Association for the Advancement of Artificial Intelligence: AAAI Publications
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Super-Class Guided Transformer for Zero-Shot Attribute Classification
No full textAttribute classification is crucial for identifying specific characteristics within image regions.
Vision-Language Models (VLMs) have been effective in zero-shot tasks by leveraging their general knowledge from large-scale datasets.
Recent studies demonstrate that transformer-based models with class-wise queries can effectively address zero-shot multi-label classification.
However, poor utilization of the relationship between seen and unseen attributes makes the model lack generalizability.
Additionally, attribute classification generally involves many attributes, making maintaining the model’s scalability difficult.
To address these issues, we propose Super-class guided transFormer (SugaFormer), a novel framework that leverages super-classes to enhance scalability and generalizability for zero-shot attribute classification.
SugaFormer employs Super-class Query Initialization (SQI) to reduce the number of queries, utilizing common semantic information from super-classes, and incorporates Multi-context Decoding (MD) to handle diverse visual cues.
To strengthen generalizability, we introduce two knowledge transfer strategies that utilize VLMs.
During training, Super-class guided Consistency Regularization (SCR) aligns model’s features with VLMs using super-class guided prompts, and during inference, Zero-shot Retrieval-based Score Enhancement (ZRSE) refines predictions for unseen attributes.
Extensive experiments demonstrate that SugaFormer achieves state-of-the-art performance across three widely-used attribute classification benchmarks under zero-shot, and cross-dataset transfer settings
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
No full textMonitoring a large population of dynamic processes with limited resources presents a significant challenge across various industrial sectors. This is due to 1) the inherent disparity between the available monitoring resources and the extensive number of processes to be monitored and 2) the unpredictable and heterogeneous dynamics inherent in the progression of these processes. Online learning approaches, commonly referred to as bandit methods, have demonstrated notable potential in addressing this issue by dynamically allocating resources and effectively balancing the exploitation of high-reward processes and the exploration of uncertain ones. However, most online learning algorithms are designed for 1) a centralized setting that requires data sharing across processes for accurate predictions or 2) a homogeneity assumption that estimates a single global model from decentralized data. To overcome these limitations and enable online learning in a heterogeneous population under a decentralized setting, we propose a federated collaborative online monitoring method. Our approach utilizes representation learning to capture the latent representative models within the population and introduces a novel federated collaborative UCB algorithm to estimate these models from sequentially observed decentralized data. This strategy facilitates informed monitoring of resource allocation. The efficacy of our method is demonstrated through theoretical analysis, simulation studies, and its application to decentralized cognitive degradation monitoring in Alzheimer’s disease
CoffeeBoost: Gradient Boosting Native Conformal Inference for Bayesian Optimization
No full textBayesian optimization (BO) is a key technique for solving black-box optimization problems. This study extends the scope of BO from conventional applications (e.g., AutoML and robotics learning) to automated tuning of software systems. Despite GP (Gaussian Process) implementing a foundation formalism for exploitation and exploration in BO, its limited predictive power and unrealistic assumptions (e.g., continuity and Gaussianity) can severely affect its effectiveness and efficiency in tuning complex software systems. To overcome these limitations, we propose a BO framework CoffeeBoost, which implements exploitation and exploration with a GBDT-native distribution-free probabilistic surrogate model. CoffeeBoost constructs surrogate models via stochastic gradient boosting ensembles (SGBE) and quantifies probabilistic distributions via distribution-free conformal predictive systems. Moreover, CoffeeBoost leverages the residual paths in SGBE to improve the local adaptiveness of the resulting predictive distributions in a GBDT-native manner. Across eight auto-tuning benchmarks for database management systems (DBMS), we evaluate CoffeeBoost and show its superior learnability and optimizability against existing GP-based and tree-ensemble-based BO schemes. Detailed analysis further shows CoffeeBoost's predictive distributions excel in both coverage and tightness
DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation
No full textIn social networks, people influence each other through social links, which can be represented as propagation among nodes in graphs. Influence minimization (IMIN) is the problem of manipulating the structures of an input graph (e.g., removing edges) to reduce the propagation among nodes. IMIN can represent time-critical real-world applications, such as rumor blocking, but IMIN is theoretically difficult and computationally expensive. Moreover, the discrete nature of IMIN hinders the usage of powerful machine learning techniques, which requires differentiable computation. In this work, we propose DiffIM, a novel method for IMIN with two differentiable schemes for acceleration: (1) surrogate modeling for efficient influence estimation, which avoids time-consuming simulations (e.g., Monte Carlo), and (2) the continuous relaxation of decisions, which avoids the evaluation of individual discrete decisions (e.g., removing an edge). We further propose a third accelerating scheme, gradient-driven selection, that chooses edges instantly based on gradients without optimization (spec., gradient descent iterations) on each test instance. Through extensive experiments on real-world graphs, we show that each proposed scheme significantly improves speed with little (or even no) IMIN performance degradation. Our method is Pareto-optimal (i.e., no baseline is faster and more effective than it) and typically several orders of magnitude (spec., up to 15,160X) faster than the most effective baseline, while being more effective
CG-TGAN: Conditional Generative Adversarial Networks with Graph Neural Networks for Tabular Data Synthesizing
No full textData sharing is necessary for AI to be widely used, but sharing sensitive data with others with privacy is risky.
To solve these problems, it is necessary to synthesize realistic tabular data.
In many cases, tabular data contains a mixture of continuous, mixed, categorical columns.
Moreover, columns of the same type may have multimodal distribution or be highly imbalanced.
These issues make it challenging to synthesize tabular data.
