bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Error Patterns to Correct Prediction Intervals
Authors: Bonnier Thomas
Published: September 2023

When assessing uncertainty in model predictions, it is key to consider potential error patterns in some regions of the feature space. In this paper, we build on quantile regression to propose a new method to produce prediction intervals in regression tasks. It estimates a conditional quantile function of the residual variable given a specific representation. The method then adjusts the regressor’s prediction with an upper and lower conditional quantile prediction in order to produce an adaptive prediction interval for any new input. Further, we suggest an additional layer based on conformal prediction in order to provide coverage guarantees. Lastly, as distribution-free conditional coverage is impossible to achieve, we suggest a tree-based representation which displays patterns of undercoverage. This diagnostic tool aims to reveal which regions of the feature space are significantly less likely to have trustworthy prediction intervals. In order to prove their efficacy, our techniques are tested over various use cases and compared against four main baselines. Our methods empirically achieve the expected coverage and tend to produce shorter intervals.
A Semantic Approach to Decidability in Epistemic Planning
Authors: Burigana Alessandro , Felli Paolo , Montali Marco , Troquard Nicolas
Published: September 2023

The use of Dynamic Epistemic Logic (DEL) in multi-agent planning has led to a widely adopted action formalism that can handle nondeterminism, partial observability and arbitrary knowledge nesting. As such expressive power comes at the cost of undecidability, several decidable fragments have been isolated, mainly based on syntactic restrictions of the action formalism. In this paper, we pursue a novel semantic approach to achieve decidability. Namely, rather than imposing syntactical constraints, the semantic approach focuses on the axioms of the logic for epistemic planning. Specifically, we augment the logic of knowledge S5n and with an interaction axiom called (knowledge) commutativity, which controls the ability of agents to unboundedly reason on the knowledge of other agents. We then provide a threefold contribution. First, we show that the resulting epistemic planning problem is decidable. In doing so, we prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest. Second, we study different generalizations of the commutativity axiom, with the goal of obtaining decidability for more expressive fragments of DEL. Finally, we show that two well-known epistemic planning systems based on action templates, when interpreted under the setting of knowledge, conform to the commutativity axiom, hence proving their decidability.
Enhancing OOD Generalization in Offline Reinforcement Learning with Energy-Based Policy Optimization
Authors: Cao Hongye , Yang Shangdong , Huo Jing , Chen Xingguo , Gao Yang
Published: September 2023

Offline Reinforcement Learning (RL) is an important research domain for real-world applications because it can avert expensive and dangerous online exploration. Offline RL is prone to extrapolation errors caused by the distribution shift between offline datasets and states visited by behavior policy. Existing offline RL methods constrain the policy to offline behavior to prevent extrapolation errors. But these methods limit the generalization potential of agents in Out-Of-Distribution (OOD) regions and cannot effectively evaluate OOD generalization behavior. To improve the generalization of the policy in OOD regions while avoiding extrapolation errors, we propose an Energy-Based Policy Optimization (EBPO) method for OOD generalization. An energy function based on the distribution of offline data is proposed for the evaluation of OOD generalization behavior, instead of relying on model discrepancies to constrain the policy. The way of quantifying exploration behavior in terms of energy values can balance the return and risk. To improve the stability of generalization and solve the problem of sparse reward in complex environment, episodic memory is applied to store successful experiences that can improve sample efficiency. Extensive experiments on the D4RL datasets demonstrate that EBPO outperforms the state-of-the-art methods and achieves robust performance on challenging tasks that require OOD generalization.
Learning Dual Mean Field Games on Graphs
Authors: Chen Xu , Liu Shuo , Di Xuan
Published: September 2023

Reinforcement learning (RL) has been developed for mean field games over graphs (G-MFG) in social media and network economics, in which the transition of agents between a node pair incurs an instantaneous reward. However, agents’ en-route choices on edges are largely neglected that incur an experienced reward depending on agents’ actions and population evolution along edges. Here we focus on a broader class of MFGs, named “dual MFG on graphs” (G-dMFG), which models two interacting MFGs, namely, one on edges and one at nodes over a graph. In this setting, agents select travel speed along edges and next-go-to edge at nodes for a minimum cumulative cost, which arises from the congestion effect when many agents compete for the same resource. This has various implications for autonomous driving navigation, spatial resource allocation, and internet packet routing. We establish formally that G-dMFG is a generic G-MFG, encompassing a more complex cost structure (that is nonseparable between states and actions) and with no need to pre-specify a termination time horizon. RL algorithms are designed to solve mean field equilibria (MFE) on large networks.
Reinforcement Learning for Bandits with Continuous Actions and Large Context Spaces
Authors: Duckworth Paul , Vallis Katherine A. , Lacerda Bruno , Hawes Nick
Published: September 2023

