IJCAI 2024 Papers

Learning the causal structure of each individual plays a crucial role in neuroscience, biology, and so on. Existing methods consider data from each individual separately, which may yield inaccurate causal structure estimations in limited samples. To leverage more samples, we consider incorporating data from all individuals as population data. We observe that the variables of all individuals are influenced by the common environment variables they share. These shared environment variables can be modeled as latent variables and serve as a bridge connecting data from different individuals. In particular, we propose an Individual Linear Acyclic Model (ILAM) for each individual from population data, which models the individual's variables as being linearly influenced by their parents, in addition to environment variables and noise terms. Theoretical analysis shows that the model is identifiable when all environment variables are non-Gaussian, or even if some are Gaussian with an adequate diversity in the variance of noises for each individual. We then develop an individual causal structures learning method based on the Share Independence Component Analysis technique. Experimental results on synthetic and real-world data demonstrate the correctness of the method even when the sample size of each individual's data is small.

Document-level event extraction (DEE) aims to extract the structured event information from a given document, facing two critical challenges: (1) event arguments always scatter across sentences (arguments-scattering); (2) multiple events can co-occur in one document (multi-event). Most recent studies mainly follow two simplified settings to ease the challenges: one simplifies DEE with the no-trigger-words design (NDEE), and the other focuses on event argument extraction (DEAE), a sub-task of DEE. However, the former excludes trigger extraction and suffers from error propagation in the sub-tasks. The latter relies heavily on the gold triggers as prerequisites and struggles to distinguish multiple arguments playing the same role in different events. To address the limitations above, we propose a novel joint trigger and argument extraction paradigm SEELE to enhance the DEE model via incorporating SchEma-awarE descriptions into Document-Level Event extraction. Specifically, the schema-aware descriptions are leveraged from two aspects: (1) guiding the attention mechanism among event-aware tokens across sentences, which relieves arguments-scattering without error propagation; (2) performing the fine-grained contrastive learning to distinguish different events, which mitigates multi-event without gold triggers. Extensive experiments show the superiority of SEELE, achieving notable improvements (2.1% to 9.7% F1) on three NDEE datasets and competitive performance on two DEAE datasets. Our code is available at https://github.com/TheoryRhapsody/SEELE.

With the expansion of the virtual communication industry using VR/AR, it has attracted increasing attention to enable users to represent their personalities in a 3D avatar. As the face of 3D avatars plays a crucial role in conveying human personality, a system that generates and manipulates 3D faces is desired. However, establishing the system is challenging due to the need for human effort and specialized knowledge. To fill this void, we present the Individual Virtual Transfer (InViTe), which enables the creation and customization of a 3D face according to the user's preference. Our proposed system is featured for 1) 3D face reconstruction with high fidelity texture map, 2) 3D face personalization, 3) realistic rendering results, and 4) real-time mobile virtual applications. We conduct an experiment to demonstrate that the proposed system can achieve sufficient individual personalization of 3D faces. Furthermore, we evaluate the system's data transmission protocol and demonstrate its efficiency. The demonstration video is available at https://www.youtube.com/watch?v=D 4pXZvGUWU.

Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages.

Cross-lingual named entity recognition (NER) aims to train an NER model for the target language leveraging only labeled source language data and unlabeled target language data. Prior approaches either perform label projection on translated source language data or employ a source model to assign pseudo labels for target language data and train a target model on these pseudo-labeled data to generalize to the target language. However, these automatic labeling procedures inevitably introduce noisy labels, thus leading to a performance drop. In this paper, we propose a Global-Local Denoising framework (GLoDe) for cross-lingual NER. Specifically, GLoDe introduces a progressive denoising strategy to rectify incorrect pseudo labels by leveraging both global and local distribution information in the semantic space. The refined pseudo-labeled target language data significantly improves the model's generalization ability. Moreover, previous methods only consider improving the model with language-agnostic features, however, we argue that target language-specific features are also important and should never be ignored. To this end, we employ a simple auxiliary task to achieve this goal. Experimental results on two benchmark datasets with six target languages demonstrate that our proposed GLoDe significantly outperforms current state-of-the-art methods.

Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure data of antibodies and antigens. MIPE implements a multi-modal contrastive learning strategy, which maximizes representations of binding and non-binding residues within each modality and meanwhile aligns uni-modal representations towards effective modal representations. To exploit the spatial interaction information, MIPE also incorporates an interaction informativeness estimation that computes the estimated interaction matrices between antibodies and antigens, thereby approximating them to the actual ones. Extensive experiments demonstrate the superiority of our method in predicting paratopes and epitopes compared to baselines. Additionally, the ablation studies and visualizations demonstrate the superiority of MIPE owing to the better representations acquired through multi-modal contrastive learning and the interaction patterns comprehended by the interaction informativeness estimation.

In multi-agent reinforcement learning (MARL), the epsilon-greedy method plays an important role in balancing exploration and exploitation during the decision-making process in value-based algorithms. However, the epsilon-greedy exploration process will introduce conservativeness when calculating the expected state value when the agents are more in need of exploitation during the approximate policy convergence, which may result in a suboptimal policy convergence. Besides, eliminating the epsilon-greedy algorithm leaves no exploration and may lead to unacceptable local optimal policies. To address this dilemma, we use the previously collected trajectories to construct a Monte-Carlo Trajectory Tree, so that an existing optimal template, a sequence of state prototypes, can be planned out. The agents start by following the planned template and act according to the policy without exploration, Stable Prefix Policy. The agents will adaptively dropout and begin to explore by following the epsilon-greedy method when the policy still needs exploration. We scale our approach to various value-based MARL methods and empirically verify our method in a cooperative MARL task, SMAC benchmarks. Experimental results demonstrate that our method achieves not only better performance but also faster convergence speed than baseline algorithms within early time steps.

Convolutional Neural Networks (CNNs) are well-known for their vulnerability to adversarial attacks, posing significant security concerns. In response to these threats, various defense methods have emerged to bolster the model's robustness. However, most existing methods either focus on learning from adversarial perturbations, leading to overfitting to the adversarial examples, or aim to eliminate such perturbations during inference, inevitably increasing computational burdens. Conversely, clean training, which strengthens the model's robustness by relying solely on clean examples, can address the aforementioned issues. In this paper, we align with this methodological stream and enhance its generalizability to unknown adversarial examples. This enhancement is achieved by scrutinizing the behavior of latent features within the network. Recognizing that a correct prediction relies on the correctness of the latent feature's pattern, we introduce a novel and effective Feature Pattern Consistency Constraint (FPCC) method to reinforce the latent feature's capacity to maintain the correct feature pattern. Specifically, we propose Spatial-wise Feature Modification and Channel-wise Feature Selection to enhance latent features. Subsequently, we employ the Pattern Consistency Loss to constrain the similarity between the feature pattern of the latent features and the correct feature pattern. Our experiments demonstrate that the FPCC method empowers latent features to uphold correct feature patterns even in the face of adversarial examples, resulting in inherent adversarial robustness surpassing state-of-the-art models.

This work proposes an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size n. Our algorithm improves the best approximation factor of the existing parallel one from 8 to 7 with O(log n) adaptive complexity. The key idea of our approach is to create an alternate threshold algorithmic framework. This new strategy alternately constructs two disjoint candidate solutions within a constant number of sequence rounds. Then, the algorithm boosts solution quality without sacrificing the adaptive complexity. Extensive experimental studies on three applications, Revenue Maximization, Image Summarization, and Maximum Weighted Cut, show that our algorithm not only significantly increases solution quality but also requires comparative adaptivity to state-of-the-art algorithms.

We present an evolutionary algorithm evo-SMC for the problem of Submodular Maximization under Cost constraints (SMC). Our algorithm achieves 1/2-approximation with a high probability 1-1/n within O(n2 K) iterations, where K denotes the maximum size of a feasible solution set with cost constraint B. To the best of our knowledge, this is the best approximation guarantee offered by evolutionary algorithms for this problem. We further refine evo-SMC and develop st-evo-SMC. This stochastic version yields a significantly faster algorithm while maintaining the approximation ratio of 1/2, with probability 1-epsilon. The required number of iterations reduces to O(nK log(1/epsilon)/p), where the user defined parameters p represents the stochasticity probability, and epsilon denotes the error threshold. Finally, the empirical evaluations carried out through extensive experimentation substantiate the efficiency and effectiveness of our proposed algorithms. Our algorithms consistently outperform existing methods, producing higher-quality solutions.

In this work, we introduce novel translations of Answer Set Programming (ASP) into Integer Programming (IP). While building upon a previously introduced IP translation, we revisit the translation of acyclicity constraints essential for capturing answer sets precisely. By leveraging vertex elimination graphs, we demonstrate that a new translation of acyclicity can yield integer programs with a more restrictive linear relaxation compared to previous methods. This enhancement enables IP solvers to prune the search space more efficiently. Furthermore, we show how acyclicity can be expressed more concisely in IP given any feedback vertex set of the underlying dependency graph. Experimental results underscore the improved efficiency of our methods over the previously implemented translation. The new vertex elimination based translation with Gurobi as the back-end solver turns out competitive against Clingo, a state-of-the-art native ASP solver, in a number of non-tight Answer Set Optimization (ASO) benchmarks.

