IJCAI 2024 论文列表

Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the expensive pixel-level annotation cost of medical images. However, most existing methods suffer from unreliable uncertainty assessment and the struggle to balance diversity and informativeness, leading to poor performance in segmentation tasks. In response, we propose an efficient Predictive Accuracy-based Active Learning (PAAL) method for medical image segmentation, first introducing predictive accuracy to define uncertainty. Specifically, PAAL mainly consists of an Accuracy Predictor (AP) and a Weighted Polling Strategy (WPS). The former is an attached learnable module that can accurately predict the segmentation accuracy of unlabeled samples relative to the target model with the predicted posterior probability. The latter provides an efficient hybrid querying scheme by combining predicted accuracy and feature representation, aiming to ensure the uncertainty and diversity of the acquired samples. Extensive experiment results on multiple datasets demonstrate the superiority of PAAL. PAAL achieves comparable accuracy to fully annotated data while reducing annotation costs by approximately 50% to 80%, showcasing significant potential in clinical applications. The code is available at https://github.com/shijun18/PAAL-MedSeg.

Urban migration is a significant aspect of a city's economy. The exploration of the underlying determinants of housing purchases among current residents contributes to the study of future trends in urban migration, enabling governments to formulate appropriate policies to guide future economic growth. This article employs a factor model to analyze data on residents' rentals, first-time home purchases, and subsequent housing upgrades. We decompose the factors influencing housing purchases into common drivers and specific drivers. Our hypothesis is that common drivers reflect universal social patterns, while personalized drivers represent stochastic elements. We construct a correlation matrix capturing the inter-resident relationships based on the common drivers of housing purchases. We then propose a graph neural network based on the correlation matrix to model housing predictions as a node classification problem. Our model addresses two critical questions. Firstly, we aim to identify which part of rental residents will engage in first-time home purchases in the future. Secondly, we seek to determine which group of residents, having completed rental and first-time home purchases, will opt for a second home purchase. The results of our testing on real-world datasets demonstrate that based solely on rental and home purchase records, we can achieve a sensitivity for housing predictions exceeding 80%.

The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion and the corresponding social problems, which is a pressing issue in densely populated cities like Singapore. In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains. The contributions in this paper are listed as follows: (1) A New Dataset: We introduce the SINPA dataset, containing a year's worth of PA data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors. (2) A Data-Driven Approach: We present DeepPA, a novel deep-learning framework, to collectively and efficiently predict future PA across thousands of parking lots. (3) Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Furthermore, we implement DeepPA in a practical web-based platform to provide real-time PA predictions to aid drivers and inform urban planning for the governors in Singapore. We release the dataset and source code at https://github.com/yoshall/SINPA.

In numerous user-centric services on mobile applications (apps), accurately mining user interests and generating effective user representations are paramount. Traditional approaches, which often involve training task-specific user representations, are becoming increasingly impractical due to their high computational costs and limited adaptability. This paper introduces a novel solution to this challenge: the Multi-type App-usage Fusion Network (MAFN). MAFN innovatively pre-trains universal user representations, leveraging multi-type app behaviors to overcome key limitations in existing methods. We address two primary challenges: 1) the varying frequency of user behaviors (ranging from low-frequency actions like (un)installations to high-frequency yet insightful app launches); and 2) the integration of multi-type behaviors to form a cohesive representation. Our approach involves the creation of novel pre-training tasks that harness self-supervised signals from diverse app behaviors, capturing both long-term and short-term user interests. MAFN's unique fusion approach effectively amalgamates these interests into a unified vector space, facilitating the development of a versatile, general-purpose user representation. With a practical workflow, extensive experiments with three typical downstream tasks on real-world datasets verify the effectiveness of our approach.

