GenAI4ELab/papercli-papers huggingface dataset:
Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation (Extended Abstract)
Authors: Enpeng Yuan; Wenbo Chen; Pascal Van Hentenryck
Venue: IJCAI 2023 (main)

Abstract: Idle vehicle relocation is crucial for addressing demand-supply imbalance that frequently arises in the ride-hailing system. Current mainstream methodologies - optimization and reinforcement learning - suffer from obvious computational drawbacks. Optimization models need to be solved in real-time and often trade off model fidelity (hence quality of solutions) for computational efficiency. Reinforcement learning is expensive to train and often struggles to achieve coordination among a large fleet. This paper designs a hybrid approach that leverages the strengths of the two while overcoming their drawbacks. Specifically, it trains an optimization proxy, i.e., a machine-learning model that approximates an optimization model, and refines the proxy with reinforcement learning. This Reinforcement Learning from Optimization Proxy (RLOP) approach is efficient to train and deploy, and achieves better results than RL or optimization alone. Numerical experiments on the New York City dataset show that the RLOP approach reduces both the relocation costs and computation time significantly compared to the optimization model, while pure reinforcement learning fails to converge due to computational complexity.
Unsupervised and Few-Shot Parsing from Pretrained Language Models (Extended Abstract)
Authors: Zhiyuan Zeng; Deyi Xiong
Venue: IJCAI 2023 (main)

Abstract: This paper proposes two Unsupervised constituent Parsing models (UPOA and UPIO) that calculate inside and outside association scores solely based on the self-attention weight matrix learned in a pretrained language model. The proposed unsupervised parsing models are further extended to few-shot parsing models (FPOA, FPIO) that use a few annotated trees to fine-tune the linear projection matrices in self-attention. Experiments on PTB and SPRML show that both unsupervised and few-shot parsing methods are better than or comparable to the previous methods.
Simplified Risk-aware Decision Making with Belief-dependent Rewards in Partially Observable Domains (Extended Abstract)
Authors: Andrey Zhitnikov; Vadim Indelman
Venue: IJCAI 2023 (main)

Abstract: It is a long-standing objective to ease the computation burden incurred by the decision-making problem under partial observability. Identifying the sensitivity to simplification of various components of the original problem has tremendous ramifications. Yet, algorithms for decision-making under uncertainty usually lean on approximations or heuristics without quantifying their effect. Therefore, challenging scenarios could severely impair the performance of such methods. In this paper, we extend the decision-making mechanism to the whole by removing standard approximations and considering all previously suppressed stochastic sources of variability. On top of this extension, we scrutinize the distribution of the return. We begin from a return given a single candidate policy and continue to the pair of returns given a corresponding pair of candidate policies. Furthermore, we present novel stochastic bounds on the return and novel tools, Probabilistic Loss (PLoss) and its online accessible counterpart (PbLoss), to characterize the effect of a simplification.
Artificial Intelligence, Bias, and Ethics
Authors: Aylin Caliskan
Venue: IJCAI 2023 (main)

Abstract: Although ChatGPT attempts to mitigate bias, when instructed to translate the gender-neutral Turkish sentences “O bir doktor. O bir hemşire” to English, the outcome is biased: “He is a doctor. She is a nurse.” In 2016, we have demonstrated that language representations trained via unsupervised learning automatically embed implicit biases documented in social cognition through the statistical regularities in language corpora. Evaluating embedding associations in language, vision, and multi-modal language-vision models reveals that large-scale sociocultural data is a source of implicit human biases regarding gender, race or ethnicity, skin color, ability, age, sexuality, religion, social class, and intersectional associations. The study of gender bias in language, vision, language-vision, and generative AI has highlighted the sexualization of women and girls in AI, while easily accessible generative AI models such as text-to-image generators amplify bias at scale. As AI increasingly automates tasks that determine life’s outcomes and opportunities, the ethics of AI bias has significant implications for human cognition, society, justice, and the future of AI. Thus, it is necessary to advance our understanding of the depth, prevalence, and complexities of bias in AI to mitigate it both in machines and society.
Towards Formal Verification of Neuro-symbolic Multi-agent Systems
Authors: Panagiotis Kouvaros
Venue: IJCAI 2023 (main)

Abstract: This paper outlines some of the key methods we developed towards the formal verification of multi- agent systems, covering both symbolic and connectionist systems. It discusses logic-based methods for the verification of unbounded multi-agent systems (i.e., systems composed of an arbitrary number of homogeneous agents, e.g., robot swarms), optimisation approaches for establishing the robustness of neural network models, and methods for analysing properties of neuro-symbolic multi-agent systems.
Counting and Sampling Models in First-Order Logic
Authors: Ondřej Kuželka
Venue: IJCAI 2023 (main)

Abstract: First-order model counting (FOMC) is the task of counting models of a first-order logic sentence over a given set of domain elements. Its weighted variant, WFOMC, generalizes FOMC by assigning weights to the models and has many applications in statistical relational learning. More than ten years of research by various authors has led to identification of non-trivial classes of WFOMC problems that can be solved in time polynomial in the number of domain elements. In this paper, we describe recent works on WFOMC and the related problem of weighted first-order model sampling (WFOMS). We also discuss possible applications of WFOMC and WFOMS within statistical relational learning and beyond, e.g., automated solving of problems from enumerative combinatorics and elementary probability theory. Finally, we mention research problems that still need to be tackled in order to make applications of these methods really practical more broadly.
The Importance of Human-Labeled Data in the Era of LLMs
Authors: Yang Liu
Venue: IJCAI 2023 (main)

Abstract: The advent of large language models (LLMs) has brought about a revolution in the development of tailored machine learning models and sparked debates on redefining data requirements. The automation facilitated by the training and implementation of LLMs has led to discussions and aspirations that human-level labeling interventions may no longer hold the same level of importance as in the era of supervised learning. This paper presents compelling arguments supporting the ongoing relevance of human-labeled data in the era of LLMs.
A Pathway Towards Responsible AI Generated Content
Authors: Lingjuan Lyu
Venue: IJCAI 2023 (main)

Abstract: AI Generated Content (AIGC) has received tremendous attention within the past few years, with content ranging from image, text, to audio, video, etc. Meanwhile, AIGC has become a double-edged sword and recently received much criticism regarding its responsible usage. In this article, we focus on three main concerns that may hinder the healthy development and deployment of AIGC in practice, including risks from privacy; bias, toxicity, misinformation; and intellectual property (IP). By documenting known and potential risks, as well as any possible misuse scenarios of AIGC, the aim is to sound the alarm of potential risks and misuse, help society to eliminate obstacles, and promote the more ethical and secure deployment of AIGC.
Algorithmic Motion Planning Meets Minimially-Invasive Robotic Surgery
Authors: Oren Salzman
Venue: IJCAI 2023 (main)

Abstract: Robots for minimally-invasive surgery such as steerable needles and concentric-tube robots have the potential to dramatically alter the way common medical procedures are performed. They can decrease patient-recovery time, speed healing and reduce scarring. However, manually controlling such devices is highly un-intuitive and automatic planning methods are in need. For the automation of such medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the motion-planning algorithms involved in procedure automation. In this paper, I survey recent and ongoing work where we develop efficient and effective planning capabilities for medical robots that provide provable guarantees on various planner attributes as well as introduce new and exciting research opportunities in the field.
AI Planning for Hybrid Systems
Authors: Enrico Scala
Venue: IJCAI 2023 (main)

Abstract: When planning the tasks of some physical entities that need to perform actions in the world (e.g., a Robot) it is necessary to take into account quite complex models for ensuring that the plan is actually executable. Indeed the state of these systems evolves according to potentially non-linear dynamics where interdependent discrete and continuous changes happen over the entire course of the task. Systems of this kind are typically compactly represented in planning using languages mixing propositional logic and mathematics. However, these languages are still poorly understood and exploited. What are the difficulties for planning in these settings? How can we build systems that can scale up over realistically sized problems? What are the domains which can benefit from these languages? This short paper shows the main two ingredients that are needed to build a heuristic search planner, outline the main impact that such techniques have on application, and provide some open challenges. These models and relative planners hold the promise to deliver explainable AI solutions that do not rely on large amounts of data.
Pushing the Limits of Fairness in Algorithmic Decision-Making
Authors: Nisarg Shah
Venue: IJCAI 2023 (main)

Abstract: Designing provably fair decision-making algorithms is a task of growing interest and importance. In this article, I argue that preference-based notions of fairness proposed decades ago in the economics literature and subsequently explored in-depth within computer science (specifically, within the field of computational social choice) are aptly suited for a wide range of modern decision-making systems, from conference peer review to recommender systems to participatory budgeting.
The Economics of Machine Learning
Authors: Haifeng Xu
Venue: IJCAI 2023 (main)

Abstract: This survey overviews a new research agenda on the economics of machine learning, pursued at the Strategic IntelliGence for Machine Agent (SIGMA) Lab at UChicago. This overall research agenda has two themes: machine learning for economics and, conversely, economics for machine learning (ML). The first theme focuses on designing and analyzing ML algorithms for economic problems, ranging from foundational economic models to influential real-world applications such as recommender systems and national security. The second theme employs economic principles to study machine learning itself, such as the valuation and pricing of data, information and ML models, and designing incentive mechanisms to improve large-scale ML research peer reviews. While our research focuses primarily on developing methodologies, in each theme we also highlight some real-world impacts of these works, including ongoing large-scale live experiments and potential deployments for various applications.
Large Decision Models
Authors: Weinan Zhang
Venue: IJCAI 2023 (main)

Abstract: Over recent decades, sequential decision-making tasks are mostly tackled with expert systems and reinforcement learning. However, these methods are still incapable of being generalizable enough to solve new tasks at a low cost. In this article, we discuss a novel paradigm that leverages Transformer-based sequence models to tackle decision-making tasks, named large decision models. Starting from offline reinforcement learning scenarios, early attempts demonstrate that sequential modeling methods can be applied to train an effective policy given sufficient expert trajectories. When the sequence model goes large, its generalization ability over a variety of tasks and fast adaptation to new tasks has been observed, which is highly potential to enable the agent to achieve artificial general intelligence for sequential decision-making in the near future.
Using Liquid Democracy For Attention-Aware Social Choice
Authors: Shiri Alouf-Heffetz
Venue: IJCAI 2023 (main)

Abstract: My PhD research deals with the use of Liquid Democracy (LD) for social choice scenarios, while considering the scarcity of attention as a driving factor. My aim is to better understand LD: theoretically as well as practically; in particular, by establishing containment in certain computational classes for corresponding combinatorial problems, suggesting methods to improve the use of LD, and understand its adaptation to specific scenarios. Concretely, I consider the use of LD as a solution for the problem of low voter attention in light of the high cognitive effort needed by voters to actively participate in voting processes.
Cost-effective Artificial Neural Networks
Authors: Zahra Atashgahi
Venue: IJCAI 2023 (main)

Abstract: Deep neural networks (DNNs) have gained huge attention over the last several years due to their promising results in various tasks. However, due to their large model size and over-parameterization, they are recognized as being computationally demanding. Therefore, deep learning models are not well-suited to applications with limited computational resources and battery life. Current solutions to reduce computation costs mainly focus on inference efficiency while being resource-intensive during training. This Ph.D. research aims to address these challenges by developing cost-effective neural networks that can achieve decent performance on various complex tasks using minimum computational resources during training and inference of the network.
Predictive Modelling of Human Reasoning Using AGM Belief Revision
Authors: Clayton Baker
Venue: IJCAI 2023 (main)

Abstract: While many forms of belief change exist, the relationship between belief revision and human reasoning is of primary interest in this work. The theory of belief revision extends classical two-valued logic with an approach to resolve the conflict between a set of beliefs and newly learned information. The goal of this project is to test how humans revise conflicting beliefs. Experiments are proposed in which human subjects are required to resolve conflicting beliefs via relevance and confidence. In our analysis, the human responses will be evaluated against the predictions of two perspectives of propositional belief revision: formal and psychological.
AI Techniques for Urban Traffic Control and Mobility
Authors: Saumya Bhatnagar
Venue: IJCAI 2023 (main)

Abstract: The growing population has accelerated the process of urbanisation, and is putting under stress the urban transport infrastructure. This results in increased traffic congestion, with significant health, economy, and social issues. Artificial Intelligence techniques are increasingly demonstrating their capabilities in predicting and supporting urban traffic control, by extending the abilities of traffic authorities in planning and reacting to different traffic conditions. In this context, our main research topic fits in the autonomic traffic control theme, with the aim of supporting the design of autonomous traffic control systems.
On Adaptivity and Safety in Sequential Decision Making
Authors: Sapana Chaudhary
Venue: IJCAI 2023 (main)

Abstract: Sequential decision making is an important field in machine learning, encompassing techniques such as online optimization, structured bandits, and reinforcement learning, which have numerous applications such as recommendation systems, online advertising, conversational agents, and robot learning. However, two key challenges face real-world sequential decision making: the need for adaptable models and the need for safety during both learning and execution. Adaptability refers to the ability of a model to quickly adapt to new and diverse environments, which is especially challenging in environments where feedback is sparse. To address this challenge, we propose using meta reinforcement learning with sub-optimal demonstration data. Safety is also critical in real-world sequential decision making. A model that adheres to safety requirements can avoid dangerous outcomes and ensure the safety of humans and other agents in the environment. We propose an approach based on online convex optimization that ensures safety at every time step. Addressing these challenges can lead to the development of more robust, safe, and adaptable AI systems that can perform a wide range of tasks and operate in a variety of environments.
Fairness and Stability in Complex Domains
Authors: Julian Chingoma
Venue: IJCAI 2023 (main)

Abstract: Fairness and stability are normative concepts that have been investigated for many social choice domains. Recently, increasing attention has fallen on richer, and more complex, settings and we look to develop, and study in depth, these fairness and stability notions in a variety of such complex domains.
Sources and Information Reliability Measures
Authors: Quentin Elsaesser
Venue: IJCAI 2023 (main)

Abstract: More and more information is shared on the web or on social media platforms, and the information provided can be conflicting. In this case, we need to decide which information is reliable and should be taken into account. We want to define measures of reliability for each source that provides information, but also to find the truth among the conflicting information, and propose properties of these measures.
Interpretability and Fairness in Machine Learning: A Formal Methods Approach
Authors: Bishwamittra Ghosh
Venue: IJCAI 2023 (main)

Abstract: The last decades have witnessed significant progress in machine learning with a host of applications of algorithmic decision-making in different safety-critical domains, such as medical, law, education, and transportation. In high-stake domains, machine learning predictions have far-reaching consequences on the end-users. With the aim of applying machine learning for societal goods, there have been increasing efforts to regulate machine learning by imposing interpretability, fairness, robustness, etc. in predictions. Towards responsible and trustworthy machine learning, we propose two research themes in our dissertation research: interpretability and fairness of machine learning classifiers. In particular, we design algorithms to learn interpretable rule-based classifiers, formally verify fairness, and explain the sources of unfairness. Prior approaches to these problems are often limited by scalability, accuracy, or both. To overcome these limitations, we closely integrate automated reasoning, formal methods, and statistics with fairness and interpretability to develop scalable and accurate solutions.
Human-Centred Multimodal Deep Learning Models for Chest X-Ray Diagnosis
Authors: Chihcheng Hsieh
Venue: IJCAI 2023 (main)

Abstract: My thesis consists of investigating how chest X-ray images, radiologists' eye movements and patients' clinical data can be used to teach a machine how radiologists read and classify images with the goal of creating human-centric AI architectures that can (1) capture radiologists' search behavioural patterns using their eye-movements in order to improve classification in DL systems, and (2) automatically detect lesions in medical images using clinical data and eye tracking data. Heterogeneous data sources such as chest X-rays, radiologists' eye movements, and patients' clinical data can contribute to novel multimodal DL architectures that, instead of learning directly from images' pixels, will learn human classification patterns encoded in both the eye movements of the images' regions and patients' medical history. In addition to a quantitative evaluation, I plan to conduct questionnaires with expert radiologists to understand the effectiveness of the proposed multimodal DL architecture.
Exploring Multilingual Intent Dynamics and Applications
Authors: Ankan Mullick
Venue: IJCAI 2023 (main)

Abstract: Multilingual Intent Detection and explore its different characteristics are major field of study for last few years. But, detection of intention dynamics from text or voice, especially in the Indian multilingual contexts, is a challenging task. So, my first research question is on intent detection and then I work on the application in Indian Multilingual Healthcare scenario. Speech dialogue systems are designed by a pre-defined set of intents to perform user specified tasks. Newer intentions may surface over time that call for retraining. However, the newer intents may not be explicitly announced and need to be inferred dynamically. Hence, here are two crucial jobs: (a) recognizing newly emergent intents; and (b) annotating the data of the new intents in order to effectively retrain the underlying classifier. The tasks become specially challenging when a large number of new intents emerge simultaneously and there is a limited budget of manual annotation. We develop MNID (Multiple Novel Intent Detection), a cluster based framework that can identify multiple novel intents while optimized human annotation cost. Empirical findings on numerous benchmark datasets (of varying sizes) show that MNID surpasses the baseline approaches in terms of accuracy and F1-score by wisely allocating the budget for annotation. We apply intent detection approach on different domains in Indian multilingual scenarios - healthcare, finance etc. The creation of advanced NLU healthcare systems is threatened by the lack of data and technology constraints for resource-poor languages in developing nations like India. We evaluate the current state of several cutting-edge language models used in the healthcare with the goal of detecting query intents and corresponding entities. We conduct comprehensive trials on a number of models different realistic contexts, and we investigate the practical relevance depending on budget and the availability of data on English.
Human-AI Collaboration in Recruitment and Selection
Authors: Neil Natarajan
Venue: IJCAI 2023 (main)

Abstract: My research focuses on using algorithmic systems alongside human collaborators to outperform either humans or machines individually. I specialize in human-machine collaboration in recruiting and selecting talented groups of people.
Argumentation for Interactive Causal Discovery
Authors: Fabrizio Russo
Venue: IJCAI 2023 (main)

Abstract: Causal reasoning reflects how humans perceive events in the world and establish relationships among them, identifying some as causes and others as effects. Causal discovery is about agreeing on these relationships and drawing them as a causal graph. Argumentation is the way humans reason systematically about an idea: the medium we use to exchange opinions, to get to know and trust each other and possibly agree on controversial matters. Developing AI which can argue with humans about causality would allow us to understand and validate the analysis of the AI and would allow the AI to bring evidence for or against humans' prior knowledge. This is the goal of this project: to develop a novel scientific paradigm of interactive causal discovery and train AI to recognise causes and effects by debating, with humans, the results of different statistical methods