The synthesized tabular data should reflect the relational meaning between columns of tabular data, so modeling the probability distribution of tabular data is a non-trivial task.
Traditional tabular data synthesizing models are based on GAN or diffusion models and are built using fully connected or convolutional layers.
However, fully connected layers have the disadvantage of low inductive bias, and convolutional layers are not invariant to the column order of tabular data.
Therefore, we assume that converting tabular data into graph-structured data and using a graph neural network would produce better synthetic data than using fully connected layers or convolutional layers.
Our study aims to show that GANs constructed with graph neural networks can outperform existing GAN models using fully connected layers or convolutional layers.
We propose CG-TGAN, a conditional GAN built using graph neural networks. To learn how to synthesize realistic data, the graph neural networks in the discriminator and generator learn graph-level tasks and node-level tasks together.
The discriminator of CG-TGAN learns a graph-level task to distinguish between real and synthetic data and node-level tasks to predict the value of the target node.
CG-TGAN’s generator learns a graph-level task to synthesize an overall graph similar to real data and node-level tasks to learn how to synthesize a fake graph with the proper relation between nodes.
In this paper, we show that CG-TGAN outperforms GAN-based models and is comparable to diffusion-based models
Real-Time Recurrent Reinforcement Learning
No full textWe introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs).
The proposed algorithm combines three integral parts: (1) A Meta-RL architecture, resembling the mammalian basal ganglia; (2) A biologically plausible reinforcement learning algorithm, exploiting temporal difference learning and eligibility traces to train the policy and the value-function; (3) An online automatic differentiation algorithm for computing the gradients with respect to parameters of a shared recurrent network backbone. Our experimental results show that the method is capable of solving a diverse set of partially observable reinforcement learning tasks. The algorithm we call real-time recurrent reinforcement learning (RTRRL) serves as a model of learning in biological neural networks, mimicking reward pathways in the basal ganglia
HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning
No full textOne-shot methods have significantly advanced the field of neural architecture search (NAS) by adopting weight-sharing strategy to reduce search costs. However, the accuracy of performance estimation can be compromised by co-adaptation. Few-shot methods divide the entire supernet into individual sub-supernets by splitting edge by edge to alleviate this issue, yet neglect relationships among edges and result in performance degradation on huge search space. In this paper, we introduce HEP-NAS, a hierarchy-wise partition algorithm designed to further enhance accuracy. To begin with, HEP-NAS treats edges sharing the same end node as a hierarchy, permuting and splitting edges within the same hierarchy to directly search for the optimal operation combination for each intermediate node. This approach aligns more closely with the ultimate goal of NAS. Furthermore, HEP-NAS selects the most promising sub-supernet after each segmentation, progressively narrowing the search space in which the optimal architecture may exist. To improve performance evaluation of sub-supernets, HEP-NAS employs search space mutual distillation, stabilizing the training process and accelerating the convergence of each individual sub-supernet. Within a given budget, HEP-NAS enables the splitting of all edges and gradually searches for architectures with higher accuracy. Experimental results across various datasets and search spaces demonstrate the superiority of HEP-NAS compared to state-of-the-art methods
On the Power of Convolution-Augmented Transformer
No full textThe transformer architecture has catalyzed revolutionary advances in language modeling. However, recent architectural recipes, such as state-space models, have bridged the performance gap. Motivated by this, we examine the benefits of Convolution-Augmented Transformer (CAT) for recall, copying, and length generalization tasks. CAT incorporates convolutional filters in the K/Q/V embeddings of an attention layer. Through CAT, we show that the locality of the convolution synergizes with the global view of the attention. Unlike comparable architectures, such as Mamba or transformer, CAT can provably solve the associative recall (AR) and copying tasks using a single layer while also enjoying guaranteed length generalization. We also establish computational tradeoffs between convolution and attention by characterizing how convolution can mitigate the need for full attention by summarizing the context window and creating salient summary tokens to attend. Evaluations on real datasets corroborate our findings and demonstrate that CAT and its variations indeed enhance the language modeling performance
Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
No full textHigh-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the source code into an FPGA circuit. The source code includes a program (called ``kernel'') and several pragmas that instruct hardware synthesis, such as parallelization, pipeline, etc. While it is relatively easy for software developers to design the program, it heavily relies on hardware knowledge to design the pragmas, posing a big challenge for software developers. Recently, different machine learning algorithms, such as GNNs, have been proposed to automate the pragma design via performance prediction. However, when applying the trained model on new kernels, the significant domain shift often leads to unsatisfactory performance. We propose a more domain-generalizable model structure: a two-level hierarchical Mixture of Experts (MoE), that can be flexibly adapted to any GNN model. Different expert networks can learn to deal with different regions in the representation space, and they can utilize similar patterns between the old kernels and new kernels. In the low-level MoE, we apply MoE on three natural granularities of a program: node, basic block, and graph. The high-level MoE learns to aggregate the three granularities for the final decision. To stably train the hierarchical MoE, we further propose a two-stage training method. Extensive experiments verify the effectiveness of the hierarchical MoE
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
No full textDespite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed EBS-CFL supports effectively training CFL while maintaining users' cluster identity confidentially. Moreover, it detects potential poisonous attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. EBS-CFL has high efficiency with client-side overhead O(ml + m^2) for communication and O(m^2l) for computation, where m is the number of cluster identities, and l is the gradient size. When m = 1, EBS-CFL's computational efficiency of client is at least O(log(n)) times better than comparison schemes, where n is the number of clients. In addition, we validate the scheme through extensive experiments. Finally, we theoretically prove the scheme's security