Occupational skill demand (OSD) forecasting seeks to predict dynamic skill demand specific to occupations, beneficial for employees and employers to grasp occupational nature and maintain a competitive edge in the rapidly evolving labor market. Although recent research has proposed data-driven techniques for forecasting skill demand, the focus has remained predominantly on overall trends rather than occupational granularity. In this paper, we propose a novel Pre-training Enhanced Dynamic Graph Autoencoder (Pre-DyGAE), forecasting skill demand from an occupational perspective. Specifically, we aggregate job descriptions (JDs) by occupation and segment them into several timestamps. Subsequently, in the initial timestamps, we pre-train a graph autoencoder (GAE), consisting of a semantically-aware cross-attention enhanced uncertainty-aware encoder and decoders for link prediction and edge regression to achieve graph reconstruction. In particular, we utilize contrastive learning on skill cooccurrence clusters to solve the data sparsity and a unified Tweedie and ranking loss for predicting the imbalanced distribution. Afterward, we incorporate an adaptive temporal encoding unit and a temporal shift module into GAE to achieve a dynamic GAE (DyGAE). Furthermore, we fine-tune the DyGAE with a two-stage optimization strategy and infer future representations. Extensive experiments on four real-world datasets validate the effectiveness of Pre-DyGAE compared with state-of-the-art baselines.

The high communication cost is a major challenge in the federated learning (FL) training process. Several methods have been proposed to reduce communication costs on the uplink channel, primarily sparsification-based methods, which have overlooked the impact of downlink channels. However, model accuracy and communication cost issues arise when applying them in practical FL applications, especially when the bandwidth is limited both on the uplink and downlink channels. In this paper, we propose a novel secure-FL-compatible hybrid gradient compression framework (HGC) that handles both uplink and downlink communication. Specifically, HGC identifies and exploits three types of redundancies in the FL training process. With proposed optimization methods based on compression ratio correction and dynamic momentum correction, HGC improves the trade-off between communication cost and model performance. The extensive theoretical and empirical analysis demonstrates the effectiveness of our framework in achieving a high compression ratio for both uplink and downlink communications with negligible loss of model accuracy, surpassing the state-of-the-art compression methods.

Given a directed graph, a set of source nodes, a target node and a budget, we study the problem of maximizing the number of source nodes disconnected from the target node by removing edges not exceeding the budget. Our model is mainly motivated by a cyber security use case where we need to minimize the attack surface of a Windows Active Directory system. In these high-profile attacks, the attackers first compromise a source (i.e., a compromised user node) and then laterally move to a destination (i.e., a high-privileged admin node). Our aim is to minimize the number of users with a path to the admin. We first prove that the problem is NP-hard. Algorithms for exact optimality usually struggle to converge on graphs that approach real-world network scales and therefore are not practical for usage. In light of this, we study anytime algorithms that return an acceptable result whenever the algorithm is terminated, and can improve optimality by allowing longer computational time. We observe the source connectivity of directed graphs, based on which we propose a novel anytime algorithm---the spiral algorithm. We also develop two Monte Carlo Tree Search (MCTS) algorithms as a baseline to study the performance of typical anytime algorithms for our problem, and show that the spiral algorithm improves the optimality at a significantly faster speed and therefore exhibits better anytime behavior compared with MCTS.

The core of the dialogue system is to generate relevant, informative, and human-like responses based on extensive dialogue history. Recently, dialogue generation domain has seen mainstream adoption of large language models (LLMs), due to its powerful capability in generating utterances. However, there is a natural deficiency for such models, that is, inherent position bias, which may lead them to pay more attention to the nearby utterances instead of causally relevant ones, resulting in generating irrelevant and generic responses in long-term dialogue. To alleviate such problem, in this paper, we propose a novel method, named Causal Perception long-term Dialogue framework (CPD), which employs perturbation-based causal variable discovery method to extract casually relevant utterances from the dialogue history and enhances model causal perception during fine-tuning. Specifically, a local-position awareness method is proposed in CPD for inter-sentence position correlation elimination, which helps models extract causally relevant utterances based on perturbations. Then, a casual-perception fine-tuning strategy is also proposed, to enhance the capability of discovering the causal invariant factors, by differently perturbing causally relevant and non-casually relevant ones for response generation. Experimental results on two datasets prove that our proposed method can effectively alleviate the position bias for multiple LLMs and achieve significant progress compared with existing baselines.

