Neural models, including large language models (LLMs), achieve superior performance on logical reasoning tasks such as question answering. To elicit reasoning capabilities from LLMs, recent works propose using the chain-of-thought (CoT) mechanism to generate both the reasoning chain and the answer, which enhances the model’s capabilities in conducting reasoning. However, due to LLM’s uninterpretable nature and the extreme flexibility of free-form explanations, several challenges remain: such as struggling with inaccurate reasoning, hallucinations, and not aligning with human preferences. In this talk, we will focus on (1) our design of leveraging structured information (that is grounded to the context), for the explainable complex question answering and reasoning; (2) our multi-module interpretable framework for inductive reasoning, which conducts step-wise faithful reasoning with iterative feedback.
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My research strives to develop fundamental graph-centric learning algorithms to reduce the need for human supervision in low-resource scenarios. The focus is on achieving effective and reliable data-efficient learning on graphs, which can be summarized into three facets: (1) graph weakly-supervised learning; (2) graph few-shot learning; and (3) graph self-supervised learning.
Significant progress in the field of fair machine learning (ML) has been made to counteract algorithmic discrimination against marginalized groups. However, fairness remains an active research area that is far from settled. One key bottleneck is the implicit assumption that environments, where ML is developed and deployed, are certain and reliable. In a world that is characterized by volatility, uncertainty, complexity, and ambiguity, whether what has been developed in algorithmic fairness can still serve its purpose is far from obvious. In this talk, I will first discuss how to improve algorithmic fairness under two kinds of predictive uncertainties, i.e., aleatoric uncertainty (i.e., randomness and ambiguity in the data) and epistemic uncertainty (i.e., a lack of data or knowledge), respectively. The former regards historical bias reflected in the data and the latter corresponds to the bias perpetuated or amplified during model training due to lack of data or knowledge. In particular, the first work studies pushing the fairness-utility trade-off through aleatoric uncertainty, and the second work investigates fair few-shot learning. The last work introduces coverage-based fairness that ensures different groups enjoy identical treatment and receive equal coverage.
Planning is the act of deliberative thinking before acting. It is based on a symbolic model of the world and the options to act in it, usually defined in function-free first-order logic. The task is to find a sequence of actions (a plan) that leads from a given current state to a desired goal state. The basic, purely physical description may be augmented with a partially ordered grammar-like structure (a Hierarchical Task Network or HTN), which can describe expert knowledge, or practical, legal, or operational requirements. In this talk, I will survey a variety of methods for automatically deriving plans using symbolic methods for planning -- from both my past and future research. These symbolic methods -- in some sense -- translate planning problems into other, simpler symbolic representations and reason over them to find plans. As a basis for these methods, I will firstly introduce relevant theoretical results on planning. First, I will discuss the expressive power of planning formalisms (ECAI'14, ICAPS'16) and second, the computational complexity of HTN planning and related tasks such as HTN plan verification, plan modification, and plan recognition (ICAPS'15, ICAPS'16). Based on these theoretical results, I will develop why SAT-based HTN planning is possible and how it can be implemented. To this end, I will survey several of my publications at top-tier conferences, including papers at ICAPS'17, AAAI'18, AAAI'19, IJCAI'19, AAAI'20, and ICAPS'21 -- in which I developed an highly SAT-based planner for HTN problems including the ability to find optimal plans as well as the grounding as a preprocessing step. Here I will also give an outlook on future developments and new ideas that I propose for SAT-based planning -- including the exploitation of structures in plan (e.g.\ landmarks or operator-counting constraints). Next, I will present the idea of expressing lifted classical planning as SAT (ICAPS'22). The resulting planner LiSAT was the first lifted SAT-based planner -- and proved highly efficient and outperformed all other lifted planners at the time of publication. Notably, LiSAT was the first planner (lifted or grounded) and still is the only one to solve the challenging OrganicSynthesis benchmark -- and could even prove optimality for all plans. I will also outline future ideas to further improve the efficiency of LiSAT. Lastly, I introduce the notion of planning with symbolic symbolic representations (AAAI'21 and ICAPS'23). Here one uses Binary Decision Diagrams to encode large sets of states efficiently. For expressing the additional structure encoded by HTNs, I show how BDDs can be suitably integrated into finite automata. Based on this representation, an efficient and optimal planning algorithm can be derived. Additionally, I show how this algorithm can be extended to also cover oversubscription planning.
