GenAI4ELab/papercli-papers huggingface dataset:
Exploiting Cultural Biases via Homoglyphs inText-to-Image Synthesis (Abstract Reprint)
Authors: Lukas Struppek; Dominik Hintersdorf; Felix Friedrich; Manuel Brack; Patrick Schramowski; Kristian Kersting
Venue: IJCAI 2024 (main)

Abstract: Models for text-to-image synthesis, such as DALL-E 2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public. These models are capable of producing high-quality images that depict a variety of concepts and styles when conditioned on textual descriptions. However, these models adopt cultural characteristics associated with specific Unicode scripts from their vast amount of training data, which may not be immediately apparent. We show that by simply inserting single non-Latin characters in the textual description, common models reflect cultural biases in their generated images. We analyze this behavior both qualitatively and quantitatively and identify a model’s text encoder as the root cause of the phenomenon. Such behavior can be interpreted as a model feature, offering users a simple way to customize the image generation and reflect their own cultural background. Yet, malicious users or service providers may also try to intentionally bias the image generation. One goal might be to create racist stereotypes by replacing Latin characters with similarly-looking characters from non-Latin scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we propose a novel homoglyph unlearning method to fine-tune a text encoder, making it robust against homoglyph manipulations.
Towards Revolutionized Smart Grids: An AI-Driven Broker for Improved Operational Efficiency
Authors: Sanjay Chandlekar
Venue: IJCAI 2024 (main)

Abstract: Smart grid system encompasses large power plants in the wholesale market and retail customers in the tariff market. An electricity broker liaises between the wholesale and tariff markets by procuring electricity from the power plants and selling it to subscribed customers. In our work, we address the prominent challenges in the smart grid system to achieve better efficiency. We discuss the wholesale market, for which we design efficient bidding strategies in periodic double auctions (PDAs), and the tariff market, which includes tariff contract generation strategies and peak demand mitigation strategies. We use the PowerTAC simulator as a test-bed; also utilise these strategies for our autonomous broker, VidyutVanika, which has been proven efficient in the PowerTAC tournaments.
Causal Graph Modeling with Deep Neural Engines for Strong Abstract Reasoning in Language and Vision
Authors: Gaël Gendron
Venue: IJCAI 2024 (main)

Abstract: Deep learning (DL) relies on discovering correlation patterns in low-level data and aggregating the information to solve a task. Despite success in a wide variety of applications, ranging from natural language to vision tasks, the learned patterns are often brittle and do not transfer out of the training data distribution (i.e. to different domains). Causality theory proposes methods to discover and estimate cause-effect relationships beyond correlations. Its powerful inference frameworks have been recently highlighted as a potential way to improve the lack of out-of-distribution generalisation in deep neural networks. However, their applications to deep learning problems remain largely under-explored. Our work attempts to bridge this gap and apply causal graphical models to abstract and causal reasoning problems in natural language and vision, requiring strong generalisation abilities beyond correlations. We integrate causal graph modelling methods into deep vision networks and Large Language Models to improve their capacity to perform strong and out-of-distribution reasoning on complex abstract problems.
NeuroSymbolic LLM for Mathematical Reasoning and Software Engineering
Authors: Prithwish Jana
Venue: IJCAI 2024 (main)

Abstract: In recent years, there has been a significant interest in Large Language Models (LLMs) owing to their notable performance in natural language processing (NLP) tasks. However, while their results show promise in mathematical reasoning and software engineering tasks, LLMs have not yet achieved a satisfactory performance level in these domains. In response, current approaches have prioritized scaling up the size of LLMs, necessitating substantial computational resources and data. Our objective, however, is to pursue a different path by developing neurosymbolic language models. We propose to integrate logical and symbolic feedback during the training process, enabling significantly smaller language models to achieve far better reasoning capabilities than the LLMs currently in use.
Parameter Efficient Instruction Tuning of LLMs for Financial Applications
Authors: Subhendu Khatuya
Venue: IJCAI 2024 (main)

Abstract: XBRL tagging in financial texts involves categorizing entities into numerous labels, presenting challenges for state-of-the-art models. Financial reports like 10-Q and 10-K, which must be tagged with XBRL according to a taxonomy with thousands of labels. The FNXL dataset exemplifies this with 2,794 labels. Manual tagging is neither scalable nor cost-effective, necessitating automatic annotation methods. Additionally, summarizing long Earnings Call Transcripts (ECTs) is crucial for financial decision-making. The ECTSum dataset highlights challenges in automatic summarization, including a high compression ratio and documents exceeding typical LLM token limits. This study proposes novel methods for both XBRL tagging and ECT summarization.
Two-Sided Facility Location Games
Authors: Simon Krogmann
Venue: IJCAI 2024 (main)