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning problems. Although population-based learning shows its potential in producing diverse behaviors, most previous works still focus on improving the exploration of a single joint policy. In this paper, we show that with a suitable exploration method, maintaining a population of joint policies rather than one joint policy can significantly improve exploration. Our key idea is to guide each member of the population to explore different regions of the environment. To this end, we propose a member-aware exploration objective which explicitly guides each member to maximize deviation from the explored regions of other members, thus forcing them to explore different regions. In addition, we further propose an exploration-enhanced policy constraint to guide each member to learn a joint policy that is both different from other members and promotes exploration, thus increasing the probability of exploring different regions. Under reward-free setting, our method achieves 72% average improvement in the number of explored states compared to classical exploration methods in the multiple-particle environment. Moreover, under sparse-reward setting, we show that the proposed method significantly outperforms the state-of-the-art methods in the multiple-particle environment, the Google Research Football, and StarCraft II micromanagement tasks.

We study the Popular Matching (PM) problem in multiple models, where the preferences of the agents in the instance may change or may be unknown or uncertain. In particular, we study an Uncertainty model, where each agent has a possible set of preference lists, a Multilayer model, where there are layers of preference profiles, and a Robust popularity model, where any agent may move some other agents up or down some places in his preference list. Our goal is always to find a matching that is popular in any possible preference profile. We study both one-sided (only one class of the agents have preferences) and two-sided bipartite markets. In the one-sided model, we show that all our problems can be solved in polynomial time by utilizing the structure of popular matchings. We also obtain nice structural results. With two-sided preferences, we show that all three above models lead to NP-hard questions for popular matchings. By using the connection between dominant matchings and stable matchings, we show that in the robust and uncertainty models, a certainly dominant matching in all possible preference profiles can be found in polynomial time, whereas in the multilayer model, the problem remains NP-hard for dominant matchings too. We also answer an open question about d-robust stable matchings.

The Robust Stable Matching (RSM) problem involves finding a stable matching that allows getting another stable matching within a minimum number of changes when a pair becomes forbidden. It has been shown that such a problem is NP-Hard. In this paper, we enrich the mathematical model for the RSM problem based on new theoretical properties. We derive from these properties new polynomial time pre-solving algorithms which both reduce the search space and speed up the exploration. We also extend our results to the instances of the Many-to-Many problem and give its corresponding constraint programming (CP) model. We show how the use of our algorithms improve the state-of-the-art results and make it possible to obtain proofs of optimality on large instances via the CP model.

Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the large number of strategies, precluding direct application to more complex scenarios. This survey provides a comprehensive overview of a framework for large games, known as Policy Space Response Oracles (PSRO), which holds promise to improve scalability by focusing attention on sufficient subsets of strategies. We first motivate PSRO and provide historical context. We then focus on the strategy exploration problem for PSRO: the challenge of assembling effective subsets of strategies that still represent the original game well with minimum computational cost. We survey current research directions for enhancing the efficiency of PSRO, and explore the applications of PSRO across various domains. We conclude by discussing open questions and future research.

Despite considerable progress being achieved in point cloud geometry compression, there still remains a challenge in effectively compressing large-scale scenes with sparse surfaces. Another key challenge lies in reducing decoding latency, a crucial requirement in real-world application. In this paper, we propose Pointsoup, an efficient learning-based geometry codec that attains high-performance and extremely low-decoding-latency simultaneously. Inspired by conventional Trisoup codec, a point model-based strategy is devised to characterize local surfaces. Specifically, skin features are embedded from local windows via an attention-based encoder, and dilated windows are introduced as cross-scale priors to infer the distribution of quantized features in parallel. During decoding, features undergo fast refinement, followed by a folding-based point generator that reconstructs point coordinates with fairly fast speed. Experiments show that Pointsoup achieves state-of-the-art performance on multiple benchmarks with significantly lower decoding complexity, i.e., up to 90~160× faster than the G-PCCv23 Trisoup decoder on a comparatively low-end platform (e.g., one RTX 2080Ti). Furthermore, it offers variable-rate control with a single neural model (2.9MB), which is attractive for industrial practitioners.