Interactive theorem provers (ITPs) are computer programs in which axioms and a conjecture are stated in a formal language, and a user provides the ITP with relatively high-level steps of a formal proof for the conjecture. Then, by invoking automated theorem provers, the ITP tries to generate low-level steps that fill the gaps between the steps provided by the user, thus forming a complete formal proof of the conjecture. The ITP also checks the entire formal proof against the axioms, thus confirming the soundness of all derivations in the formal proof. In this talk, I will discuss the existing opportunities and potential benefits to applying ITPs to reason about and verify AI concepts, algorithms, and software. I will also discuss the challenges we have to being able to apply ITPs in AI and reap those benefits. I will do so by discussing a number of my previous projects on the application of ITPs to different AI concepts, algorithms, and software systems. These projects span different areas of planning (classical planning, temporal planning, and planning under uncertainty) as well as algorithms with applications in algorithmic game theory, like general graph matching and online matching.
Recently, transformers have become incredibly popular in computer vision and vision-language tasks. This notable rise in their usage can be primarily attributed to the capabilities offered by attention mechanisms and the outstanding ability of transformers to adapt and apply themselves to a variety of tasks and domains. Their versatility and state-of-the-art performance have established them as indispensable tools for a wide array of applications. However, in the constantly changing landscape of machine learning, the assurance of the trustworthiness of transformers holds utmost importance. This paper conducts a thorough examination of vision-language transformers, employing three fundamental principles of responsible AI: Bias, Robustness, and Interpretability. The primary objective of this paper is to delve into the intricacies and complexities associated with the practical use of transformers, with the overarching goal of advancing our comprehension of how to enhance their reliability and accountability.
The fair allocation of resources to agents is a fundamental problem in society and has received significant attention and rapid developments from the game theory and artificial intelligence communities in recent years. The majority of the fair division literature can be divided along at least two orthogonal directions: goods versus chores, and divisible versus indivisible resources. In this survey, besides describing the state of the art, we outline a number of interesting open questions in three mixed fair division settings: (i) indivisible goods and chores, (ii) divisible and indivisible goods (i.e., mixed goods), and (iii) fair division of indivisible goods with subsidy.
Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms. There is a large body of work dealing with axiomatic characterizations, computational complexity, and algorithmic analysis of multiwinner voting rules. Although many challenges remain, significant progress has been made in showing existence of fair and representative outcomes as well as efficient algorithmic solutions for many commonly studied settings. However, much of this work focuses on single-shot elections, even though in numerous real-world settings elections are held periodically and repeatedly. Hence, it is imperative to extend the study of multiwinner voting to temporal settings. Recently, there have been several efforts to address this challenge. However, these works are difficult to compare, as they model multi-period voting in very different ways. We propose a unified framework for studying temporal fairness in this domain, drawing connections with various existing bodies of work, and consolidating them within a general framework. We also identify gaps in existing literature, outline multiple opportunities for future work, and put forward a vision for the future of multiwinner voting in temporal settings.
Fairness is one of the most desirable societal principles in collective decision-making. It has been extensively studied in the past decades for its axiomatic properties and has received substantial attention from the multiagent systems community in recent years for its theoretical and computational aspects in algorithmic decision-making. However, these studies are often not sufficiently rich to capture the intricacies of human perception of fairness in the ambivalent nature of the real-world problems. We argue that not only fair solutions should be deemed desirable by social planners (designers), but they should be governed by human and societal cognition, consider perceived outcomes based on human judgement, and be verifiable. We discuss how achieving this goal requires a broad transdisciplinary approach ranging from computing and AI to behavioral economics and human-AI interaction. In doing so, we identify shortcomings and long-term challenges of the current literature of fair division, describe recent efforts in addressing them, and more importantly, highlight a series of open research directions.
Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, video) is usually high-dimensional and complex, and contains information that does not exist in source data, which hinders effective and efficient learning on the source-target mapping. In this paper, we present a learning paradigm called regeneration learning for data generation, which first generates Y' (an abstraction/representation of Y) from X and then generates Y from Y'. During training, Y' is obtained from Y through either handcrafted rules or self-supervised learning and is used to learn X-->Y' and Y'-->Y. Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be learned in a self-supervised way (e.g., pre-training); 3) both the mappings from X to Y' in regeneration learning and from X' to Y in representation learning are simpler than the direct mapping from X to Y. We show that regeneration learning can be a widely-used paradigm for data generation (e.g., text generation, speech recognition, speech synthesis, music composition, image generation, and video generation) and can provide valuable insights into developing data generation methods.
In the last few years, a lot of the activity of the computational social choice community has focused on novel mechanisms for reaching decisions by large groups of people. While this research makes meaningful scientific contributions, many of these mechanisms are not quite useful in realistic decision-making settings. Moreover, their radicalism ignores the centuries-old experience we have with large-scale human decision-making, and what it teaches us about what works. We believe it is important the community engage with mechanisms which are widely-used in the real world, as they may hold a key to a deeper understanding of how people reach decisions and the way that helps them do that productively. Moreover, letting the community bring its analysis and understanding to these will allow for algorithmic suggestions that have some chance of being implemented (and, thus, can contribute to the public debate on these topics). In particular, we highlight the relatively less-investigated role of parties and grouping of voters and candidates, and the role of executive capacity in analyzing decision-making structures.
This paper poses the challenge of developing and evaluating integrated systems for computational scientific discovery. We note some distinguishing characteristics of discovery tasks, examine eight component abilities, review previous successes at partial integration, and consider hurdles the AI research community must leap to transform the vision for integrated discovery into reality. In closing, we discuss promising scientific domains in which to test such computational artifacts.
Many problems, from Sudoku to factory scheduling, can be regarded as constraint satisfaction problems. A key component of real world problem solving is a conversation between a constraint programming expert and a problem domain expert to specify the problem to be solved. This presentation argues that the time is ripe for progress in automating the constraint programmer side of this conversation and suggests promising avenues for this pursuit.
In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks. However, deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. This paper provides an overview of model reprogramming to bridge this gap. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing and reusing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning, where the source and target domains can be vastly different. In many applications, model reprogramming outperforms transfer learning and training from scratch. This paper elucidates the methodology of model reprogramming, summarizes existing use cases, provides a theoretical explanation of the success of model reprogramming, and concludes with a discussion on open-ended research questions and opportunities.
Modern recommender systems lie at the heart of complex recommender ecosystems that couple the behavior of users, content providers, vendors, advertisers, and other actors. Despite this, the focus of much recommender systems research and deployment is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommender systems generate for their users. We argue that modeling the incentives and behaviors of these actors, and the interactions among them induced by the recommender systems, is needed to maximize value and improve overall ecosystem health. Moreover, we propose the use of economic mechanism design, an area largely overlooked in recommender systems research, as a framework for developing such models. That said, one cannot apply “vanilla” mechanism design to recommender ecosystem modeling optimization out of the box—the use of mechanism design raises a number of subtle and interesting research challenges. We outline a number of these in this talk (and paper), emphasizing the need to develop nonstandard approaches to mechanism design that intersect with numerous areas of research, including preference modeling, reinforcement learning and exploration, behavioral economics, and generative AI, among others.