Abstract: Facility location problems have been studied in settings like hospital placement or the competition between stores. In some cases, a central authority coordinates facility placements to optimize metrics like the coverage of an area or emergency response time. In many cases, however, facilities are placed by multiple rational agents to maximize their utility, e.g., the number of clients they attract. In previous research, these games feature simplistic client behavior independent of other clients' strategic choices, e.g., visiting the closest facility. Our goal is to understand what happens if clients also act selfishly, resulting in a two-stage game consisting of strategic facility and client agents. In three recent publications, we investigated such two-stage models for clients that optimize their waiting times. We showed the existence and gave algorithms for (approximate) subgame perfect equilibria, a common extension of Nash equilibria for sequential games. To learn more about this domain, we intend to investigate further natural client behaviors and eventually create a more general model or hierarchy of two-sided facility location games. With this, we aim to make predictions in real-world settings, e.g., the placement of renewable energy infrastructure.
Enhancing Policy Gradient Algorithms with Search in Imperfect Information Games
Authors: Ondřej Kubíček
Venue: IJCAI 2024 (main)

Abstract: Sequential decision-making under uncertainty in multi-agent environments is a fundamental problem in artificial intelligence. Games serve as a base model for these problems. Finding optimal plans in games that model real-world scenarios necessitates scalable algorithms. In games with perfect information, algorithms that use a combination of search and deep reinforcement learning can scale to arbitrary-sized games and achieve superhuman performance. In games with imperfect information, the situation is more challenging due to the nature of the search. This work aims to develop algorithms that use search but can scale into larger games than currently possible.
Fair and Efficient Chore Allocation: Existence and Computation
Authors: Aniket Murhekar
Venue: IJCAI 2024 (main)

Abstract: We investigate the existence and computation of fair and efficient allocations of indivisible chores to agents with additive preferences. We consider the popular envy-based fairness notions of envy-freeness up to one chore (EF1) and the efficiency notion of Pareto-optimality (PO). The existence of an allocation of chores that is simultaneously EF1 and PO is regarded a major open problem in discrete fair division. We show that an EF1 and PO allocation can be computed in polynomial time for certain structured instances. These results comprise the first non-trivial positive results for the problem and reveal insights towards settling the problem in its full generality.
Multivariate Analysis and Structural Restrictions in Computational Social Choice
Authors: Šimon Schierreich
Venue: IJCAI 2024 (main)

Abstract: In my research, I focus on computationally hard problems in the area of computational social choice. I am interested in the study of input restrictions that guarantee the existence of efficient and scalable algorithms that can be of practical interest.
Optimization Under Epistemic Uncertainty Using Prediction
Authors: Noah Schutte
Venue: IJCAI 2024 (main)

Abstract: Due to the complexity of randomness, optimization problems are often modeled to be deterministic to be solvable. Specifically epistemic uncertainty, i.e., uncertainty that is caused due to a lack of knowledge, is not easy to model, let alone easy to subsequently solve. Despite this, taking uncertainty into account is often required for optimization models to produce robust decisions that perform well in practice. We analyze effective existing frameworks, aiming to improve robustness without increasing complexity. Specifically we focus on robustness in decision-focused learning, which is a framework aimed at making context-based predictions for an optimization problem's uncertain parameters that minimize decision error.
Cooperation and Fairness in Systems of Indirect Reciprocity
Authors: Martin Smit
Venue: IJCAI 2024 (main)

Abstract: Across disciplines, cooperation is a fundamental research topic. While socially desirable to a population, it often bears a cost to the individual who, in their own self-interest, rationally chooses not to engage in costly cooperation. As such, much work has been done in understanding the biological mechanisms behind cooperation in human and animal populations. In my PhD project, I develop and apply these mechanisms both to artificial multi-agent systems and real social systems. I examine how factors such as agent heterogeneity and different learning algorithms affect not only the level of cooperation within a system, but also the level of fairness in the distribution of payoffs. In previous work, I showed how the effectiveness of the social norm-based mechanism of indirect reciprocity is affected when in-group biased cooperation is present. Beyond my future work on online platforms, I also plan to explore the effects of space, gossip, and partial and subjective observations to widen the potential scope of applications.
Deep Learning with Requirements in the Real World
Authors: Mihaela C. Stoian
Venue: IJCAI 2024 (main)

Abstract: Deep learning models have repeatedly shown their strengths in various application domains. However, their predictions often struggle to meet background knowledge requirements, which is a crucial condition for safety-critical systems. My research focuses on integrating requirements into neural networks to guide the learning process and ultimately produce outputs that ensure the requirements' satisfaction. Here, I will discuss my proposed methods in the context of two real-world applications: tabular data generation and autonomous driving.
Implicit Anomaly Subgraph Detection (IASD) in Multi-Domain Attribute Networks
Authors: Ying Sun
Venue: IJCAI 2024 (main)

Abstract: Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often deeply hidden within large datasets, and 2) Anomaly detection approaches are required to unveil the mechanisms behind anomaly generation. Our study focuses on detecting hidden anomaly subgraphs within big data and offering improved explanations for the root cause of anomalies by integrating multi-domain datasets.
Bio-inspired Dynamic and Decentralized Online Learning in Uninformed Heterogeneous Multi-Agent Environments
Authors: Angel Sylvester
Venue: IJCAI 2024 (main)

Abstract: Dynamic adaptation and learning, akin to natural organisms, is crucial for robots operating in real-world scenarios like search and rescue missions. We propose a solution combining intuition from embodied evolution and Bayes theory to promote flexible exploration in foraging tasks. Our investigation focuses on three main areas: 1) leveraging communication and prior knowledge to develop adaptable strategies in agent groups, 2) addressing challenges from sparse rewards or limited data availability, and 3) developing methods for concurrent evaluation and training in a single iteration, filling a current gap in learning-based solutions. Future directions include exploring decentralized coordination among agents and incorporating assistance based on prospective memory and altruism in multi-agent reinforcement learning.
Stakeholder-oriented Decision Support for Auction-based Federated Learning
Authors: Xiaoli Tang
Venue: IJCAI 2024 (main)

Abstract: Auction-based federated learning (AFL) is an important area of FL incentive mechanism design. It effectively incentivizes high-quality data owners (DOs) to participate in data consumers' (DCs, i.e., servers') FL training tasks. However, AFL is still evolving, with existing methods primarily addressing optimal DC-DO matching or DC selection problems in monopoly markets. To enhance the practicality of AFL, we introduce stakeholder-oriented decision support in AFL. This facilitates optimal and strategic decision-making for all stakeholders, improving the efficiency and sustainability of the AFL ecosystem.
Fairness and Optimization in Dynamic Multiagent Allocation Problems
Authors: Yohai Trabelsi
Venue: IJCAI 2024 (main)

Abstract: In many allocation problems, understanding individual agents' needs, wants, and tradeoffs is crucial for providing fair and efficient solutions. This paper begins with motivating applications and critical definitions. We review existing results, such as advising agents on relaxing restrictions for improved resource allocation, optimizing task allocation in online settings without rejection of a task, and more. We conclude by outlining three potential directions for future research.
N-Agent Ad Hoc Teamwork
Authors: Caroline Wang
Venue: IJCAI 2024 (main)

Abstract: Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In fully cooperative multi-agent reinforcement learning, the learning algorithm controls all agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a single agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars to cooperate with each other, yet once on the road, these cars must additionally cooperate with cars from other companies. Towards expanding the class of scenarios that cooperative learning methods may optimally address, this research agenda introduces and proposes to study N-agent ad hoc teamwork (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates.
Culturally-aware Image Captioning
Authors: Youngsik Yun
Venue: IJCAI 2024 (main)

Abstract: The primary research challenge lies in mitigating and measuring geographical and demographic biases in generative models, which is crucial for ensuring fairness in AI applications. Existing models trained on web-crawled datasets like LAION-400M often perpetuate harmful stereotypes and biases, especially concerning minority groups or less-represented regions. To address this, I proposed a framework called CIC (Culturally-aware Image Caption) to generate culturally-aware image captions. This framework leverages visual question answering (VQA) to extract cultural visual elements from images. It prompts both caption prompts and cultural visual elements to generate culturally-aware captions using large language models (LLMs). Human evaluations confirm the effectiveness of our approach in depicting cultural information accurately. Two key future directions are outlined. First, current image caption evaluation methods are inadequate for assessing culturally-aware captions, necessitating the development of new evaluation metrics leveraging cultural datasets and representations. Second, ethical considerations, particularly concerning stereotypes embedded in existing models, demand consensus and standards development through diverse cultural perspectives. Addressing these challenges is vital for the responsible deployment of AI technologies in diverse real-world contexts.
Human-Robot Alignment through Interactivity and Interpretability: Don't Assume a ``Spherical Human''
Authors: Matthew Gombolay
Venue: IJCAI 2024 (main)

