IJCAI 2023 Papers

With the increased integration of AI technologies in human decision making processes, adversarial attacks on AI models become a greater concern than ever before as they may significantly hurt humans’ trust in AI models and decrease the effectiveness of human-AI collaboration. While many adversarial attack methods have been proposed to decrease the performance of an AI model, limited attention has been paid on understanding how these attacks will impact the human decision makers interacting with the model, and accordingly, how to strategically deploy adversarial attacks to maximize the reduction of human trust and reliance. In this paper, through a human-subject experiment, we first show that in AI-assisted decision making, the timing of the attacks largely influences how much humans decrease their trust in and reliance on AI—the decrease is particularly salient when attacks occur on decision making tasks that humans are highly confident themselves. Based on these insights, we next propose an algorithmic framework to infer the human decision maker’s hidden trust in the AI model and dynamically decide when the attacker should launch an attack to the model. Our evaluations show that following the proposed approach, attackers deploy more efficient attacks and achieve higher utility than adopting other baseline strategies.

Typical RL-for-finance solutions directly optimize trading policies over the noisy market data, such as stock prices and trading volumes, without explicitly considering the future trends and correlations of different investment assets as we humans do. In this paper, we present StockFormer, a hybrid trading machine that integrates the forward modeling capabilities of predictive coding with the advantages of RL agents in policy flexibility. The predictive coding part consists of three Transformer branches with modified structures, which respectively extract effective latent states of long-/short-term future dynamics and asset relations. The RL agent adaptively fuses these states and then executes an actor-critic algorithm in the unified state space. The entire model is jointly trained by propagating the critic's gradients back to the predictive coding module. StockFormer significantly outperforms existing approaches across three publicly available financial datasets in terms of portfolio returns and Sharpe ratios.

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update is a key component in multi-objective EAs (MOEAs), and it is performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied bi-objective problem, OneJumpZeroJump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. This work is an attempt to challenge a common practice for the population update in MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.

Deep neural networks have shown promising results on a wide variety of tasks using large-scale and well-annotated training datasets. However, data collected from real-world applications can suffer from two prevalent biases, i.e., long-tailed class distribution and label noise. Previous efforts on long-tailed learning and label-noise learning can only address a single type of data bias, leading to a severe deterioration of their performance. In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. With SFA, we detect noisy samples based on their distances to class centroids sampled from this Gaussian distribution. Based on the identified clean samples, we then propose to train an auxiliary balanced classifier to improve the generalization for the minority class and facilitate the update of Gaussian parameters. Extensive experimental results show that SFA can enhance the performance of existing methods on both simulated and real-world datasets. Further, we propose to combine SFA with the sample-selection approach, distribution-robust, and noise-robust loss functions, resulting in significant improvement in performance over the baselines. Our code is available at https://github.com/HotanLee/SFA

A concept-based classifier can explain the decision process of a deep learning model by human understandable concepts in image classification problems. However, sometimes concept-based explanations may cause false positives, which misregards unrelated concepts as important for the prediction task. Our goal is to find the statistically significant concept for classification to prevent misinterpretation. In this study, we propose a method using a deep learning model to learn the image concept and then using the knockoff sample to select the important concepts for prediction by controlling the False Discovery Rate (FDR) under a certain value. We evaluate the proposed method in our experiments on both synthetic and real data. Also, it shows that our method can control the FDR properly while selecting highly interpretable concepts to improve the trustworthiness of the model.

Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major concern is safety, in another word, constraint satisfaction. State-wise constraints are one of the most common constraints in real-world applications and one of the most challenging constraints in Safe RL. Enforcing state-wise constraints is necessary and essential to many challenging tasks such as autonomous driving, robot manipulation. This paper provides a comprehensive review of existing approaches that address state-wise constraints in RL. Under the framework of State-wise Constrained Markov Decision Process (SCMDP), we will discuss the connections, differences, and trade-offs of existing approaches in terms of (i) safety guarantee and scalability, (ii) safety and reward performance, and (iii) safety after convergence and during training. We also summarize limitations of current methods and discuss potential future directions.

Modeling and capturing the 3D spatial arrangement of the human and the object is the key to perceiving 3D human-object interaction from monocular images. In this work, we propose to use the Human-Object Offset between anchors which are densely sampled from the surface of human mesh and object mesh to represent human-object spatial relation. Compared with previous works which use contact map or implicit distance filed to encode 3D human-object spatial relations, our method is a simple and efficient way to encode the highly detailed spatial correlation between the human and object. Based on this representation, we propose Stacked Normalizing Flow (StackFLOW) to infer the posterior distribution of human-object spatial relations from the image. During the optimization stage, we finetune the human body pose and object 6D pose by maximizing the likelihood of samples based on this posterior distribution and minimizing the 2D-3D corresponding reprojection loss. Extensive experimental results show that our method achieves impressive results on two challenging benchmarks, BEHAVE and InterCap datasets. Our code has been publicly available at https://github.com/MoChen-bop/StackFLOW.