We consider the challenging scenario of contextual bandits with continuous actions and large context spaces. This is an increasingly important application area in personalised healthcare where an agent is requested to make dosing decisions based on a patient’s single image scan. In this paper, we first adapt a reinforcement learning (RL) algorithm for continuous control to outperform contextual bandit algorithms specifically hand-crafted for continuous action spaces. We empirically demonstrate this on a suite of standard benchmark datasets for vector contexts. Secondly, we demonstrate that our RL agent can generalise problems with continuous actions to large context spaces, providing results that outperform previous methods on image contexts. Thirdly, we introduce a new contextual bandits test domain with multi-dimensional continuous action space and image contexts which existing tree-based methods cannot handle. We provide initial results with our RL agent.
Heteroscedastic Causal Structure Learning
Authors: Duong Bao , Nguyen Thin
Published: September 2023

Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent trend of studies has shown that it is possible to recover the DAGs with polynomial time complexity under the equal variances assumption. However, this prohibits the heteroscedasticity of the noise, which allows for more flexible modeling capabilities, but at the same time is substantially more challenging to handle. In this study, we tackle the heteroscedastic causal structure learning problem under Gaussian noises. By exploiting the normality of the causal mechanisms, we can recover a valid causal ordering, which can uniquely identify the causal DAG using a series of conditional independence tests. The result is HOST (Heteroscedastic causal STructure learning), a simple yet effective causal structure learning algorithm that scales polynomially in both sample size and dimensionality. In addition, via extensive empirical evaluations on a wide range of both controlled and real datasets, we show that the proposed HOST method is competitive with state-of-the-art approaches in both the causal order learning and structure learning problems.
Settling the Score: Portioning with Cardinal Preferences
Authors: Elkind Edith , Suksompong Warut , Teh Nicholas
Published: September 2023

We study a portioning setting in which a public resource such as time or money is to be divided among a given set of candidates, and each agent proposes a division of the resource. We consider two families of aggregation rules for this setting—those based on coordinate-wise aggregation and those that optimize some notion of welfare—as well as the recently proposed Independent Markets mechanism. We provide a detailed analysis of these rules from an axiomatic perspective, both for classic axioms, such as strategyproofness and Pareto optimality, and for novel axioms, which aim to capture proportionality in this setting. Our results indicate that a simple rule that computes the average of all proposals satisfies many of our axioms, including some that are violated by more sophisticated rules.
Optimal Alignment of Temporal Knowledge Bases
Authors: Fernández Gil Oliver , Patrizi Fabio , Perelli Giuseppe , Turhan Anni-Yasmin
Published: September 2023

Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally changes the TKB, but entails the given temporal CQ and is in that sense (cost-) optimal. We investigate this problem for ALC TKBs and conjunctive queries with LTL operators and devise a solution technique to compute (cost-optimal) alignments of TKBs that extends techniques for the alignment problem for propositional LTL over finite traces.
Using Weighted Matching to Solve 2-Approval/Veto Control and Bribery
Authors: Fitzsimmons Zack , Hemaspaandra Edith
Published: September 2023

Determining the complexity of election attack problems is a major research direction in the computational study of voting problems. The paper “Towards completing the puzzle: complexity of control by replacing, adding, and deleting candidates or voters” by Erdélyi et al. (JAAMAS 2021) provides a comprehensive study of the complexity of control problems. The sole open problem is constructive control by replacing voters for 2-Approval. We show that this case is in P, strengthening the recent RP (randomized polynomial-time) upper bound due to Fitzsimmons and Hemaspaandra (IJCAI 2022). We show this by transforming 2-Approval CCRV to weighted matching. We also use this approach to show that priced bribery for 2-Veto elections is in P. With this result, and the accompanying (unsurprising) result that priced bribery for 3-Veto elections is NP-complete, this settles the complexity for k-Approval and k-Veto standard control and bribery cases.
Scaling-Up LAO* in FOND Planning: An Ablation Study
Authors: Fraga Pereira Ramon
Published: September 2023