The success of contrastive learning models like CLIP, known for aligning 2D image-text pairs, has inspired the development of triplet alignment for Large 3D Point Cloud Models (3D-PCM). Examples like ULIP integrate images, text, and point clouds into a unified semantic space. However, despite showing impressive zero-shot capabilities, frozen 3D-PCM still falls short compared to fine-tuned methods, especially when downstream 3D datasets are significantly different from upstream data. Addressing this, we propose a Data-Efficient, Training-Free 3D Adaptation method named PointTFA that adjusts ULIP outputs with representative samples. PointTFA comprises the Representative Memory Cache (RMC) for selecting a representative support set, Cloud Query Refactor (CQR) for reconstructing a query cloud using the support set, and Training-Free 3D Adapter (3D-TFA) for inferring query categories from the support set. A key advantage of PointTFA is that it introduces no extra training parameters, yet outperforms vanilla frozen ULIP, closely approaching few-shot fine-tuning training methods in downstream cloud classification tasks like ModelNet10 & 40 and ScanObjectNN. The code is available at: https://github.com/CaoChong-git/PointTFA.

In many real-world scenarios, we need to select from a set of candidate policies before online deployment. Although existing Off-policy evaluation (OPE) methods can be used to estimate the online performance, they suffer from high variance. Fortunately, we care only about the ranking of the candidate policies, rather than their exact online rewards. Based on this, we propose a novel framework PoRank for learning to rank policies. In practice, learning to rank policies faces two main challenges: 1) generalization over the huge policy space and 2) lack of supervision signals. To overcome the first challenge, PoRank uses a Policy Comparison Transformer (PCT) for learning cross-policy representations, which capture the core discrepancies between policies and generalizes well across the whole policy space. The second challenge arises because learning to rank requires online comparisons of policies as ground-truth labels, whereas deploying policies online might be highly expensive. To overcome this, PoRank adopts a crowdsourcing based learning-to-rank (LTR) framework, where a set of OPE algorithms are employed to provide weak comparison labels. Experimental results show that PoRank not only outperforms baselines when the ground-truth labels are provided, but also achieves competitive performance when the ground-truth labels are unavailable.

Deepfake model misuse poses major security concerns. Existing passive and active Deepfake detection methods both suffer from a lack of generalizability and robustness. In this study, we propose a pluggable and efficient active model watermarking framework for Deepfake detection. This approach facilitates the embedding of identification watermarks across a variety of Deepfake generation models, enabling easy extraction by authorities for detection purposes. Specifically, our method leverages the universal convolutional structure in generative model decoders. It employs convolutional kernel sparsification for adaptive watermark embedding positioning and introduces convolutional kernel normalization to seamlessly integrate watermark parameters with those of the generative model. For watermark extraction, we jointly train a watermark extractor based on a Deepfake detection model and use BCH encoding to identify watermark images effectively. Finally, we apply our approach to eight major types of Deepfake generation models. Experiments show our method successfully detects Deepfakes with an average accuracy exceeding 94% even in heavy lossy channels. This approach operates independently of the generation model's training without affecting the original model's performance. Furthermore, our model requires training a very limited number of parameters, and it is resilient against three major adaptive attacks. The source code can be found at https://github.com/GuaiZao/Pluggable-Watermarking

Today industrial facilities are equipped with lots of sensors throughout all the production line for monitoring means. Gathered data can be used to detect and predict failures; however, manual labeling of large amounts of data for supervised learning is complicated. This paper introduces an innovative approach to unsupervised fault detection and diagnosis tailored for monitoring industrial chemical processes. We showcase the efficacy of our model using two publicly accessible datasets from the Tennessee Eastman Process, each containing various faults. Furthermore, we illustrate that by fine-tuning the model on a limited amount of labeled data, it achieves performance close to that of a state-of-the-art model trained on the entire dataset.

We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. Specifically, we show that planning for PPLTL goals can be encoded into classical planning with minimal overhead, introducing only a number of new fluents that is at most linear in the PPLTL goal and no spurious additional actions. Based on these results, we implemented a system called Plan4Past, which can be used along with state-of-the-art classical planners, such as LAMA. An empirical analysis demonstrates the practical effectiveness of Plan4Past, showing that a classical planner generally performs better with our compilation than with other existing compilations for LTLf goals over the considered benchmarks.