Association for the Advancement of Artificial Intelligence: AAAI Publications
Not a member yet
26155 research outputs found
Sort by
TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization
No full textIn 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
An Enhanced Levenberg--Marquardt Method via Gram Reduction
No full textThis paper studies the problem of solving the system of nonlinear equations.
We propose the Gram-reduced Levenberg--Marquardt method, which reuses the Gram matrix.
Our method has a global convergence guarantee without relying on any step of line-search or solving sub-problems.
We show that our method takes a smaller computational complexity than existing Levenberg--Marquardt methods to find the stationary point of the square norm of the equations.
We also show that the proposed method enjoys a local superlinear convergence rate under the non-degenerate assumption.
Experiments are conducted on real-world applications in scientific computing and machine learning, which validate the efficiency of our method
Pareto Set Learning for Multi-Objective Reinforcement Learning
No full textMulti-objective decision-making problems have emerged in numerous real-world scenarios, such as video games, navigation and robotics. Considering the clear advantages of Reinforcement Learning (RL) in optimizing decision-making processes, researchers have delved into the development of Multi-Objective RL (MORL) methods for solving multi-objective decision problems. However, previous methods either cannot obtain the entire Pareto front, or employ only a single policy network for all the preferences over multiple objectives, which may not produce personalized solutions for each preference. To address these limitations, we propose a novel decomposition-based framework for MORL, Pareto Set Learning for MORL (PSL-MORL), that harnesses the generation capability of hypernetwork to produce the parameters of the policy network for each decomposition weight, generating relatively distinct policies for various scalarized subproblems with high efficiency. PSL-MORL is a general framework, which is compatible for any RL algorithm. The theoretical result guarantees the superiority of the model capacity of PSL-MORL and the optimality of the obtained policy network. Through extensive experiments on diverse benchmarks, we demonstrate the effectiveness of PSL-MORL in achieving dense coverage of the Pareto front, significantly outperforming state-of-the-art MORL methods in both the hypervolume and sparsity indicators
Multi-Objective Molecular Design Through Learning Latent Pareto Set
No full textMolecular design inherently involves the optimization of multiple conflicting objectives, such as enhancing bio-activity and ensuring synthesizability. Evaluating these objectives often requires resource-intensive computations or physical experiments. Current molecular design methodologies typically approximate the Pareto set using a limited number of molecules. In this paper, we present an innovative approach, called Multi-Objective Molecular Design through Learning Latent Pareto Set (MLPS). MLPS initially utilizes an encoder-decoder model to seamlessly transform the discrete chemical space into a continuous latent space. We then employ local Bayesian optimization models to efficiently search for local optimal solutions (i.e., molecules) within predefined trust regions. Using surrogate objective values derived from these local models, we train a global Pareto set learning model to understand the mapping between direction vectors (called “preferences”) in the objective space and the entire Pareto set in the continuous latent space. Both the global Pareto set learning model and local Bayesian optimization models collaborate to discover high-quality solutions and adapt the trust regions dynamically. Our work is an effective endeavor towards learning the Pareto set for multi-objective molecular design, providing decision-makers with the capability to fine-tune their preferences and thoroughly explore the Pareto set. Experimental results demonstrate that MLPS achieves state-of-the-art performance across various multi-objective scenarios, encompassing diverse objective types and varying numbers of objectives. The effectiveness of MLPS was further validated through real-world challenges in discovering antifungal peptides with low toxicity and high activity
Noisy Node Classification by Bi-level Optimization Based Multi-Teacher Distillation
No full textPrevious graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data.
Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality.
Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods
Heterogeneous Multi-Agent Bandits with Parsimonious Hints
No full textWe study a hinted heterogeneous multi-agent multi-armed bandits problem (HMA2B), where agents can query low-cost observations (hints) in addition to pulling arms. In this framework, each of the M agents has a unique reward distribution over K arms, and in T rounds, they can observe the reward of the arm they pull only if no other agent pulls that arm. The goal is to maximize the total utility by querying the minimal necessary hints without pulling arms, achieving time-independent regret. We study HMA2B in both centralized and decentralized setups. Our main centralized algorithm, GP-HCLA, which is an extension of HCLA, uses a central decision-maker for arm-pulling and hint queries, achieving O(M^4 K) regret with O(M K log T) adaptive hints. In decentralized setups, we propose two algorithms, HD-ETC and EBHD-ETC, that allow agents to choose actions independently through collision-based communication and query hints uniformly until stopping, yielding O(M^3 K^2) regret with O(M^3 K log T) hints, where the former requires knowledge of the minimum gap and the latter does not. Finally, we establish lower bounds to prove the optimality of our results and verify them through numerical simulations
Decoupled Policy Actor-Critic: Bridging Pessimism and Risk Awareness in Reinforcement Learning
No full textActor-Critic (AC) algorithms like SAC and TD3 were shown to perform well in a variety of continuous-action tasks. However, the theoretical basis for the pessimistic objectives these algorithms employ remains unestablished, raising questions about the specific class of policies they are implementing. In this work, we apply the expected utility hypothesis, a fundamental concept in economics, to illustrate that both pessimistic and non-pessimistic RL objectives can be interpreted through expected utility maximization using an exponential utility function. This approach reveals that pessimistic policies effectively maximize value certainty equivalent, aligning them with the optimization of risk-aware objectives. Furthermore, we propose Decoupled Policy Actor-Critic (DAC). DAC is a model-free algorithm that features two distinct actor networks: a pessimistic actor for temporal-difference learning and an optimistic actor for exploration. Our evaluations of DAC across various locomotion and manipulation tasks demonstrate improvements in sample efficiency and final performance. Remarkably, DAC, while requiring significantly fewer computational resources, matches the performance of leading model-based methods in the complex dog and humanoid domains
Federated Unlearning with Gradient Descent and Conflict Mitigation
No full textFederated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients' local data. Therefore, it’s necessary to effectively remove the target client's data from the FL global model to ease the risk of privacy leakage and implement "the right to be forgotten". Federated Unlearning (FU) has been considered a promising solution to remove data without full retraining. But the model utility easily suffers significant reduction during unlearning due to the gradient conflicts. Furthermore, when conducting the post-training to recovery the model utility, it’s prone to move back and revert what have already been unlearned. To address these issues, we propose Federated Unlearning with Orthogonal Steepest Descent (FedOSD). We first design an unlearning cross entropy loss to overcome the convergence issue of the gradient ascent. A steepest descent direction for unlearning is then calculated in the condition of being non-conflicting with other clients’ gradients and closest to the target client's gradient. This benefits to efficiently unlearn and mitigate the model utility reduction. After unlearning, we recover the model utility by maintaining the achievement of unlearning. Finally, extensive experiments in several FL scenarios verify that FedOSD outperforms the SOTA FU algorithms in terms of unlearning and the model utility
Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language Models
No full textMultimodal large language models (MLLMs) can simultaneously process visual, textual, and auditory data, capturing insights that complement human analysis.
However, existing video question-answering (VidQA) benchmarks and datasets often exhibit a bias toward a single modality, despite the goal of requiring advanced reasoning skills that integrate diverse modalities to answer the queries.
In this work, we introduce the modality importance score (MIS) to identify such bias. It is designed to assess which modality embeds the necessary information to answer the question.
Additionally, we propose an innovative method using state-of-the-art MLLMs to estimate the modality importance, which can serve as a proxy for human judgments of modality perception.
With this MIS, we demonstrate the presence of unimodal bias and the scarcity of genuinely multimodal questions in existing datasets.
We further validate the modality importance score with multiple ablation studies to evaluate the performance of MLLMs on permuted feature sets.
Our results indicate that current models do not effectively integrate information due to modality imbalance in existing datasets.
Our proposed MLLM-derived MIS can guide the curation of modality-balanced datasets that advance multimodal learning and enhance MLLMs' capabilities to understand and utilize synergistic relations across modalities
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning
No full textGraph contrastive learning has emerged as a powerful technique for learning graph representations that are robust and discriminative. However, traditional approaches often neglect the critical role of subgraph structures, particularly the intra-subgraph characteristics and inter-subgraph relationships, which are crucial for generating informative and diverse contrastive pairs. These subgraph features are crucial as they vary significantly across different graph types, such as social networks where they represent communities, and biochemical networks where they symbolize molecular interactions. To address this issue, our work proposes a novel subgraph-oriented learnable augmentation method for graph contrastive learning, termed SOLA-GCL, that centers around subgraphs, taking full advantage of the subgraph information for data augmentation. Specifically, SOLA-GCL initially partitions a graph into multiple densely connected subgraphs based on their intrinsic properties. To preserve and enhance the unique characteristics inherent to subgraphs, a graph view generator optimizes augmentation strategies for each subgraph, thereby generating tailored views for graph contrastive learning. This generator uses a combination of intra-subgraph and inter-subgraph augmentation strategies, including node dropping, feature masking, intra-edge perturbation, inter-edge perturbation, and subgraph swapping. Extensive experiments have been conducted on various graph learning applications, ranging from social networks to molecules, under semi-supervised learning, unsupervised learning, and transfer learning settings to demonstrate the superiority of our proposed approach