverage probability. However, existing CP methods are often limited by assumptions that are violated in real-world scenarios, such as non-i.i.d. data, and by a lack of integration with modern machine learning workflows, particularly in large generative models. This research aims to address these limitations by advancing CP techniques to operate effectively in non-i.i.d. settings, improving predictive efficiency without sacrificing theoretical guarantees, and integrating CP directly into model training processes. These developments will enhance the practical applicability of CP for a wide range of ML tasks, enabling more reliable and interpretable models in high-stakes applications.

Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the transformations under the specific group. However, the presentation or distribution of real-world data rarely conforms to strict rotational equivariance, commonly referred to as Rotational Symmetry-Breaking (RSB) in the system or dataset, making GConv unable to adapt effectively to this phenomenon. Motivated by this, we propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called G-Biases under the group order to break strict group constraints and then achieve a Relaxed Rotational Equivariant Convolution (RREConv). To validate the efficiency of RREConv, we conduct extensive ablation experiments on the discrete rotational group Cn. Experiments demonstrate that the proposed RREConv-based methods achieve excellent performance compared to existing GConv-based methods in both classification and 2D object detection tasks on the natural image datasets.

The key to semi-supervised semantic segmentation lies in how to fully exploit a large amount of unlabeled data to improve the model’s generalization performance. Most methods are lured into the trap of taking each class independently (i.e., class-independent consistency) and neglecting the fact that there exist semantic dependencies among classes. In this paper, we analyze the bottlenecks of class-independent consistency inherent in previous methods and offer a fresh perspective of cooperative game theory to explicitly encourage class-consensus alignment (i.e., class-consensus consistency between the teacher (weak augmented view) and student network (strong augmented view). We formulate classes as players in an cooperative game to model their interpretable consensus and shed light on the possibility of closer collaboration between consensus themselves and consistency regularization, yielding more comprehensive and effective supervision signals. To this end, we carefully design the class-consensus consistency without introducing any external knowledge to model class structure information which renders better interpretability, and further, prepend relaxed class-consensus consistency (RCC) to unlock the potential of modeling class consensus by relaxing the strict alignment of direct class consensus values to ranking alignment. Extensive experimental results on multiple benchmarks demonstrate that RCC performs favorably against state-of-the-art methods. Particularly in the low-data regimes, RCC achieves significant improvements.

Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in these settings but fail to guarantee the satisfaction of constraints necessary for trustworthy deployment. In contrast, neurosymbolic AI (NeSy) provides a sound formalism to enforce constraints in deep probabilistic models but scales exponentially on sequential problems. To overcome these limitations, we introduce relational neurosymbolic Markov models (NeSy-MMs), a new class of end-to-end differentiable sequential models that integrate and provably satisfy relational logical constraints. We propose a strategy for inference and learning that scales on sequential settings, and that combines approximate Bayesian inference, automated reasoning, and gradient estimation. Our experiments show that NeSy-MMs can solve problems beyond the current state-of-the-art in neurosymbolic AI and still provide strong guarantees with respect to desired properties. Moreover, we show that our models are more interpretable and that constraints can be adapted at test time to out-of-distribution scenarios.

Open-vocabulary Scene Graph Generation (OV-SGG) overcomes the limitations of the closed-set assumption by aligning visual relationship representations with open-vocabulary textual representations. This enables the identification of novel visual relationships, making it applicable to real-world scenarios with diverse relationships. However, existing OV-SGG methods are constrained by fixed text representations, limiting diversity and accuracy in image-text alignment. To address these challenges, we propose the Relation-Aware Hierarchical Prompting (RAHP) framework, which enhances text representation by integrating subject-object and region-specific relation information. Our approach utilizes entity clustering to address the complexity of relation triplet categories, enabling the effective integration of subject-object information. Additionally, we utilize a large language model (LLM) to generate detailed region-aware prompts, capturing fine-grained visual interactions and improving alignment between visual and textual modalities. RAHP also introduces a dynamic selection mechanism within Vision-Language Models (VLMs), which adaptively selects relevant text prompts based on the visual content, reducing noise from irrelevant prompts. Extensive experiments on the Visual Genome and Open Images v6 datasets demonstrate that our framework consistently achieves state-of-the-art performance, demonstrating its effectiveness in addressing the challenges of open-vocabulary scene graph generation.