The use of multi-queue heuristic search and tie-breaking strategies has shown to be very effective for satisficing planning in the Classical Planning setting. However, to the best of our knowledge, the use of such techniques has never been studied and employed in heuristic search algorithms for Fully Observable Non-Deterministic (FOND) Planning. In this paper, we adapt existing satisficing techniques for scaling-up an AND/OR heuristic search algorithm for FOND Planning. Namely, we employ multi-queue heuristic search, dead-end detection, and tie-breaking strategies in LAO* for improving the extraction of strong-cyclic policies. We assess the efficiency of our techniques in LAO* through an extensive ablation study over two different FOND Planning benchmarks. Empirical results show that our techniques improve the performance of LAO* in terms of coverage, expanded nodes, and planning time compared to a well-known planner based on vanilla LAO*. Indeed, the best configuration of our techniques is competitive with the current state-of-the-art in FOND Planning.
Revision Transformers: Instructing Language Models to Change Their Values
Authors: Friedrich Felix , Stammer Wolfgang , Schramowski Patrick , Kersting Kristian
Published: September 2023

Current transformer language models (LM) are large-scale models with billions of parameters. They have been shown to provide high performances on a variety of tasks but are also prone to shortcut learning and bias. Addressing such incorrect model behavior via parameter adjustments is very costly. This is particularly problematic for updating dynamic concepts, such as moral values, which vary culturally or interpersonally. In this work, we question the current common practice of storing all information in the model parameters and propose the Revision Transformer (RiT) to facilitate easy model updating. The specific combination of a large-scale pre-trained LM that inherently but also diffusely encodes world knowledge with a clear-structured revision engine makes it possible to update the model’s knowledge with little effort and the help of user interaction. We exemplify RiT on a moral dataset and simulate user feedback demonstrating strong performance in model revision even with small data. This way, users can easily design a model regarding their preferences, paving the way for more transparent AI models.
Towards Trustworthy NLP: An Adversarial Robustness Enhancement Based on Perplexity Difference
Authors: Ge Zhaocheng , Hu Hanping , Zhao Tengfei
Published: September 2023

The vulnerability of artificial intelligence has emerged as a bottleneck, with adversarial attacks posing a significant threat to natural language processing. Although multiple defense mechanisms have been proposed, they often suffer from strict constraints, weak generalization, and low scalability. To address these challenges, we propose leveraging perplexity to quantify the difference between clean and adversarial examples based on the observation of numerous cases. We then statistically prove the substantial difference between them using Bayesian hypothesis testing. Subsequently, we develop an adversarial defense framework named UMPS, which contains two branches: “Uncovering the Mask”(UM) and “Perplexity-guided Sampling”(PS). UM utilizes a masked language model and Jaro-Winkler distance constraint to recover out-of-vocabulary words, while PS employs perplexity to locate the optimal sample within a convex hull which is constructed with integrated gradients. Theoretically, the proposed framework fulfills three requirements: effectiveness, universality, and portability. The experimental results demonstrate that UMPS effectively enhances the robustness of language models including BERT, against advanced attacks and outperforms three strong baseline methods. Furthermore, we conduct an instance analysis to illustrate how UMPS functions and what it outputs, an ablation study to support the validity and necessity of the two branches, and an post-hoc test on the difference in perplexity to explains the defense performance of our framework.
A Hybrid Approach to Preference Learning with Interaction Terms
Authors: Gilbert Hugo , Ouaguenouni Mohamed , Öztürk Meltem , Spanjaard Olivier
Published: September 2023

Preference learning is an essential component in numerous applications, such as recommendation systems, decision-making processes, and personalized services. We propose here a novel approach to preference learning that interleaves Gaussian Processes (GP) and Robust Ordinal Regression (ROR). A Gaussian process gives a probability distribution on the latent function values that generate users’ preferences. Our method extends the traditional non-parametric Gaussian process framework by approximating the latent function by a very flexible parameterized function, that we call θ-additive function, where θ is the parameter set. The set θ reflects the degree of sophistication of the generalized additive model that can potentially represent the user’s preferences. To learn what are the components of θ, we update a probability distribution on the space of all possible sets θ, depending on the ability of the parameterized function to approximate the latent function. We predict pairwise preferences by using the parameter set θ that maximizes the posterior distribution and by performing robust ordinal regression based on this parameter set. Experimental results on synthetic data demonstrate the effectiveness and robustness of our proposed methodology.
XGBD: Explanation-Guided Graph Backdoor Detection
Authors: Guan Zihan , Du Mengnan , Liu Ninghao
Published: September 2023