Next Point-of-Interest (POI) recommendation has been proven effective at utilizing sparse, intricate spatial-temporal trajectory data to recommend subsequent POIs to users. While existing methods commonly alleviate the problem of data sparsity by integrating spatial-temporal context information, POI category features, and social relationships, they largely overlook the fact that the trajectory sequences collected in the datasets are often incomplete. This oversight limits the model’s potential to fully leverage historical context. In light of this background, we propose Trajectory Data Augmentation with Uncertainty (TAU) for Next POI Recommendation. TAU is a general graph-based trajectory data augmentation method designed to complete user mobility patterns by marrying uncertainty estimation into the next POI recommendation task. More precisely, TAU taps into the global transition pattern graph to identify sets of intermediate nodes located between every pair of locations, effectively leveraging edge weights as transition probabilities. During trajectory sequence construction, TAU selectively prompts intermediate nodes, chosen based on their likelihood of occurrence as pseudo-labels, to establish comprehensive trajectory sequences. Furthermore, to gauge the certainty and impact of pseudo-labels on the target location, we introduce a novel confidence-aware calibration strategy using evidence deep learning (EDL) for improved performance and reliability. The experimental results clearly indicate that our TAU method achieves consistent performance improvements over existing techniques across two real-world datasets, verifying its effectiveness as the state-of-the-art approach to the task.
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System
PDF ↗The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation
PDF ↗Online dating platforms have gained widespread popularity as a means for individuals to seek potential romantic relationships. While recommender systems have been designed to improve the user experience in dating platforms by providing personalized recommendations, increasing concerns about fairness have encouraged the development of fairness-aware recommender systems from various perspectives (e.g., gender and race). However, sexual orientation, which plays a significant role in finding a satisfying relationship, is under-investigated. To fill this crucial gap, we propose a novel metric, Opposite Gender Interaction Ratio (OGIR), as a way to investigate potential unfairness for users with varying preferences towards the opposite gender. We empirically analyze a real online dating dataset and observe existing recommender algorithms could suffer from group unfairness according to OGIR. We further investigate the potential causes for such gaps in recommendation quality, which lead to the challenges of group quantity imbalance and group calibration imbalance. Ultimately, we propose a fair recommender system based on re-weighting and re-ranking strategies to respectively mitigate these associated imbalance challenges. Experimental results demonstrate both strategies improve fairness while their combination achieves the best performance towards maintaining model utility while improving fairness.
Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization
PDF ↗Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snapshot observation is especially relevant due to its prevalence. However, the inherent complexities of this problem, such as limited information, interactions among sources, and dependence on diffusion models, pose challenges to resolution. Current methods typically utilize heuristics and greedy selection, and they are usually bonded with one diffusion model. Consequently, their effectiveness is constrained. To address these limitations, we propose a simulation-based method termed BOSouL. Bayesian optimization (BO) is adopted to approximate the results for its sample efficiency. A surrogate function models uncertainty from the limited information. It takes sets of nodes as the input instead of individual nodes. BOSouL can incorporate any diffusion model in the data acquisition process through simulations. Empirical studies demonstrate that its performance is robust across graph structures and diffusion models. The code is available at https://github.com/XGraph-Team/BOSouL.
Artificial light plays an integral role in modern cities, significantly enhancing human productivity and the efficiency of civilization. However, excessive illumination can lead to light pollution, posing non-negligible threats to economic burdens, ecosystems, and human health. Despite its critical importance, the exploration of its causes remains relatively limited within the field of artificial intelligence, leaving an incomplete understanding of the factors contributing to light pollution and sustainable illumination planning distant. To address this gap, we introduce a novel framework named Causally Aware Generative Adversarial Networks (CAGAN). This innovative approach aims to uncover the fundamental drivers of light pollution within cities and offer intelligent solutions for optimal illumination resource allocation in the context of sustainable urban development. We commence by examining light pollution across 33,593 residential areas in seven global metropolises. Our findings reveal substantial influences on light pollution levels from various building types, notably grasslands, commercial centers and residential buildings as significant contributors. These discovered causal relationships are seamlessly integrated into the generative modeling framework, guiding the process of generating light pollution maps for diverse residential areas. Extensive experiments showcase CAGAN’s potential to inform and guide the implementation of effective strategies to mitigate light pollution. Our code and data are publicly available at https://github.com/zhangyuuao/Light_Pollution_CAGAN.