GenAI4ELab/papercli-papers huggingface dataset:
Content Matters: A Computational Investigation into the Effectiveness of Retrieval Practice and Worked Examples (Extended Abstract)
Authors: Napol Rachatasumrit; Paulo F. Carvalho; Sophie Li; Kenneth R. Koedinger
Venue: IJCAI 2024 (main)

Abstract: In this paper, we argue that computational models of learning can contribute precise theory to explain surprising student learning phenomena. In some past studies, practice produces better learning than studying examples, whereas other studies show the opposite result. We explain this contradiction by suggesting that retrieval practice and example study involve different learning cognitive processes, memorization and induction, and each process is optimal for different types of knowledge. We implement and test this theoretical explanation by extending an AI model of human cognition to include both memory and induction processes and comparing the behavior of the simulated learners to those of human participants. We show that the behavior of simulated learners with forgetting matches that of human participants better than simulated learners without forgetting. Simulated learners with forgetting learn best using retrieval practice in situations that emphasize memorization (such as learning facts), whereas studying examples improves learning when multiple pieces of information are available, so induction and generalization are necessary (such as learning skills).
Voting by Axioms (Extended Abstract)
Authors: Marie Christin Schmidtlein; Ulle Endriss
Venue: IJCAI 2024 (main)

Abstract: We develop an approach for collective decision making from first principles. In this approach, rather than using a---necessarily imperfect---voting rule to map any given scenario where individual agents report their preferences into a collective decision, we identify for every concrete such scenario the most appealing set of normative principles (known as axioms in social choice theory) that would entail a unique decision and then implement that decision. We analyse some of the fundamental properties of this new approach, from both an algorithmic and a normative point of view.
All in One: Multi-task Prompting for Graph Neural Networks (Extended Abstract)
Authors: Xiangguo Sun; Hong Cheng; Jia Li; Bo Liu; Jihong Guan
Venue: IJCAI 2024 (main)

Abstract: This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award. The paper introduces a novel approach to bridging the gap between pre-trained graph models and the diverse tasks they’re applied to, inspired by the success of prompt learning in NLP. Recognizing the challenge of aligning pre-trained models with varied graph tasks (node level, edge level, and graph level), which can lead to negative transfer and poor performance, we propose a multi-task prompting method for graphs. This method involves unifying graph and language prompt formats, enabling NLP’s prompting strategies to be adapted for graph tasks. By analyzing the task space of graph applications, we reformulate problems to fit graph-level tasks and apply meta-learning to improve prompt initialization for multiple tasks. Experiments show our method’s effectiveness in enhancing model performance across different graph tasks. Beyond the original work, in this extended abstract, we further discuss the graph prompt from a bigger picture and provide some of the latest work toward this area.
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract)
Authors: Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Guennemann; David Ruegamer
Venue: IJCAI 2024 (main)

Abstract: Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively expensive for large modern architectures. We argue that the dilemma between exact-but-unaffordable and cheap-but-inexact approaches can be mitigated by exploiting symmetries in the posterior landscape. We show theoretically that the posterior predictive density in Bayesian neural networks can be restricted to a symmetry-free parameter reference set. By further deriving an upper bound on the number of Monte Carlo chains required to capture the functional diversity, we propose a straightforward approach for feasible Bayesian inference.
On the Complexity of Finding Set Repairs for Data-Graphs (Abstract Reprint)
Authors: Sergio Abriola; Maria Vanina Martinez; Nina Pardal; Santiago Cifuentes; Edwin Pin Baque
Venue: IJCAI 2024 (main)

Abstract: In the deeply interconnected world we live in, pieces of information link domains all around us. As graph databases embrace effectively relationships among data and allow processing and querying these connections efficiently, they are rapidly becoming a popular platform for storage that supports a wide range of domains and applications. As in the relational case, it is expected that data preserves a set of integrity constraints that define the semantic structure of the world it represents. When a database does not satisfy its integrity constraints, a possible approach is to search for a ‘similar’ database that does satisfy the constraints, also known as a repair. In this work, we study the problem of computing subset and superset repairs for graph databases with data values using a notion of consistency based on having a set of Reg-GXPath expressions as integrity constraints. We show that for positive fragments of Reg-GXPath these problems admit a polynomialtime algorithm, while the full expressive power of the language renders them intractable.
Hybrid planning for challenging construction problems: An Answer Set Programming approach (Abstract Reprint)
Authors: Faseeh Ahmad; Volkan Patoglu; Esra Erdem
Venue: IJCAI 2024 (main)

