bluuebunny/crossref_metadata_2025 huggingface dataset:
Recovering Implicit Physics Model Under Real-World Constraints
Authors: Banerjee Ayan , Gupta Sandeep K.S.
Published: October 2024

Recovering a physics-driven model, i.e. a governing set of equations of the underlying dynamical systems, from the real-world data has been of recent interest. Most existing methods either operate on simulation data with unrealistically high sampling rates or require explicit measurements of all system variables, which is not amenable in real-world deployments. Moreover, they assume the timestamps of external perturbations to the physical system are known a priori, without uncertainty, implicitly discounting any sensor time-synchronization or human reporting errors. In this paper, we propose a novel liquid time constant neural network (LTC-NN) based architecture to recover underlying model of physical dynamics from real-world data. The automatic differentiation property of LTC-NN nodes overcomes problems associated with low sampling rates, the input dependent time constant in the forward pass of the hidden layer of LTC-NN nodes creates a massive search space of implicit physical dynamics, the physics model solver based data reconstruction loss guides the search for the correct set of implicit dynamics, and the use of the dropout regularization in the dense layer ensures extraction of the sparsest model. Further, to account for the perturbation timing error, we utilize dense layer nodes to search through input shifts that results in the lowest reconstruction loss. Experiments on four benchmark dynamical systems, three with simulation data and one with the real-world data show that the LTC-NN architecture is more accurate in recovering implicit physics model coefficients than the state-of-the-art sparse model recovery approaches. We also introduce four additional case studies (total eight) on real-life medical examples in simulation and with real-world clinical data to show effectiveness of our approach in recovering underlying model in practice.
Dynamic Multimodal Prompt Tuning: Boost Few-Shot Learning with VLM-Guided Point Cloud Models
Authors: Gu Xiang , Pang Shuchao , Du Anan , Wang Yifei , Miao Jixiang , Díez Jorge
Published: October 2024

Few-shot learning is crucial for downstream tasks involving point clouds, given the challenge of obtaining sufficient datasets due to extensive collecting and labeling efforts. Pre-trained VLM-Guided point cloud models, containing abundant knowledge, can compensate for the scarcity of training data, potentially leading to very good performance. However, adapting these pre-trained point cloud models to specific few-shot learning tasks is challenging due to their huge number of parameters and high computational cost. To this end, we propose a novel Dynamic Multimodal Prompt Tuning method, named DMMPT, for boosting few-shot learning with pre-trained VLM-Guided point cloud models. Specifically, we build a dynamic knowledge collector capable of gathering task- and data-related information from various modalities. Then, a multimodal prompt generator is constructed to integrate collected dynamic knowledge and generate multimodal prompts, which efficiently direct pre-trained VLM-guided point cloud models toward few-shot learning tasks and address the issue of limited training data. Our method is evaluated on benchmark datasets not only in a standard N-way K-shot few-shot learning setting, but also in a more challenging setting with all classes and K-shot few-shot learning. Notably, our method outperforms other prompt-tuning techniques, achieving highly competitive results comparable to full fine-tuning methods while significantly enhancing computational efficiency.
TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
Authors: Meng Zizhuo , Li Boyu , Fan Xuhui , Li Zhidong , Wang Yang , Chen Fang , Zhou Feng
Published: October 2024

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://github.com/waystogetthere/TransFeat.git.
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence
Authors: Pagliari Roberto , Hill Peter , Chen Po-Yu , Dabrowny Maciej , Tan Tingsheng , Buet-Golfouse Francois
Published: October 2024

In many applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of sustainability in AI, an emerging practical AI topic. However, although these pillars have been individually addressed by past literature, none of these works have considered all the pillars. There are inherent trade-offs between each of the pillars (for example, utility vs fairness or utility vs privacy), making it even more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning. It proposes FPIG, a general AI pipeline that allows for simultaneous consideration and a better understanding of the tradeoffs between the pillars. Based on the FPIG framework, we propose a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training. This algorithm allows users to select the optimal model architecture for a given dataset and a set of user requirements on the pillars. We illustrate the trade-offs under the FPIG model on three classical datasets and demonstrate the meta-learning approach with an example of real-world datasets and models with different interpretability, showcasing how it can aid model selection.
Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
Authors: Le Thao , Miller Tim , Sonenberg Liz , Singh Ronal
Published: October 2024

Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines.
Empirical Evaluation of Concept Probing for Game-Playing Agents
Authors: Pálsson Aðalsteinn , Björnsson Yngvi
Published: October 2024

Concept probing is one prominent methodology for interpreting and analyzing (deep) neural network models. It has, for example, formed the backbone of several recent works to understand better the high-level knowledge learned and employed by game-playing agents, particularly in chess. However, some recent theoretical and empirical studies have questioned the methodology’s reliability and highlighted some limitations. Here, in the game-playing domain of chess, we investigate the effectiveness of several different probing architectures and look into the reliability of methods for interpreting their results. We use a world-class chess-playing agent as our test domain, which allows us, via self-play, to quantify the importance of the concepts identified in the agent’s neural network by the concept probes. Our results demonstrate that the widespread practice of using linear probes and interpreting their accuracy to indicate concept importance is somewhat unreliable and needs to be revised. We demonstrate several ways of doing that in our domain, particularly by using more complex probes and amnesic-like probing.
(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers
Authors: Duong Manh Khoi , Conrad Stefan
Published: October 2024

Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains, including machine learning models and human decision-makers in real-world applications. This involves calculating the disparities between probabilistic outcomes among social groups, such as acceptance rates between male and female applicants. However, traditional fairness metrics do not account for the uncertainty in these processes and lack of comparability when two decision-makers exhibit the same disparity. Using Bayesian statistics, we quantify the uncertainty of the disparity to enhance discrimination assessments. We represent each decision-maker, whether a machine learning model or a human, by its disparity and the corresponding uncertainty in that disparity. We define preferences over decision-makers and utilize brute-force to choose the optimal decision-maker according to a utility function that ranks decision-makers based on these preferences. The decision-maker with the highest utility score can be interpreted as the one for whom we are most certain that it is fair.
Time Series Imputation with Multivariate Radial Basis Function Neural Network
Authors: Jung Chanyoung , Jang Yun
Published: October 2024

Researchers have been persistently working to address the issue of missing values in time series data. Numerous models have been proposed, striving to estimate the distribution of the data. The Radial Basis Functions Neural Network (RBFNN) has recently exhibited exceptional performance in estimating data distribution. In this paper, we propose a time series imputation model based on RBFNN. Our imputation model learns local information from timestamps to create a continuous function. Additionally, we incorporate time gaps to facilitate learning information considering the missing terms of missing values. We name this model the Missing Imputation Multivariate RBFNN (MIM-RBFNN). However, MIM-RBFNN relies on a local information-based learning approach, which presents difficulties in utilizing temporal information. Therefore, we propose an extension called the Missing Value Imputation Recurrent Neural Network with Continuous Function (MIRNN-CF) using the continuous function generated by MIM-RBFNN. We evaluate the performance using two real-world datasets with non-random missing and random missing patterns, and conduct an ablation study comparing MIM-RBFNN and MIRNN-CF.
FairUS - UpSampling Optimized Method for Boosting Fairness
Authors: Cohen-Inger Nurit , Rozenblatt Guy , Cohen Seffi , Rokach Lior , Shapira Bracha
Published: October 2024

The increasing application of machine learning (ML) in critical areas such as healthcare and finance highlights the importance of fairness in ML models, challenged by biases in training data that can lead to discrimination. We introduce ‘FairUS’, a novel pre-processing method for reducing bias in ML models utilizing the Conditional Generative Adversarial Network (CTGAN) to synthesize upsampled data. Unlike traditional approaches that focus solely on balancing subgroup sample sizes, FairUS strategically optimizes the quantity of synthesized data. This optimization aims to achieve an ideal balance between enhancing fairness and maintaining the overall performance of the model. Extensive evaluations of our method over several canonical datasets show that the proposed method enhances fairness by 2.7 times more than the related work and 4 times more than the baseline without mitigation, while preserving the performance of the ML model. Moreover, less than a third of the amount of synthetic data was needed on average. Uniquely, the proposed method enables decision-makers to choose the working point between improved fairness and model’s performance according to their preferences.
The Distributional Uncertainty of the SHAP Score in Explainable Machine Learning
Authors: Cifuentes Santiago , Bertossi Leopoldo , Pardal Nina , Abriola Sergio , Martinez Maria Vanina , Romero Miguel
Published: October 2024