Backdoor attacks pose a significant security risk to graph learning models. Backdoors can be embedded into the target model by inserting backdoor triggers into the training dataset, causing the model to make incorrect predictions when the trigger is present. To counter backdoor attacks, backdoor detection has been proposed. An emerging detection strategy in the vision and NLP domains is based on an intriguing phenomenon: when training models on a mixture of backdoor and clean samples, the loss on backdoor samples drops significantly faster than on clean samples, allowing backdoor samples to be easily detected by selecting samples with the lowest loss values. However, the ignorance of topological feature information on graph data limits its detection effectiveness when applied directly to the graph domain. To this end, we propose an explanation-guided backdoor detection method to take advantage of the topological information. Specifically, we train a helper model on the graph dataset, feed graph samples into the model, and then adopt explanation methods to attribute model prediction to an important subgraph. We observe that backdoor samples have distinct attribution distribution than clean samples, so the explanatory subgraph could serve as more discriminative features for detecting backdoor samples. Comprehensive experiments on multiple popular datasets and attack methods demonstrate the effectiveness and explainability of our method. Our code is available: https://github.com/GuanZihan/GNN_backdoor_detection.
Preserving Semantics in Textual Adversarial Attacks
Authors: Herel David , Cisneros Hugo , Mikolov Tomas
Published: September 2023

The growth of hateful online content, or hate speech, has been associated with a global increase in violent crimes against minorities [23]. Harmful online content can be produced easily, automatically and anonymously. Even though, some form of auto-detection is already achieved through text classifiers in NLP, they can be fooled by adversarial attacks. To strengthen existing systems and stay ahead of attackers, we need better adversarial attacks. In this paper, we show that up to 70% of adversarial examples generated by adversarial attacks should be discarded because they do not preserve semantics. We address this core weakness and propose a new, fully supervised sentence embedding technique called Semantics-Preserving-Encoder (SPE). Our method outperforms existing sentence encoders used in adversarial attacks by achieving 1.2× ∼ 5.1× better real attack success rate. We release our code as a plugin that can be used in any existing adversarial attack to improve its quality and speed up its execution. (The code, datasets and test examples are available at https://github.com/DavidHerel/semantics-preserving-encoder.)
Gaifman Graphs in Lifted Planning
Authors: Horčík Rostislav , Fišer Daniel
Published: September 2023

We introduce the metric induced by Gaifman graphs into lifted planning. We analyze what kind of information this metric carries and how it can be utilized for constructing lifted delete-free relaxation heuristics. In particular, we prove how the action dynamics influence the distances between objects. As a corollary, we derive a lower bound on the length of any plan. Finally, we apply our theoretical findings on the Gaifman graphs to improve the delete-free relaxation heuristics induced by PDDL homomorphisms.
From Decision Trees to Explained Decision Sets
Authors: Huang Xuanxiang , Marques-Silva Joao
Published: September 2023

Recent work demonstrated that path explanation redundancy is ubiquitous in decision trees, i.e. most often paths in decision trees include literals that are redundant for explaining a prediction. The implication of this result is that decision trees must be explained. Nevertheless, there are applications of DTs where running an explanation algorithm is impractical. For example, in settings that are time or power constrained, running software algorithms for explaining predictions would be undesirable. Although the explanations for paths in DTs do not generally represent themselves a decision tree, this paper shows that one can construct a decision set from some of the decision tree explanations, such that the decision set is not only explained, but it also exhibits a number of properties that are critical for replacing the original decision tree.
T-VAKS: A Tutoring-Based Multimodal Dialog System via Knowledge Selection
Authors: Jain Raghav , Saha Tulika , Saha Sriparna
Published: September 2023

Advancements in Conversational Natural Language Processing (NLP) have the potential to address critical social challenges, particularly in achieving the United Nations’ Sustainable Development Goal of quality education. However, the application of NLP in the educational domain, especially language learning, has been limited due to the inherent complexities of the field and the scarcity of available datasets. In this paper, we introduce T-VAKS (Tutoring Virtual Agent with Knowledge Selection), a novel language tutoring multimodal Virtual Agent (VA) designed to assist students in learning a new language, thereby promoting AI for Social Good. T-VAKS aims to bridge the gap between NLP and the educational domain, enabling more effective language tutoring through intelligent virtual agents. Our approach employs an information theory-based knowledge selection module built on top of a multimodal seq2seq generative model, facilitating the generation of appropriate, informative, and contextually relevant tutor responses. The knowledge selection module in turn consists of two sub-modules: (i) knowledge relevance estimation, and (ii) knowledge focusing framework. We evaluate the performance of our proposed end-to-end dialog system against various baseline models and the most recent state-of-the-art models, using multiple evaluation metrics. The results demonstrate that T-VAKS outperforms competing models, highlighting the potential of our approach in enhancing language learning through the use of conversational NLP and virtual agents, ultimately contributing to addressing social challenges and promoting well-being.
Complexity of Control by Adding or Deleting Edges in Graph-Restricted Weighted Voting Games
Authors: Kaczmarek Joanna , Rothe Jörg , Talmon Nimrod
Published: September 2023