Abstract: Interactive and interpretable robot learning can help to democratize robots, placing the power of assistive robotic systems in the hands of end-users. While machine learning-based approaches to robotics have achieved impressive results, robot learning is still a feat of costly engineering performed in controlled settings and relying upon impractical assumptions about humans. To achieve a vision in which robots can be integrated sustainably into our daily lives for robotic assistance, researchers must take a human-centered approach and develop novel approaches for human-robot alignment of robot values and behaviors. This paper amalgamates recent human factors insights and computational techniques that can support human-robot alignment through interactive and interpretable robot learning and teaming.
Human-AI Interaction Generation: A Connective Lens for Generative AI and Procedural Content Generation
Authors: Matthew Guzdial
Venue: IJCAI 2024 (main)

Abstract: Generative AI has recently gained popularity as a paradigm for content generation. In this paper, we link this paradigm to an older one: Procedural Content Generation (PCG). We propose a lens to identify the commonalities between both paradigms that we call human-AI interactive generation. Using this lens, we identify three beneficial attributes then survey recent related work and summarize relevant findings.
Trustworthy Machine Learning under Imperfect Data
Authors: Bo Han
Venue: IJCAI 2024 (main)

Abstract: Trustworthy machine learning (TML) under imperfect data has recently brought much attention in the data-centric fields of machine learning (ML) and artificial intelligence (AI). Specifically, there are mainly three types of imperfect data along with their challenges for ML, including i) label-level imperfection: noisy labels; ii) feature-level imperfection: adversarial examples; iii) distribution-level imperfection: out-of-distribution data. Therefore, in this paper, we systematically share our insights and solutions of TML to handle three types of imperfect data. More importantly, we discuss some new challenges in TML, which also open more opportunities for future studies, such as trustworthy foundation models, trustworthy federated learning, and trustworthy causal learning.
Algorithmic Fairness in Distribution of Resources and Tasks
Authors: Hadi Hosseini
Venue: IJCAI 2024 (main)

Abstract: The widespread adoption of Artificial Intelligence (AI) systems has profoundly reshaped decision-making in social, political, and commercial contexts. This paper explores the critical issue of fairness in AI-driven decision-making, particularly in allocating resources and tasks. By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences.
The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence
Authors: Guodong Long
Venue: IJCAI 2024 (main)

Abstract: The success of foundation models advances the development of various intelligent and personalized agents to handle intricate tasks in their daily lives, however finite resources and privacy concerns from end users limit the potential of customizing the large intelligent agents for personal use. This paper explores the preliminary design of federated intelligence that paves the way toward personalized intelligent agents in large-scale collaboration scenarios. In Federated Intelligence, agents can collaboratively augment their intelligence quotient (IQ) by learning complementary knowledge and fine-grained adaptations. These personalized intelligent agents can also co-work together to jointly address complex tasks in the form of collective intelligence. The paper will highlight federated intelligence as a new pathway for tackling complex intelligent tasks by refining and extending centralized foundation models to an open and collaborative paradigm.
Towards a Theory of Machine Learning on Graphs and its Applications in Combinatorial Optimization
Authors: Christopher Morris
Venue: IJCAI 2024 (main)

Abstract: Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across many disciplines, from life and physical to social and engineering sciences. Despite their practical success, our theoretical understanding of the properties of GNNs remains incomplete. Here, we survey the author's and his collaborators' progress in developing a deeper theoretical understanding of GNNs' expressive power and generalization abilities. In addition, we overview recent progress in using GNNs to speed up solvers for hard combinatorial optimization tasks.
Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values
Authors: Julia Neidhardt
Venue: IJCAI 2024 (main)

Abstract: Recent advancements in recommender systems highlight the importance of metrics beyond accuracy, including diversity, serendipity, and fairness. This paper discusses various aspects of modern recommender systems, focusing on challenges such as preference elicitation, the complexity of human decision-making, and multi-domain applicability. The integration of Generative AI and Large Language Models offers enhanced personalization capabilities but also raises concerns regarding transparency and fairness. This work examines ongoing research efforts aimed at developing transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and responsibility, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights.
A Little of That Human Touch: Achieving Human-Centric Explainable AI via Argumentation
Authors: Antonio Rago
Venue: IJCAI 2024 (main)

Abstract: As data-driven AI models achieve unprecedented feats across previously unthinkable tasks, the diminishing levels of interpretability of their increasingly complex architectures can often be sidelined in place of performance. If we are to comprehend and trust these AI models as they advance, it is clear that symbolic methods, given their unparalleled strengths in knowledge representation and reasoning, can play an important role in explaining AI models. In this paper, I discuss some of the ways in which one branch of such methods, computational argumentation, given its human-like nature, can be used to tackle this problem. I first outline a general paradigm for this area of explainable AI, before detailing a prominent methodology therein which we have pioneered. I then illustrate how this approach has been put into practice with diverse AI models and types of explanations, before looking ahead to challenges, future work and the outlook in this field.