Attribution scores reflect how important the feature values in an input entity are for the output of a machine learning model. One of the most popular attribution scores is the SHAP score, which is an instantiation of the general Shapley value used in coalition game theory. The definition of this score relies on a probability distribution on the entity population. Since the exact distribution is generally unknown, it needs to be assigned subjectively or be estimated from data, which may lead to misleading feature scores. In this paper, we propose a principled framework for reasoning on SHAP scores under unknown entity population distributions. In our framework, we consider an uncertainty region that contains the potential distributions, and the SHAP score of a feature becomes a function defined over this region. We study the basic problems of finding maxima and minima of this function, which allows us to determine tight ranges for the SHAP scores of all features. In particular, we pinpoint the complexity of these problems, and other related ones, showing them to be intractable. Finally, we present experiments on a real-world dataset, showing that our framework may contribute to a more robust feature scoring.
Understanding XAI Through the Philosopher’s Lens: A Historical Perspective
Authors: Mattioli Martina , Cinà Antonio Emanuele , Pelillo Marcello
Published: October 2024

Despite explainable AI (XAI) has recently become a hot topic and several different approaches have been developed, there is still a widespread belief that it lacks a convincing unifying foundation. On the other hand, over the past centuries, the very concept of explanation has been the subject of extensive philosophical analysis in an attempt to address the fundamental question of “why” in the context of scientific law. However, this discussion has rarely been connected with XAI. This paper tries to fill in this gap and aims to explore the concept of explanation in AI through an epistemological lens. By comparing the historical development of both the philosophy of science and AI, an intriguing picture emerges. Specifically, we show that a gradual progression has independently occurred in both domains from logical-deductive to statistical models of explanation, thereby experiencing in both cases a paradigm shift from deterministic to nondeterministic and probabilistic causality. Interestingly, we also notice that similar concepts have independently emerged in both realms such as, for example, the relation between explanation and understanding and the importance of pragmatic factors. Our study aims to be the first step towards understanding the philosophical underpinnings of the notion of explanation in AI, and we hope that our findings will shed some fresh light on the elusive nature of XAI.
Mitigating Bias: Model Pruning for Enhanced Model Fairness and Efficiency
Authors: Kasyap Harsh , Atmaca Ugur Ilker , Iezzi Michela , Walsh Toby , Maple Carsten
Published: October 2024

Machine learning models have been instrumental in making decisions across domains, like mortgage lending and risk assessment in finance. However, these models have been found susceptible to biases, causing unfair decisions for a specific group of individuals. Such bias is generally based on some protected (or sensitive) attributes, such as age, sex, or race, and is still prevalent due to historical context or algorithmic bias. There have been several efforts to ensure equal opportunities for each individual/group, based on creditworthiness, rather than any social bias. Several pre-, in- and post-processing bias mitigation techniques have been proposed. However, these techniques perform data transformation or design new constraint/cost functions, which are task-specific, to achieve a fair prediction. Such techniques even require further access to the complete training/testing data. This paper proposes a novel post-processing bias mitigation technique that employs a model interpretation strategy to find the responsible model weights causing the bias. Pruning only a few model weights exhibits group fairness in model predictions while maintaining competitive accuracy levels, thus aligning with the goals of fairness and efficiency in decision-making. The proposed scheme requires access to only a few data samples representing the protected attributes, without exposing the complete training data. Through extensive experiments with multiple census datasets/methods, we demonstrate the efficacy of our approach, achieving up to a significant 50% reduction in bias while preserving the overall accuracy.
Fair-OBNC: Correcting Label Noise for Fairer Datasets
Authors: Oliveira e Silva Inâs , Jesus Sérgio , Ferreira Hugo , Saleiro Pedro , Sousa Inês , Bizarro Pedro , Soares Carlos
Published: October 2024

Data used by automated decision-making systems, such as Machine Learning models, often reflects discriminatory behavior that occurred in the past. These biases in the training data are sometimes related to label noise, such as in COMPAS, where more African-American offenders are wrongly labeled as having a higher risk of recidivism when compared to their White counterparts. Models trained on such biased data may perpetuate or even aggravate the biases with respect to sensitive information, such as gender, race, or age. However, while multiple label noise correction approaches are available in the literature, these focus on model performance exclusively. In this work, we propose Fair-OBNC, a label noise correction method with fairness considerations, to produce training datasets with measurable demographic parity. The presented method adapts Ordering-Based Noise Correction, with an adjusted criterion of ordering, based both on the margin of error of an ensemble, and the potential increase in the observed demographic parity of the dataset. We evaluate Fair-OBNC against other different pre-processing techniques, under different scenarios of controlled label noise. Our results show that the proposed method is the overall better alternative within the pool of label correction methods, being capable of attaining better reconstructions of the original labels. Models trained in the corrected data have an increase, on average, of 150% in demographic parity, when compared to models trained in data with noisy labels, across the considered levels of label noise.
FairCognizer: A Model for Accurate Predictions with Inherent Fairness Evaluation
Authors: Bendoukha Adda-Akram , Kaaniche Nesrine , Boudguiga Aymen , Sirdey Renaud
Published: October 2024

Algorithmic fairness is a critical challenge in building trustworthy Machine Learning (ML) models. ML classifiers strive to make predictions that closely match real-world observations (ground truth). However, if the ground truth data itself reflects biases against certain sub-populations, a dilemma arises: prioritize fairness and potentially reduce accuracy, or emphasize accuracy at the expense of fairness. This work proposes a novel training framework that goes beyond achieving high accuracy. Our framework trains a classifier to not only deliver optimal predictions but also to identify potential fairness risks associated with each prediction. To do so, we specify a dual-labeling strategy where the second label contains a per-prediction fairness evaluation, referred to as an unfairness risk evaluation. In addition, we identify a subset of samples as highly vulnerable to group-unfair classifiers. Our experiments demonstrate that our classifiers attain optimal accuracy levels on both the Adult-Census-Income and Compas-Recidivism datasets. Moreover, they identify unfair predictions with nearly 75% accuracy at the cost of expanding the size of the classifier by a mere 45%.
MixCon: A Hybrid Architecture for Efficient and Adaptive Sequence Modeling
Authors: Xu Xin , Lin Zhouchen
Published: October 2024

Sequence modeling is a critical task in various domains such as natural language processing, speech recognition, and time series analysis. The existing models still face challenges in capturing long-range dependencies and efficiently modeling sequences. This paper proposes a novel hybrid sequence modeling architecture called MixCon to address these challenges. The MixCon (Mixture of Conba) architecture combines a Transformer layer based on attention mechanism, a Conba layer, and a Mixture of Experts (MoE) module. We apply this idea to the design of the attention mechanism, achieving significant improvements in computational efficiency. Additionally, the MixCon architecture integrates feedback and adaptive control mechanism inspired by control theory, providing a new perspective and approach to sequence modeling. The experimental results demonstrate MixCon’s superior throughput, outperforming Mixtral by 4.5 times and Jamba by 1.5 times when processing lengthy sequences of up to 128K tokens on a single A800 80GB GPU. Moreover, MixCon achieves top-tier scores on academic benchmarks, exemplified by its outstanding performance with a score of 87.9% on HellaSwag and 83.4% on WinoGrande, showcasing its capability to excel in complex sequence modeling tasks.
On Explaining with Attention Matrices
Authors: Naim Omar , Asher Nicholas
Published: October 2024

This paper explores the much discussed, possible explanatory link between attention weights (AW) in transformer models and predicted output. Contrary to intuition and early research on attention, more recent prior research has provided formal arguments and empirical evidence that AW are not explanatorily relevant. We show that the formal arguments are incorrect. We introduce and effectively compute efficient attention, which isolates the effective components of attention matrices in tasks and models in which AW play an explanatory role. We show that efficient attention has a causal role (provides minimally necessary and sufficient conditions) for predicting model output in NLP tasks requiring contextual information, and we show, contrary to [7], that efficient attention matrices are probability distributions and are effectively calculable. Thus, they should play an important part in the explanation of attention based model behavior. We offer empirical experiments in support of our method illustrating various properties of efficient attention with various metrics on four datasets.
Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
Authors: Marzari Luca , Leofante Francesco , Cicalese Ferdinando , Farinelli Alessandro
Published: October 2024

We study the problem of assessing the robustness of counterfactual explanations for deep learning models. We focus on plausible model shifts altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete. As this (practically) rules out the existence of scalable algorithms for exactly computing robustness, we propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees while preserving scalability. Remarkably, and differently from existing solutions targeting plausible model shifts, our approach does not impose requirements on the network to be analyzed, thus enabling robustness analysis on a wider range of architectures. Experiments on four binary classification datasets indicate that our method improves the state of the art in generating robust explanations, outperforming existing methods on a range of metrics.
Understanding the Impact of Human Oversight on Discriminatory Outcomes in AI-Supported Decision-Making
Authors: Gaudeul Alexia , Arrigoni Ottla , Charisi Vicky , Escobar-Planas Marina , Hupont Isabelle
Published: October 2024