We study how to fairly allocate a set of indivisible chores among n agents who may have different weights corresponding to their involvement in completing these chores. We found that some of the existing fairness notions may place agents with lower weights at a disadvantage, which motivates us to explore weighted maximin share fairness (WMMS). While it is known that a WMMS allocation may not exist, no non-trivial approximation has been discovered thus far. In this paper, we first design a simple sequential picking algorithm that solely relies on the agents’ ordinal rankings of the items, which achieves an approximation ratio of O(log n). Then, for the case involving two agents, we improve the approximation ratio to (√3+1)/2 ≈1.366, and prove that it is optimal. We also consider an online setting when the items arrive one after another and design an O(√n)-competitive online algorithm given the valuations are normalized

The Capacitated Location Routing Problem is an important planning and routing problem in logistics, which generalizes the capacitated vehicle routing problem and the uncapacitated facility location problem. In this problem, we are given a set of depots and a set of customers where each depot has an opening cost and each customer has a demand, and we need to use minimum cost to open some depots and route capacitated vehicles in the opened depots to satisfy all customers' demand. In this paper, we propose a 4.169-approximation algorithm for this problem, improving the best-known 4.38-approximation ratio (Transportation Science 2013). Moreover, if the demand of each customer is allowed to be delivered by multiple tours, we propose a more refined 4.092-approximation algorithm. Experimental study on benchmark instances shows that the quality of our computed solutions is better than that of previous algorithms and is also much closer to optimality than the provable approximation factor.

Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets. While robust MDPs have been studied extensively, work on RPOMDPs is limited and primarily focuses on algorithmic solution methods. We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) semantic; and 3) the same RPOMDP with different assumptions leads to semantically different POSGs and, thus, different policies and values. These novel semantics for RPOMDPs give access to results for POSGs, studied in game theory; concretely, we show the existence of a Nash equilibrium. Finally, we classify the existing RPOMDP literature using our semantics, clarifying under which uncertainty assumptions these existing works operate.

Recently, various deep denoising methods have been proposed to solve the insufficient feature problem in image denoising. These methods can be mainly classified into two categories: (1) Injecting learnable tensors into denoising backbone to supplement feature, which is effective to some extent but may cause serious over-fitting. (2) Using diverse natural images from large image datasets to synthesize noisy images and pre-train denoising models, which can bring model generalization but require large model size and expensive training costs. To address these issues, this paper proposes Implicit Prompt Learning for Image Denoising (IPLID) method to flexibly generate adaptive prompts without meticulously designing them. Specifically, we first introduce an efficient Linear Prompt (LP) block with ultra-few parameters to produce dynamic prompts for both different stages and samples in denoising procedure. We further propose an efficient Compact Feature Fusion (CFF) block to process previous multi-level prompted denoising feature to reconstruct the denoising images. Finally, to further efficiently and effectively produce satisfactory prompt and denoising performance, a Gradient Accumulation (GA) learning scheme is proposed. Experiments on multiple benchmarks showed that the proposed IPLID achieves competitive results with only 1 percent of pre-trained backbone parameters, outperforming classical denoising methods in both efficiency and quality of restored images.

Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often deeply hidden within large datasets, and 2) Anomaly detection approaches are required to unveil the mechanisms behind anomaly generation. Our study focuses on detecting hidden anomaly subgraphs within big data and offering improved explanations for the root cause of anomalies by integrating multi-domain datasets.

Natural language processing (NLP) has received unprecedented attention. While advancements in NLP models have led to extensive research into their backdoor vulnerabilities, the potential for these advancements to introduce new backdoor threats remains unexplored. This paper proposes Imperio, which harnesses the language understanding capabilities of NLP models to enrich backdoor attacks. Imperio provides a new model control experience. Demonstrated through controlling image classifiers, it empowers the adversary to manipulate the victim model with arbitrary output through language-guided instructions. This is achieved using a language model to fuel a conditional trigger generator, with optimizations designed to extend its language understanding capabilities to backdoor instruction interpretation and execution. Our experiments across three datasets, five attacks, and nine defenses confirm Imperio's effectiveness. It can produce contextually adaptive triggers from text descriptions and control the victim model with desired outputs, even in scenarios not encountered during training. The attack reaches a high success rate without compromising the accuracy of clean inputs and exhibits resilience against representative defenses. Supplementary materials are available at https://khchow.com/Imperio.

We investigate optimal decision making under imperfect recall, that is, when an agent forgets information it once held before. An example is the absentminded driver game, as well as team games in which the members have limited communication capabilities. In the framework of extensive-form games with imperfect recall, we analyze the computational complexities of finding equilibria in multiplayer settings across three different solution concepts: Nash, multiselves based on evidential decision theory (EDT), and multiselves based on causal decision theory (CDT). We are interested in both exact and approximate solution computation. As special cases, we consider (1) single-player games, (2) two-player zero-sum games and relationships to maximin values, and (3) games without exogenous stochasticity (chance nodes). We relate these problems to the complexity classes PPAD, PLS, Σ_2^P, ∃R, and ∃∀R.

Common approaches to image retrieval include contrastive methods and specialized loss functions such as ranking losses and entropy regularizers. We present DMCAC (Divergence Minimization with Cross-Attention Classification), a novel image retrieval method that offers a new perspective on this training paradigm. We use self-supervision with a novel divergence loss framework alongside a simple data flow adjustment that minimizes a distribution over a database directly during training. We show that jointly learning a query representation over a database is a competitive and often improved alternative to traditional contrastive methods for image retrieval. We evaluate our method across several model configurations and four datasets, achieving state-of-the-art performance in multiple settings. We also conduct a thorough set of ablations that show the robustness of our method across full vs. approximate retrieval and different hyperparameter configurations.