Graph-restricted weighted voting games generalize weighted voting games, a well-studied class of succinct simple games, by embedding them into a communication structure: a graph whose vertices are the players some of which are connected by edges. In such games, only connected coalitions are taken into consideration for calculating the players’ power indices. We focus on the probabilistic Penrose–Banzhaf index [5] and the Shapley–Shubik index [18] and study the computational complexity of manipulating these games by an external agent who can add edges to or delete edges from the graph. For the problems modeling such scenarios, we raise some of the lower bounds obtained by Kaczmarek and Rothe [9] from NP- or DP-hardness to PP-hardness, where PP is probabilistic polynomial time. We also solve one of their open problems by showing that it is a coNP-hard problem to maintain the Shapley–Shubik index of a given player in a graph-restricted weighted voting game when edges are deleted.
Satisfaction-Maximizing Optimal Stopping
Authors: Koren Stav , Sarne David
Published: September 2023

In recent years, autonomous agents have been increasingly handling decision tasks on behalf of their human users. One such type of task with much potential to be carried out by an assisting autonomous agent is optimal stopping (e.g., in costly search). In such case, when it is the agent’s responsibility to decide when to terminate search, the challenge of maximizing user satisfaction with the process becomes acute. This paper provides evidence for the loose correlation between agent performance, profit-wise, and user satisfaction in this application domain, ruling out the use of the profit-maximizing strategy. As an alternative, it proposes a strategy relying on behavioral features. An extensive comparative evaluation of the proposed strategy, as well as the profit-maximizing strategy and the highest ranked strategy elicited through crowdsourcing reveals that the average satisfaction with the first is substantially greater than when experiencing with the others. The analysis of the results also reveals several important insights related to people’s ability to estimate the effectiveness of search strategies, satisfaction-wise, or propose such strategies themselves, contributing to the study of how human beings deal with optimal stopping problems in practice.
QCCDM: A Q-Augmented Causal Cognitive Diagnosis Model for Student Learning
Authors: Liu Shuo , Qian Hong , Li Mingjia , Zhou Aimin
Published: September 2023

Cognitive diagnosis is vital for intelligent education to determine students’ knowledge mastery levels from their response logs. The Q-matrix, representing the relationships between exercises and knowledge attributes, improves the interpretability of cognitive diagnosis models. However, completing the Q-matrix poses an expensive and challenging task due to the fine-grained division of knowledge attributes. Moreover, a manually sparse Q-matrix can also compromise the accuracy and interpretability of deducing students’ mastery levels, especially for infrequently observed or unseen knowledge attributes. To address this issue, this paper proposes a Q-augmented Causal Cognitive Diagnosis Model (QCCDM) for student learning. Specifically, QCCDM incorporates the structure causal model (SCM) to capture the causality between students’ mastery levels on different attributes, which enables to infer their proficiency on rarely observed knowledge attributes with better accuracy and interpretability. Notably, with SCM, one can guide students on how to realize their self-improvement through intervention. Furthermore, we propose to augment the Q-matrix in QCCDM, which uses the manual Q-matrix as a prior to deduce the relationships between exercises and explicit as well as latent knowledge attributes, resulting in a complete and comprehensive assessment of students’ abilities. We assess the efficacy of Q-augmentation across the widely-used Q-based cognitive diagnosis models and conduct the ablation study. The extensive experimental results on real-world datasets show that QCCDM outperforms the compared methods in terms of both accuracy and interpretability.
Aspect-Oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
Authors: Liu Xueyi , Hou Rui , Gan Yanglei , Luo Da , Li Changlin , Shi Xiaojun , Liu Qiao
Published: September 2023

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.
Revisiting Graph Contrastive Learning for Anomaly Detection
Authors: Liu Zhiyuan , Cao Chunjie , Tao Fangjian , Sun Jingzhang
Published: September 2023

Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through graph augmentation and multi-scale contrast modules. However, the underlying mechanisms of how these modules work have not been fully explored. We dive into the multi-scale and graph augmentation mechanism and observed that multi-scale contrast modules do not enhance the expression, while the multi-GNN modules are the hidden contributors. Previous studies have tended to attribute the benefits brought by multi-GNN to the multi-scale modules. In the paper, we delve into the misconception and propose Multi-GNN and Augmented Graph contrastive framework MAG, which unified the existing GCAD methods in the contrastive self-supervised perspective. We extracted two variants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight instance of the MAG, which outperform the state-of-the-art on Cora and Pubmed with the low computational cost. The variant M-MAG equipped with multi-GNN modules further improve the detection performance. Our study sheds light on the drawback of the existing GCAD methods and demonstrates the potential of multi-GNN and graph augmentation modules. Our code is available at https://anonymous.4open.science/r/MAG-Framework-74D0.
Dynamic Workload-Aware Bike Rebalancing for Bike-Sharing Systems
Authors: Luo Yuan
Published: September 2023

Bike Sharing Systems (BSSs) offer a flexible and sustainable transport option that has gained popularity in urban areas globally. However, as users move bikes according to their own needs, imbalanced bike distribution becomes a significant challenge for BSS operators. To address this problem, we propose a Workload Awareness (WA) approach that considers the rebalancing workload of BSS sub-networks and congestion issues when repositioning bikes dynamically. Our algorithm, WA, identifies sub-networks in a BSS and ensures a similar rebalancing load for each sub-network. Our mixed integer nonlinear programming (MINLP) model then finds a repositioning policy for each sub-network, taking into account operator capacity, bike and dock information, and minimizing total losses due to bike shortages and dock congestion. Our experiments on the Ningbo City Bike system demonstrate that our approach outperforms state-of-the-art methods by reducing the loss of the system by up to 60% and significantly reducing computational time by up to 36%.
A Local Non-Additive Framework for Explaining Black-Box Predictive Models
Authors: Mohammadi Majid , Tiddi Ilaria , ten Teije Annette
Published: September 2023

Understanding the rationale behind the predictions made by machine learning models holds paramount importance across numerous applications. Various explainable models have been developed to shed light on these predictions by assessing the individual contributions of features to the outcome of black-box models. However, existing methods often overlook the crucial aspect of interactions among features, restricting the explanation to isolated feature attributions. In this paper, we introduce a novel Choquet integral-based explainable method, termed ChoquEx, which not only considers the interactions among features but also enables the computation of contributions for any subset of features. To achieve this, we propose an innovative algorithm based on support vector regression that efficiently estimates the contributions of all feature subsets. Intriguingly, we leverage game-theoretic concepts, including Shapley values and interaction index, to calculate both the feature importance and interaction strength. This approach adds further interpretability and insight into the model’s decision-making process. To evaluate the effectiveness of ChoquEx, we conduct extensive experiments on diverse real-world scenarios. Our results convincingly demonstrate the superiority of the proposed model over existing explainable techniques. With its ability to unravel feature interactions and furnish comprehensive explanations, ChoquEx significantly enhances our understanding of predictive models, opening new avenues for applying machine learning in critical domains that require transparent decision-making.
MaxSAT-Based Inconsistency Measurement
Authors: Niskanen Andreas , Kuhlmann Isabelle , Thimm Matthias , Järvisalo Matti
Published: September 2023

Inconsistency measurement aims at obtaining a quantitative assessment of the level of inconsistency in knowledge bases. While having such a quantitative assessment is beneficial in various settings, inconsistency measurement of propositional knowledge bases is under most existing measures a significantly challenging computational task. In this work, we harness Boolean satisfiability (SAT) based solving techniques for developing practical inconsistency measurement algorithms. Our algorithms—some of which constitute, to the best of our knowledge, the first practical approaches for specific inconsistency measures—are based on using natural choices of SAT-based techniques for the individual inconsistency measures, ranging from direct maximum satisfiability (MaxSAT) encodings to MaxSAT-based column generation techniques making use of incremental computations. We show through an extensive empirical evaluation that our approaches scale well in practice and significantly outperform recently-proposed answer set programming approaches to inconsistency measurement.
Exploring Information Bottleneck for Weakly Supervised Semantic Segmentation
Authors: Qin Jie , Lyu Yueming , Wang Xingang
Published: September 2023

Image-level weakly supervised semantic segmentation (WSSS) has attracted much attention due to the easily acquired class labels. Most existing methods resort to utilizing Class Activation Maps (CAMs) obtained from the classification network to play as the initial pseudo labels. However, the classifiers only focus on the most discriminative regions of the target objects, which is referred to as the information bottleneck from the perspective of the information theory. To alleviate this information bottleneck limitation, we propose an Information Perturbation Module (IPM) to explicitly obtain the information difference maps, which provide the accurate direction and magnitude of the information compression in the classification network. After that, an information bottleneck breakthrough mechanism with three branches is proposed to overcome the information bottleneck in the classification network for segmentation. Additionally, a diversity regularization on the generated two information difference maps is proposed to improve the diversity of the output CAMs. Extensive experiments on PASCAL VOC2012 val and test sets demonstrate that the proposed method can effectively improve the weakly supervised semantic segmentation performance of the advanced approaches.
The Problem of Coherence in Natural Language Explanations of Recommendations
Authors: Raczyński Jakub , Lango Mateusz , Stefanowski Jerzy
Published: September 2023

Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user. Although several methods for providing such explanations have recently been proposed, we argue that an important aspect of explanation quality has been overlooked in their experimental evaluation. Specifically, the coherence between generated text and predicted rating, which is a necessary condition for an explanation to be useful, is not properly captured by currently used evaluation measures. In this paper, we highlight the issue of explanation and prediction coherence by 1) presenting results from a manual verification of explanations generated by one of the state-of-the-art approaches 2) proposing a method of automatic coherence evaluation 3) introducing a new transformer-based method that aims to produce more coherent explanations than the state-of-the-art approaches 4) performing an experimental evaluation which demonstrates that this method significantly improves the explanation coherence without affecting the other aspects of recommendation performance.
Fair Latent Deep Generative Models (FLDGMs) for Syntax-Agnostic and Fair Synthetic Data Generation
Authors: Ramachandranpillai Resmi , Sikder Md Fahim , Heintz Fredrik
Published: September 2023

Deep Generative Models (DGMs) for generating synthetic data with properties such as quality, diversity, fidelity, and privacy is an important research topic. Fairness is one particular aspect that has not received the attention it deserves. One difficulty is training DGMs with an in-process fairness objective, which can disturb the global convergence characteristics. To address this, we propose Fair Latent Deep Generative Models (FLDGMs) as enablers for more flexible and stable training of fair DGMs, by first learning a syntax-agnostic, model-agnostic fair latent representation (low dimensional) of the data. This separates the fairness optimization and data generation processes thereby boosting stability and optimization performance. Moreover, data generation in the low dimensional space enhances the accessibility of models by reducing computational demands. We conduct extensive experiments on image and tabular domains using Generative Adversarial Networks (GANs) and Diffusion Models (DMs) and compare them to the state-of-the-art in terms of fairness and utility. Our proposed FLDGMs achieve superior performance in generating high-quality, high-fidelity, and high-diversity fair synthetic data compared to the state-of-the-art fair generative models.
Capsule Network with Label Dependency Modeling for Multi-Label Emotion Classification
Authors: Ruan Yu-Ping , Li Taihao
Published: September 2023

This paper proposes a simple-yet-efficient model, called capsule network with label dependency modeling (CapsLDM), for the task of multi-label emotion classification (MLEC) in text, in which multiple emotion categories can be assigned to the input data instance (e.g., a sentence). Unlike the traditional single-label emotion classification, the modeling of label (i.e., emotion) dependency plays an important role in MLEC, since the co-existing emotions in an utterance are not independent of each other. The capsule network has been successfully applied to many multi-label classification scenarios, however, the modeling of label dependency has not been considered in existing work. In our proposed CapsLDM model, we add similarity regularization terms on both the dynamic routing weights and the instance vectors of emotion capsules by exploiting the co-occurrence information of emotion labels, which resembles the dependency between different emotion categories for a certain input instance. Extensive experiments are conducted on four MLEC benchmark datasets with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of our proposed CapsLDM model and confirm the effectiveness of label dependency modeling in CapsLDM for the MLEC task.
Stackelberg Attacks on Auctions and Blockchain Transaction Fee Mechanisms
Authors: Landis Daji , Schwartzbach Nikolaj
Published: September 2023

We study a multi-unit single-demand auction in a setting where agents can arbitrarily commit to strategies that may depend on the commitments of other agents. Such commitments non-trivially change the equilibria of the auction by inducing a metagame, in which agents commit to strategies. We demonstrate a strategy an attacker may commit to that ensures they receive one such item for free, while forcing the remaining agents to enter a lottery for the remaining items. The attack is detrimental to the auctioneer, who loses most of their revenue. We show that the strategy works as long as the agents have valuations that are somewhat concentrated. The attack is robust to a large fraction of the agents being either oblivious to the attack or having exceptionally high valuations. The attacker may coerce these agents into cooperating by promising them a free item. We show that the conditions for the attack to work hold with high probability when (1) the auction is not too congested, and (2) the valuations are sampled i.i.d. from either a uniform distribution or a Pareto distribution. The attack works for first-price auctions, second-price auctions, and the transaction fee mechanism EIP-1559 used by Ethereum.
Progression for Monitoring in Temporal ASP
Authors: Soldà Davide , Lopez-Miguel Ignacio D. , Bartocci Ezio , Eiter Thomas
Published: September 2023