This large-scale study assesses the impact of human oversight on countering discrimination in AI-aided decision-making for sensitive tasks. It follows a mixed method approach, including a quantitative experiment with Human Resources (HR) and banking professionals in Italy and Germany (N=1411), and qualitative analyses through interviews and workshops with participants and fair AI experts. The results show that human overseers were equally likely to follow advice from a fair AI as from a generic, discriminatory AI. Human oversight does not prevent discrimination by the generic AI. Fair AI reduces gender bias but not nationality bias. Participants’ choices are neither more nor less responsive to their preferences when using an AI or when left on their own. Interviews and workshops with participants highlight individual, organizational and societal biases. In case of conflict, participants prioritize their company’s interests over their own view of fairness. Participants also ask for better guidance on when to override AI recommendations. Fair AI experts stress the need for a comprehensive approach when designing oversight systems. Both technological and social aspects should be taken into consideration to ensure fairness.
Designing an XAI Interface for Tree-Based ML Models
Authors: Audemard Gilles , Coste-Marquis Sylvie , Marquis Pierre , Sabiri Mehdi , Szczepanski Nicolas
Published: October 2024

We present and evaluate empirically an XAI protocol for ruling interactions between a tree-based ML model (the AI system) and its user U, in the context of a prediction task. The pieces of knowledge held by U concerning the prediction task are supposed to be representable by a set of classification rules that is reliable and consistent, but (typically) incomplete. The proposed protocol aims to help U decide what to do with each prediction made by AI (accept it, reject it). It also aims to improve the quality of further predictions made by AI thanks to the expertise of U, and, reciprocally, to complete the pieces of knowledge held by U by leveraging the predictions made by AI. Experiments show that the approach can prove valuable in practice.
Fairness Auditing with Multi-Agent Collaboration
Authors: de Vos Martijn , Dhasade Akash , Garcia Bourrée Jade , Kermarrec Anne-Marie , Le Merrer Erwan , Rottembourg Benoît , Tredan Gilles
Published: October 2024

Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, with or without coordination beforehand, and their strategy for sampling appropriate data points. We theoretically compare the interplay of these levers. Our main findings are that (i) collaboration is generally beneficial for accurate audits, (ii) basic sampling methods often prove to be effective, and (iii) counter-intuitively, extensive coordination on queries often deteriorates audits accuracy as the number of agents increases. Experiments on three large datasets confirm our theoretical results. Our findings motivate collaboration during fairness audits of platforms that use ML models for decision-making.
Explaining a Probabilistic Prediction on the Simplex with Shapley Compositions
Authors: Noé Paul-Gauthier , Perelló-Nieto Miquel , Bonastre Jean-François , Flach Peter
Published: October 2024

Originating in game theory, Shapley values are widely used for explaining a machine learning model’s prediction by quantifying the contribution of each feature’s value to the prediction. This requires a scalar prediction as in binary classification, whereas a multiclass probabilistic prediction is a discrete probability distribution, living on a multidimensional simplex. In such a multiclass setting the Shapley values are typically computed separately on each class in a one-vs-rest manner, ignoring the compositional nature of the output distribution. In this paper, we introduce Shapley compositions as a well-founded way to properly explain a multiclass probabilistic prediction, using the Aitchison geometry from compositional data analysis. We prove that the Shapley composition is the unique quantity satisfying linearity, symmetry and efficiency on the Aitchison simplex, extending the corresponding axiomatic properties of the standard Shapley value. We demonstrate this proper multiclass treatment in a range of scenarios.
Vanilla Gradient Descent for Oblique Decision Trees
Authors: Panda Subrat Prasad , Genest Blaise , Easwaran Arvind , Suganthan Ponnuthurai Nagaratnam
Published: October 2024

Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, especially for oblique DTs, and does take a significant training time. Further, DTs suffer from overfitting, e.g., they proverbially “do not generalize” in regression tasks. Recently, some works proposed ways to make (oblique) DTs differentiable. This enables highly efficient gradient-descent algorithms to be used to learn DTs. It also enables generalizing capabilities by learning regressors at the leaves simultaneously with the decisions in the tree. Prior approaches to making DTs differentiable rely either on probabilistic approximations at the tree’s internal nodes (soft DTs) or on approximations in gradient computation at the internal node (quantized gradient descent). In this work, we propose DTSemNet, a novel semantically equivalent and invertible encoding for (hard, oblique) DTs as Neural Networks (NNs), that uses standard vanilla gradient descent. Experiments across various classification and regression benchmarks show that oblique DTs learned using DTSemNet are more accurate than oblique DTs of similar size learned using state-of-the-art techniques. Further, DT training time is significantly reduced. We also experimentally demonstrate that DTSemNet can learn DT policies as efficiently as NN policies in the Reinforcement Learning (RL) setup with physical inputs (dimensions ≤32). The code is available at https://github.com/CPS-research-group/dtsemnet.
Measuring User Understanding in Dialogue-Based xAI Systems
Authors: Mindlin Dimitry , Robrecht Amelie Sophie , Morasch Michael , Cimiano Philipp
Published: October 2024

The field of eXplainable Artificial Intelligence (xAI) is increasingly recognizing the need to personalize and interactively adapt the explanation to reflect better users’ explanation needs. While dialogue-based approaches to xAI have been proposed recently, the state-of-the-art in xAI is still characterized by what we call one-shot, non-personalized, and one-way explanations. In contrast, dialogue-based systems that can adapt explanations through interaction with a user promise to be superior to GUI-based or dashboard explanations as they offer a more intuitive way of requesting information. In general, while interactive xAI systems are often evaluated in terms of user satisfaction, there are limited studies that access user’s objective model understanding. This is in particular the case for dialogue-based xAI approaches. In this paper, we close this gap by carrying out controlled experiments within a dialogue framework in which we measure the understanding of users in three phases by asking them to simulate the predictions of the model they are learning about. By this, we can quantify the level of understanding of how the model works, comparing the state before and after the interaction. We further analyze the data to reveal patterns of how the interaction between groups with high vs. low understanding differs. Overall, our work thus contributes to our understanding of the effectiveness of xAI approaches.
LoginMEA: Local-to-Global Interaction Network for Multi-Modal Entity Alignment
Authors: Su Taoyu , Zhang Xinghua , Sheng Jiawei , Zhang Zhenyu , Liu Tingwen
Published: October 2024

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs (MMKGs), whose entities can be associated with relational triples and related images. Most previous studies treat the graph structure as a special modality, and fuse different modality information with separate uni-modal encoders, neglecting valuable relational associations in modalities. Other studies refine each uni-modal information with graph structures, but may introduce unnecessary relations in specific modalities. To this end, we propose a novel local-to-global interaction network for MMEA, termed as LoginMEA. Particularly, we first fuse local multi-modal interactions to generate holistic entity semantics and then refine them with global relational interactions of entity neighbors. In this design, the uni-modal information is fused adaptively, and can be refined with relations accordingly. To enrich local interactions of multi-modal entity information, we devise modality weights and low-rank interactive fusion, allowing diverse impacts and element-level feature interactions among modalities. To capture global interactions of graph structures, we adopt relation reflection graph attention networks, which fully capture relational associations between entities. Extensive experiments demonstrate superior results of our method over 5 cross-KG or bilingual benchmark datasets, indicating the effectiveness of capturing local and global interactions.
Maximally Permissive Reward Machines
Authors: Varricchione Giovanni , Alechina Natasha , Dastani Mehdi , Logan Brian
Published: October 2024

Reward machines allow the definition of rewards for temporally extended tasks and behaviors. Specifying “informative” reward machines can be challenging. One way to address this is to generate reward machines from a high-level abstract description of the learning environment, using techniques such as AI planning. However, previous planning-based approaches generate a reward machine based on a single (sequential or partial-order) plan, and do not allow maximum flexibility to the learning agent. In this paper we propose a new approach to synthesising reward machines which is based on the set of partial order plans for a goal. We prove that learning using such “maximally permissive” reward machines results in higher rewards than learning using RMs based on a single plan. We present experimental results which support our theoretical claims by showing that our approach obtains higher rewards than the single-plan approach in practice.
Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset
Authors: Yan Xi , Westphal Patrick , Seliger Jan , Usbeck Ricardo
Published: October 2024

Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total, we create 85,368 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA systems.
Merge-and-Shrink Heuristics for SSPs with Prune Transformations
Authors: Klößner Thorsten , Torralba Álvaro , Steinmetz Marcel , Sievers Silvan
Published: October 2024

The merge-and-shrink framework is a powerful tool for constructing state-of-the-art admissible heuristics in classical planning. Recent work has begun generalizing the complex theory behind this framework to probabilistic planning in forms of stochastic shortest-path problems (SSPs). There however remain two important gaps. Firstly, although the previous work makes substantial efforts, the probabilistic merge-and-shrink theory is still incomplete, lacking in particular prune transformations, i.e., transformations discarding uninteresting states, effectively reducing the size of the abstraction without losing relevant information. Secondly, an actual implementation and experimental evaluation of the merge-and-shrink framework for SSPs is so far missing. Here, we round off the previous work by contributing both a theoretical analysis of prune transformations, as well as an empirical evaluation of merge-and-shrink heuristics. Our results show that merge-and-shrink heuristics outperform previous single abstraction heuristics, but do not quite reach the performance of state-of-the-art additive combinations of such heuristics yet.
Belief Erasure in Propositional Logic
Authors: Creignou Nadia , Ktari Raïda , Papini Odile
Published: October 2024

Belief change is an important topic of knowledge representation and reasoning in artificial intelligence. Within the logical framework, the AGM approach has become a standard and various belief change operations have been considered. While revision, contraction and updating have given rise to a great deal of work, erasure has so far attracted less interest. Erasure is to contraction what update is to revision.This article deals with the study of erasure within the framework of propositional logic. It extends Katsuno and Mendelzon’s approach with additional postulates capturing the minimality of change and proposes two representation theorems for erasure operators, one in terms of total preorders on interpretations, the other in terms of partial preorders on interpretations. Finally, it completes the work of Caridroit, Konieczny and Marquis for contraction by proposing a new representation theorem for contraction operators in terms of partial preorders on interpretations.
Modelling Diffusion of Dependent and Conflicting Behaviours with Dynamic Logic
Authors: de Senne Amorim Gabriel , Souza Marlo , Freitas Moreira Álvaro
Published: October 2024

We propose modeling different policies for behaviour diffusion in a network of agents considering conflict and dependency. In approaches based on the threshold-limited influence model, an agent adopts a behaviour if it has received enough influence from its social connections to surpass a given behaviour threshold. We also consider models where adopting a behaviour might depend on other behaviours and where behaviours might conflict. We assume a network with a fixed structure and undirected and same-weight connections between agents, i.e., a network where, at least in principle, any two agents have the same capacity to influence each other. We propose a minimal propositional dynamic logic language for reasoning about each diffusion policy, and we provide a sound and complete axiomatization for it.
Differentiable Logic Programming for Distant Supervision
Authors: Takemura Akihiro , Inoue Katsumi
Published: October 2024

We introduce a new method for integrating neural networks with logic programming in Neural-Symbolic AI (NeSy), aimed at learning with distant supervision, in which direct labels are unavailable. Unlike prior methods, our approach does not depend on symbolic solvers for reasoning about missing labels. Instead, it evaluates logical implications and constraints in a differentiable manner by embedding both neural network outputs and logic programs into matrices. This method facilitates more efficient learning under distant supervision. We evaluated our approach against existing methods while maintaining a constant volume of training data. The findings indicate that our method not only matches or exceeds the accuracy of other methods across various tasks but also speeds up the learning process. These results highlight the potential of our approach to enhance both accuracy and learning efficiency in NeSy applications.
The Form and the Content: Non-Monotonic Reasoning with Syntactic Contextual Filtering
Authors: Dupin de Saint-Cyr Florence , Bisquert Pierre
Published: October 2024

In order to avoid ambiguity and be efficient, the context in which a query is made can help to better target the relevant pieces of information from the knowledge base to be processed by the inference system. In this paper, we are interested in the notion of dynamical compartmentalization where the knowledge base that will be used for reasoning is dynamically extracted from the original base. Compartmentalization is a selection of a sub-base which is done according to a function, called refiner, and depending on this function some properties are satisfied. We introduce a particular syntactic refiner that uses a similarity symbol-based distance between a context (a multiset of variable symbols) and a formula of a knowledge base. We prove that the inference operator based on this refiner, called contextual inference, satisfies a series of desirable axioms
CAMAOT: Channel-Aware Multi-Camera Active Object Tracking System
Authors: Yin Maolong , Guo Bin , Sun Zhuo , Wu Lei , Hao Zhaotie , Yu Zhiwen
Published: October 2024

Multi-Camera Active Object Tracking is an attractive technique in the area of intelligent surveillance, where cameras share their observations via the wireless communication to collaboratively track the target. Due to the variability in wireless channel, the dynamic transmission delay between cameras significantly affects the collaboration performance, especially when the tracking is time-sensitive. In this paper, we propose a channel-aware multi-camera active object tracking (CAMAOT) system, to achieve the stable and improved tracking performance. Specifically, a communication decision module is designed in CAMAOT, where the cameras’ communication graph and communication resource allocation adapt to the channels. Our experiments demonstrate that for time-varying channels, CAMAOT has a stable performance improvement over other systems, particularly when the communication resources are limited.
Automated Synthesis of Certified Neural Networks
Authors: Zavatteri Matteo , Bresolin Davide , Navarin Nicolò
Published: October 2024

Neural networks find applications in many safety-critical systems that raise concerns about their deployment: Are we sure the network will never advise doing anything violating a set of safety constraints? Formal verification has been recently applied to prove whether an existing neural network is certified for some property (i.e., if it satisfies the property for all possible inputs) or not. Formal verification can prove that a network respects the property, but cannot fix a network that does not respect it. In this paper we focus on the automated synthesis of certified neural networks, that is, on how to automatically build a network that is guaranteed to respect some required properties. We exploit a Counter Example Guided Inductive Synthesis (CEGIS) loop that alternates Deep Learning, Formal Verification, and a novel data generation technique that augments the training data to synthesize certified networks in a fully automatic way. An application of a proof-of-concept implementation of the framework shows the feasibility of the approach.
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Authors: Verreet Victor , De Smet Lennert , De Raedt Luc , Sansone Emanuele
Published: October 2024

Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabilistic logic. Learning corresponds to likelihood optimization of the neural networks. However, to obtain the likelihood exactly, expensive probabilistic logic inference is required. To scale learning to more complex systems, we therefore propose to instead optimize a sampling based objective. We prove that the objective has a bounded error with respect to the likelihood, which vanishes when increasing the sample count. Furthermore, the error vanishes faster by exploiting a new concept of sample diversity. We then develop the EXPLAIN, AGREE, LEARN (EXAL) method that uses this objective. EXPLAIN samples explanations for the data. AGREE reweighs each explanation in concordance with the neural component. LEARN uses the reweighed explanations as a signal for learning. In contrast to previous NeSy methods, EXAL can scale to larger problem sizes while retaining theoretical guarantees on the error. Experimentally, our theoretical claims are verified and EXAL outperforms recent NeSy methods when scaling up the MNIST addition and Warcraft pathfinding problems.
stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic
Authors: Saveri Gaia , Nenzi Laura , Bortolussi Luca , Křetínský Jan
Published: October 2024

Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable gap exists due to the discreteness of symbolic representations and the continuous nature of machine-learning computations. One of the desired bridges between these two worlds would be to define semantically grounded vector representation (feature embedding) of logic formulae, thus enabling to perform continuous learning and optimization in the semantic space of formulae. We tackle this goal for knowledge expressed in Signal Temporal Logic (STL) and devise a method to compute continuous embeddings of formulae with several desirable properties: the embedding (i) is finite-dimensional, (ii) faithfully reflects the semantics of the formulae, (iii) does not require any learning but instead is defined from basic principles, (iv) is interpretable. Another significant contribution lies in demonstrating the efficacy of the approach in two tasks: learning model checking, where we predict the probability of requirements being satisfied in stochastic processes; and integrating the embeddings into a neuro-symbolic framework, to constrain the output of a deep-learning generative model to comply to a given logical specification.
Ontology Text Alignment: Aligning Textual Content to Terminological Axioms
Authors: Chen Jieying , Dong Hang , Chen Jiaoyan , Horrocks Ian
Published: October 2024

Despite the impressive advancements in Large Language Models (LLMs), their ability to perform reasoning and provide explainable outcomes remains a challenge, underscoring the continued relevance of ontologies in certain areas, particularly due to the reasoning and validation capabilities of ontologies. Ontology modelling and semantic search, due to their inherent complexity, still demand considerable human effort and expertise. Addressing this gap, our paper introduces the problem of ontology text alignment, which involves finding the most relevant axioms with respect to the given reference text. We propose an advanced Retrieval Augmented Generation framework that leverages BERT models and generative LLMs, together with ontology semantic enhancement based on atomic decomposition. Additionally, we have developed benchmarks in geology and biomedical areas. Our evaluation demonstrates the positive impact of our framework.