Graph convolutional networks (GCN) are viewed as one of the most popular representations among the variants of graph neural networks over graph data and have shown powerful performance in empirical experiments. That l2-based graph smoothing enforces the global smoothness of GCN, while (soft) l1-based sparse graph learning tends to promote signal sparsity to trade for discontinuity. This paper aims to quantify the trade-off of GCN between smoothness and sparsity, with the help of a general lp-regularized (1

News-driven quantitative trading (NQT) has been popularly studied in recent years. Most existing NQT methods are performed in a two-step paradigm, i.e., first analyzing markets by a financial prediction task and then making trading decisions, which is doomed to failure due to the nearly futile financial prediction task. To bypass the financial prediction task, in this paper, we focus on reinforcement learning (RL) based NQT paradigm, which leverages news to make profitable trading decisions directly. In this paper, we propose a novel NQT framework SpotlightTrader based on decision trajectory optimization, which can effectively stitch together a continuous and flexible sequence of trading decisions to maximize profits. In addition, we enhance this framework by constructing a spotlight-driven state trajectory that obeys a stochastic process with irregular abrupt jumps caused by spotlight news. Furthermore, in order to adapt to non-stationary financial markets, we propose an effective training pipeline for this framework, which blends offline pretraining with online finetuning to balance exploration and exploitation effectively during online tradings. Extensive experiments on three real-world datasets demonstrate our proposed model’s superiority over the state-of-the-art NQT methods.

Spiking Neural Networks (SNNs) are the promising models of neuromorphic vision recognition. The mean square error (MSE) and cross-entropy (CE) losses are widely applied to supervise the training of SNNs on neuromorphic datasets. However, the relevance between the output spike counts and predictions is not well modeled by the existing loss functions. This paper proposes a Spike Count Maximization (SCM) training approach for the SNN-based neuromorphic vision recognition model based on optimizing the output spike counts. The SCM is achieved by structural risk minimization (SRM) and a specially designed spike counting loss. The spike counting loss counts the output spikes of the SNN by using the L0-norm, and the SRM maximizes the distance between the margin boundaries of the classifier to ensure the generalization of the model. The SCM is non-smooth and non-differentiable, and we design a two-stage algorithm with fast convergence to solve the problem. Experiment results demonstrate that the SCM performs satisfactorily in most cases. Using the output spikes for prediction, the accuracies of SCM are 2.12%~16.50% higher than the popular training losses on the CIFAR10-DVS dataset. The code is available at https://github.com/TJXTT/SCM-SNN.

Object detection on panoramic/spherical images has been developed rapidly in the past few years, where IoU-calculator is a fundamental part of various detector components, i.e. Label Assignment, Loss and NMS. Due to the low efficiency and non-differentiability of spherical Unbiased IoU, spherical approximate IoU methods have been proposed recently. We find that the key of these approximate methods is to map spherical boxes to planar boxes. However, there exists two problems in these methods: (1) they do not eliminate the influence of panoramic image distortion; (2) they break the original pose between bounding boxes. They lead to the low accuracy of these methods. Taking the two problems into account, we propose a new sphere-plane boxes transform, called Sph2Pob. Based on the Sph2Pob, we propose (1) an differentiable IoU, Sph2Pob-IoU, for spherical boxes with low time-cost and high accuracy and (2) an agent Loss, Sph2Pob-Loss, for spherical detection with high flexibility and expansibility. Extensive experiments verify the effectiveness and generality of our approaches, and Sph2Pob-IoU and Sph2Pob-Loss together boost the performance of spherical detectors. The source code is available at https://github.com/AntXinyuan/sph2pob.

Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the acceleration of multi-objective (MO) optimization becomes ever more important. Despite the significant body of work on meta-learning for HPO, existing methods are inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE’s acquisition function to the meta-learning setting using a task similarity defined by the overlap of top domains between tasks. We also theoretically analyze and address the limitations of our task similarity. In the experiments, we demonstrate that our method speeds up MO-TPE on tabular HPO benchmarks and attains state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on “Multiobjective Hyperparameter Optimization for Transformers”. See https://arxiv.org/abs/2212.06751 for the latest version with Appendix.

Machine-learning models are becoming increasingly prevalent in our lives, for instance assisting in image-classification or decision-making tasks. Consequently, the reliability of these models is of critical importance and has resulted in the development of numerous approaches for validating and verifying their robustness and fairness. However, beyond such specific properties, it is challenging to specify, let alone check, general functional-correctness expectations from models. In this paper, we take inspiration from specifications used in formal methods, expressing functional-correctness properties by reasoning about k different executions---so-called k-safety properties. Considering a credit-screening model of a bank, the expected property that "if a person is denied a loan and their income decreases, they should still be denied the loan" is a 2-safety property. Here, we show the wide applicability of k-safety properties for machine-learning models and present the first specification language for expressing them. We also operationalize the language in a framework for automatically validating such properties using metamorphic testing. Our experiments show that our framework is effective in identifying property violations, and that detected bugs could be used to train better models.

Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks. In this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the computational efficiency and meantime maintain or increase accuracy for lesion segmentation. SCP relaxes the spatial invariance constraint imposed by convolutional operations and optimizes an underlying implicit function that maps image coordinates to network weights, the parameters of which are obtained along with the backbone network training and later used for generating network weights to capture spatially covariant contextual information. We demonstrate the effectiveness and efficiency of the proposed SCP using two lesion segmentation tasks from different imaging modalities: white matter hyperintensity segmentation in magnetic resonance imaging and liver tumor segmentation in contrast-enhanced abdominal computerized tomography. The network using SCP has achieved 23.8, 64.9 and 74.7 reduction in GPU memory usage, FLOPs, and network size with similar or better accuracy for lesion segmentation.

Optical flow estimation have shown significant improvements with advances in deep neural networks. However, these flow networks have recently been shown to be vulnerable to patch-based adversarial attacks, which poses security risks in real-world applications, such as self-driving cars and robotics. We propose SADL, a Spatially constrained adversarial Attack Detection and Localization framework, to detect and localize these patch-based attack without requiring a dedicated training. The detection of an attacked input sequence is performed via iterative optimization on the features from the inner layers of flow networks, without any prior knowledge of the attacks. The novel spatially constrained optimization ensures that the detected anomalous subset of features comes from a local region. To this end, SADL provides a subset of nodes within a spatial neighborhood that contribute more to the detection, which will be utilized to localize the attack in the input sequence. The proposed SADL is validated across multiple datasets and flow networks. With patch attacks 4.8% of the size of the input image resolution on RAFT, our method successfully detects and localizes them with an average precision of 0.946 and 0.951 for KITTI-2015 and MPI-Sintel datasets, respectively. The results show that SADL consistently achieves higher detection rates than existing methods and provides new localization capabilities.

The brain-inspired spiking neural networks (SNNs) are receiving increasing attention due to their asynchronous event-driven characteristics and low power consumption. As attention mechanisms recently become an indispensable part of sequence dependence modeling, the combination of SNNs and attention mechanisms holds great potential for energy-efficient and high-performance computing paradigms. However, the existing works cannot benefit from both temporal-wise attention and the asynchronous characteristic of SNNs. To fully leverage the advantages of both SNNs and attention mechanisms, we propose an SNNs-based spatial-temporal self-attention (STSA) mechanism, which calculates the feature dependence across the time and space domains without destroying the asynchronous transmission properties of SNNs. To further improve the performance, we also propose a spatial-temporal relative position bias (STRPB) for STSA to consider the spatiotemporal position of spikes. Based on the STSA and STRPB, we construct a spatial-temporal spiking Transformer framework, named STS-Transformer, which is powerful and enables SNNs to work in an asynchronous event-driven manner. Extensive experiments are conducted on popular neuromorphic datasets and speech datasets, including DVS128 Gesture, CIFAR10-DVS, and Google Speech Commands, and our experimental results can outperform other state-of-the-art models.

More and more information is shared on the web or on social media platforms, and the information provided can be conflicting. In this case, we need to decide which information is reliable and should be taken into account. We want to define measures of reliability for each source that provides information, but also to find the truth among the conflicting information, and propose properties of these measures.

Learning-augmented algorithms have been attracting increasing interest, but have only recently been considered in the setting of explorable uncertainty where precise values of uncertain input elements can be obtained by a query and the goal is to minimize the number of queries needed to solve a problem. We study learning-augmented algorithms for sorting and hypergraph orientation under uncertainty, assuming access to untrusted predictions for the uncertain values. Our algorithms provide improved performance guarantees for accurate predictions while maintaining worst-case guarantees that are best possible without predictions. For sorting, our algorithm uses the optimal number of queries for accurate predictions and at most twice the optimal number for arbitrarily wrong predictions. For hypergraph orientation, for any γ≥2, we give an algorithm that uses at most 1+1/γ times the optimal number of queries for accurate predictions and at most γ times the optimal number for arbitrarily wrong predictions. These tradeoffs are the best possible. We also consider different error metrics and show that the performance of our algorithms degrades smoothly with the prediction error in all the cases where this is possible.

The expressivity of Graph Neural Networks (GNNs) is dependent on the aggregation functions they employ. Theoretical works have pointed towards Sum aggregation GNNs subsuming every other GNNs, while certain practical works have observed a clear advantage to using Mean and Max. An examination of the theoretical guarantee identifies two caveats. First, it is size-restricted, that is, the power of every specific GNN is limited to graphs of a specific size. Successfully processing larger graphs may require an other GNN, and so on. Second, it concerns the power to distinguish non-isomorphic graphs, not the power to approximate general functions on graphs, and the former does not necessarily imply the latter. It is desired that a GNN's usability will not be limited to graphs of any specific size. Therefore, we explore the realm of unrestricted-size expressivity. We prove that basic functions, which can be computed exactly by Mean or Max GNNs, are inapproximable by any Sum GNN. We prove that under certain restrictions, every Mean or Max GNN can be approximated by a Sum GNN, but even there, a combination of (Sum, [Mean/Max]) is more expressive than Sum alone. Lastly, we prove further expressivity limitations for GNNs with a broad class of aggregations.