In recent years, there has been growing interest in the application of temporal reasoning approaches and non-monotonic logics from artificial intelligence in dynamic systems that generate data. A well-known approach to temporal reasoning is the use of a progression technique, which allows for the online computation of logical consequences of a logical knowledge base over time. We consider a progression technique for Temporal Here and There and Temporal Equilibrium Logic, which is the logic underlying answer programming over linear-temporal logic (LTL). Compared to usual LTL online computation, where the goal is to check whether a trace is compliant with a temporal specification, our approach provides also the means to compute non-monotonic temporal reasoning over a trace of observations. Besides formal notions and results, we also present an algorithm for performing progression to monitor a dynamic system, which has been implemented as a proof of concept and allows for handling expressive application scenarios.
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations
Authors: Wang Kunyu , Shi Juluan , Wang Wenxuan
Published: September 2023

Deep neural networks are susceptible to adversarial attacks, which pose a significant threat to their security and reliability in real-world applications. The most notable adversarial attacks are transfer-based attacks, where an adversary crafts an adversarial example to fool one model, which can also fool other models. While previous research has made progress in improving the transferability of untargeted adversarial examples, the generation of targeted adversarial examples that can transfer between models remains a challenging task. In this work, we present a novel approach to generate transferable targeted adversarial examples by exploiting the vulnerability of deep neural networks to perturbations on high-frequency components of images. We observe that replacing the high-frequency component of an image with that of another image can mislead deep models, motivating us to craft perturbations containing high-frequency information to achieve targeted attacks. To this end, we propose a method called Low-Frequency Adversarial Attack (LFAA), which trains a conditional generator to generate targeted adversarial perturbations that are then added to the low-frequency component of the image. Extensive experiments on ImageNet demonstrate that our proposed approach significantly outperforms state-of-the-art methods, improving targeted attack success rates by a margin from 3.2% to 15.5%.
Investigating the Learning Behaviour of In-Context Learning: A Comparison with Supervised Learning
Authors: Wang Xindi , Wang Yufei , Xu Can , Geng Xiubo , Zhang Bowen , Tao Chongyang , Rudzicz Frank , Mercer Robert E. , Jiang Daxin
Published: September 2023

Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been little understanding of how ICL learns the knowledge from the given prompts. In this paper, to make progress toward understanding the learning behaviour of ICL, we train the same LLMs with the same demonstration examples via ICL and supervised learning (SL), respectively, and investigate their performance under label perturbations (i.e., noisy labels and label imbalance) on a range of classification tasks. First, via extensive experiments, we find that gold labels have significant impacts on the downstream in-context performance, especially for large language models; however, imbalanced labels matter little to ICL across all model sizes. Second, when comparing with SL, we show empirically that ICL is less sensitive to label perturbations than SL, and ICL gradually attains comparable performance to SL as the model size increases.
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations
Authors: Xiao Qingfa , Li Shuangyin , Chen Lei
Published: September 2023

The enhancement of unsupervised learning of sentence representations has been significantly achieved by the utility of contrastive learning. This approach clusters the augmented positive instance with the anchor instance to create a desired embedding space. However, relying solely on the contrastive objective can result in sub-optimal outcomes due to its inability to differentiate subtle semantic variations between positive pairs. Specifically, common data augmentation techniques frequently introduce semantic distortion, leading to a semantic margin between the positive pair. While the InfoNCE loss function overlooks the semantic margin and prioritizes similarity maximization between positive pairs during training, leading to the insensitive semantic comprehension ability of the trained model. In this paper, we introduce a novel Identical and Fraternal Twins of Contrastive Learning (named IFTCL) framework, capable of simultaneously adapting to various positive pairs generated by different augmentation techniques. We propose a Twins Loss to preserve the innate margin during training and promote the potential of data enhancement in order to overcome the sub-optimal issue. We also present proof-of-concept experiments combined with the contrastive objective to prove the validity of the proposed Twins Loss. Furthermore, we propose a hippocampus queue mechanism to restore and reuse the negative instances without additional calculation, which further enhances the efficiency and performance of the IFCL. We verify the IFCL framework on nine semantic textual similarity tasks with both English and Chinese datasets, and the experimental results show that IFCL outperforms state-of-the-art methods.