Abstract: We study construction problems where multiple robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by a surface or a robot and stability of the overall structure at all times. We propose a general elaboration tolerant method to solve a wide range of construction problems, based on the knowledge representation and reasoning paradigm of Answer Set Programming. This method not only (i) determines a stable final configuration of the structure, but also (ii) computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, (iii) while simultaneously ensuring the requirements of supportedness and stability at all times. We prove the soundness and completeness of our method with respect to these properties. We introduce a set of challenging construction benchmark instances, including construction of (uneven) bridges and overhangs, and discuss the usefulness of our framework over these instances. Furthermore, we perform experiments to investigate the computational performance of our hybrid method, and demonstrate the applicability of our method using a bimanual Baxter robot.
Negative Human Rights as a Basis for Long-term AI Safety and Regulation (Abstract Reprint)
Authors: Ondrej Bajgar; Jan Horenovsky
Venue: IJCAI 2024 (main)

Abstract: If autonomous AI systems are to be reliably safe in novel situations, they will need to incorporate general principles guiding them to recognize and avoid harmful behaviours. Such principles may need to be supported by a binding system of regulation, which would need the underlying principles to be widely accepted. They should also be specific enough for technical implementation. Drawing inspiration from law, this article explains how negative human rights could fulfil the role of such principles and serve as a foundation both for an international regulatory system and for building technical safety constraints for future AI systems.
Weighted, Circular and Semi-Algebraic Proofs (Abstract Reprint)
Authors: Ilario Bonacina; Maria Luisa Bonet; Jordi Levy
Venue: IJCAI 2024 (main)

Abstract: In recent years there has been an increasing interest in studying proof systems stronger than Resolution, with the aim of building more efficient SAT solvers based on them. In defining these proof systems, we try to find a balance between the power of the proof system (the size of the proofs required to refute a formula) and the difficulty of finding the proofs. In this paper we consider the proof systems circular Resolution, Sherali-Adams, Nullstellensatz and Weighted Resolution and we study their relative power from a theoretical perspective. We prove that circular Resolution, Sherali-Adams and Weighted Resolution are polynomially equivalent proof systems. We also prove that Nullstellensatz is polynomially equivalent to a restricted version of Weighted Resolution. The equivalences carry on also for versions of the systems where the coefficients/weights are expressed in unary. The practical interest in these systems comes from the fact that they admit efficient algorithms to find proofs in case these have small width/degree.
Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint)
Authors: Yang Cao; Ye Zhu; Kai Ming Ting; Flora D. Salim; Hong Xian Li; Luxing Yang; Gang Li
Venue: IJCAI 2024 (main)

Abstract: Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitivity to outliers. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change-points in data streams with the tolerance of outliers. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.
Learning to Resolve Social Dilemmas: A Survey (Abstract Reprint)
Authors: Shaheen Fatima; Nicholas Jennings; Michael Wooldridge
Venue: IJCAI 2024 (main)

Abstract: Social dilemmasare situations of inter-dependent decision making in which individualrationality can lead to outcomes with poor social qualities. The ubiquity of social dilem-mas in social, biological, and computational systems has generated substantial researchacross these diverse disciplines into the study of mechanisms for avoiding deficient outcomes by promoting and maintaining mutual cooperation. Much of this research is focused on studying how individuals faced with a dilemma can learn to cooperate by adapting their behaviours according to their past experience. In particular, three types of learning approaches have been studied: evolutionary game-theoretic learning, reinforcement learning, and best-response learning. This article is a comprehensive integrated survey of these learning approaches in the context of dilemma games. We formally introduce dilemma games and their inherent challenges. We then outline the three learning approaches and, for eachapproach, provide a survey of the solutions proposed for dilemma resolution. Finally, we provide a comparative summary and discuss directions in which further research is needed.
A differentiable first-order rule learner for inductive logic programming (Abstract Reprint)
Authors: Kun Gao; Katsumi Inoue; Yongzhi Cao; Hanpin Wang
Venue: IJCAI 2024 (main)

Abstract: Learning first-order logic programs from relational facts yields intuitive insights into the data. Inductive logic programming (ILP) models are effective in learning first-order logic programs from observed relational data. Symbolic ILP models support rule learning in a data-ecient manner. However, symbolic ILP models are not robust to learn from noisy data. Neuro-symbolic ILP models utilize neural networks to learn logic programs in a differentiable manner which improves the robustness of ILP models. However, most neuro-symbolic methods need a strong language bias to learn logic programs, which reduces the usability and flexibility of ILP models and limits the logic program formats. In addition, most neuro-symbolic ILP methods cannot learn logic programs effectively from both small-size datasets and large-size datasets such as knowledge graphs. In the paper, we introduce a novel differentiable ILP model called differentiable first-order rule learner (DFORL), which is scalable to learn rules from both smaller and larger datasets. Besides, DFORL only needs the number of variables in the learned logic programs as input. Hence, DFORL is easy to use and does not need a strong language bias. We demonstrate that DFORL can perform well on several standard ILP datasets, knowledge graphs, and probabilistic relation facts and outperform several well-known differentiable ILP models. Experimental results indicate that DFORL is a precise, robust, scalable, and computationally cheap differentiable ILP model.
SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes (Abstract Reprint)
Authors: Maksim Golyadkin; Vitaliy Pozdnyakov; Leonid Zhukov; Ilya Makarov
Venue: IJCAI 2024 (main)

Abstract: Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings. In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan
On Mitigating the Utility-Loss in Differentially Private Learning: A New Perspective by a Geometrically Inspired Kernel Approach (Abstract Reprint)
Authors: Mohit Kumar; Bernhard A. Moser; Lukas Fischer
Venue: IJCAI 2024 (main)

Abstract: Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. In this approach, a representation of the affine hull of given data points is learned in Reproducing Kernel Hilbert Spaces (RKHS). This leads to a novel distance measure that hides privacy-sensitive information about individual data points and improves the privacy-utility tradeoff via significantly reducing the risk of membership inference attacks. The effectiveness of the approach is demonstrated through experiments on MNIST dataset, Freiburg groceries dataset, and a real biomedical dataset. It is verified that the approach remains computationally practical. The application of the approach to federated learning is considered and it is observed that the accuracy-loss due to data being distributed is either marginal or not significantly high.
Optimality Guarantees for Particle Belief Approximation of POMDPs (Abstract Reprint)
Authors: Michael Lim; Tyler Becker; Mykel Kochenderfer; Claire Tomlin; Zachary Sunberg
Venue: IJCAI 2024 (main)

Abstract: Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of O(C), where C is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.
Mitigating robust overfitting via self-residual-calibration regularization (Abstract Reprint)
Authors: Hong Liu; Zhun Zhong; Nicu Sebe; Shin'ichi Satoh
Venue: IJCAI 2024 (main)

Abstract: Overfitting in adversarial training has attracted the interest of researchers in the community of artificial intelligence and machine learning in recent years. To address this issue, in this paper we begin by evaluating the defense performances of several calibration methods on various robust models. Our analysis and experiments reveal two intriguing properties: 1) a well-calibrated robust model is decreasing the confidence of robust model; 2) there is a trade-off between the confidences of natural and adversarial images. These new properties offer a straightforward insight into designing a simple but effective regularization, called Self-Residual-Calibration (SRC). The proposed SRC calculates the absolute residual between adversarial and natural logit features corresponding to the ground-truth labels. Furthermore, we utilize the pinball loss to minimize the quantile residual between them, resulting in more robust regularization. Extensive experiments indicate that our SRC can effectively mitigate the overfitting problem while improving the robustness of state-of-the-art models. Importantly, SRC is complementary to various regularization methods. When combined with them, we are capable of achieving the top-rank performance on the AutoAttack benchmark leaderboard.
Performative Ethics From Within the Ivory Tower: How CSPractitioners Uphold Systems of Oppression (Abstract Reprint)
Authors: Zari McFadden; Lauren Alvarez
Venue: IJCAI 2024 (main)

Abstract: This paper analyzes where Artificial Intelligence (AI) Ethics research fails and breaks down the dangers of well-intentioned, but ultimately performative ethics research. A large majority of AI ethics research is critiqued for lacking a comprehensive analysis of how AI is interconnected with sociological systems of oppression and power. Our work contributes to the handful of research that presents intersectional, Western systems of oppression and power as a framework for examining AI ethics work and the complexities of building less harmful technology; directly connecting technology to named systems such as capitalism and classism, colonialism, racism and white supremacy, patriarchy, and ableism. We then explore current AI ethics rhetoric's effect on the AI ethics domain and AI regulation. In conclusion, we provide an applied example to contextualize intersectional systems of oppression and AI interventions in the U.S. justice system and present actionable steps for AI practitioners to participate in a less performative, critical analysis of AI.
Methods for recovering conditional independence graphs (Abstract Reprint)
Authors: Harsh Shrivastava; Urszula Chajewska
Venue: IJCAI 2024 (main)

Abstract: Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations . To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes. Keywords: Conditional Independence Graphs, Probabilistic Graphical Models, Graphical Lasso, Deep Learning, Optimization
Hierarchical Decompositions and Termination Analysis for Generalized Planning (Abstract Reprint)
Authors: Siddharth Srivastava
Venue: IJCAI 2024 (main)

Abstract: This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fun- damental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hi- erarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans.