GenAI4ELab/papercli-papers huggingface dataset:
Regeneration Learning: A Learning Paradigm for Data Generation
Authors: Xu Tan; Tao Qin; Jiang Bian; Tie-Yan Liu; Yoshua Bengio
Venue: AAAI 2024 (main)

Abstract: 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.
The Fairness Fair: Bringing Human Perception into Collective Decision-Making
Authors: Hadi Hosseini
Venue: AAAI 2024 (main)

Abstract: 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.
Temporal Fairness in Multiwinner Voting
Authors: Edith Elkind; Svetlana Obraztsova; Nicholas Teh
Venue: AAAI 2024 (main)

Abstract: 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.
Mixed Fair Division: A Survey
Authors: Shengxin Liu; Xinhang Lu; Mashbat Suzuki; Toby Walsh
Venue: AAAI 2024 (main)

Abstract: 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.
Adventures of Trustworthy Vision-Language Models: A Survey
Authors: Mayank Vatsa; Anubhooti Jain; Richa Singh
Venue: AAAI 2024 (main)

Abstract: 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.
Interactive Theorem Provers: Applications in AI, Opportunities, and Challenges
Authors: Mohammad Abdulaziz
Venue: AAAI 2024 (main)

Abstract: 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.
Symbolic Reasoning Methods for AI Planning
Authors: Gregor Behnke
Venue: AAAI 2024 (main)

Abstract: 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.
Demystifying Algorithmic Fairness in an Uncertain World
Authors: Lu Cheng
Venue: AAAI 2024 (main)

Abstract: 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.
Data-Efficient Graph Learning
Authors: Kaize Ding
Venue: AAAI 2024 (main)

Abstract: 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.
Making Natural Language Reasoning Explainable and Faithful
Authors: Xinya Du
Venue: AAAI 2024 (main)

Abstract: 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.
Towards Robust Visual Understanding: from Recognition to Reasoning
Authors: Tejas Gokhale
Venue: AAAI 2024 (main)

Abstract: Models that learn from data are widely and rapidly being deployed today for real-world use, but they suffer from unforeseen failures due to distribution shift, adversarial attacks, noise and corruption, and data scarcity.  But many failures also occur because many modern AI tasks require reasoning beyond pattern matching -- and such reasoning abilities are difficult to formulate as data-based input-output function fitting.  The reliability problem has become increasingly important under the new paradigm of semantic ``multimodal'' learning.  My research provides avenues to develop robust and reliable computer vision systems, particularly by leveraging the interactions between vision and language. In this AAAI New Faculty highlights talk, I will cover three thematic areas of my research, ranging from robustness in computer vision, open-domain reliability in visual reasoning, and challenges and opportunities in evaluation of generative models. Readers are encouraged to refer to my website (www.tejasgokhale.com) for more details and updates from my lab's activities towards the goal of robust visual understanding.
Continual Learning in an Open and Dynamic World
Authors: Yunhui Guo
Venue: AAAI 2024 (main)

Abstract: Building autonomous agents that can process massive amounts of real-time sensor-captured data is essential for many real-world applications including autonomous vehicles, robotics and AI in medicine. As the agent often needs to explore in a dynamic environment, it is thus a desirable as well as challenging goal to enable the agent to learn over time without performance degradation. Continual learning aims to build a continual learner which can learn new concepts over the data stream while preserving previously learnt concepts. In the talk, I will survey three pieces of my recent research on continual learning (i) supervised continual learning, (ii) unsupervised continual learning, and (iii) multi-modal continual learning. In the first work, I will discuss a supervised
continual learning algorithm called MEGA which dynamically balances the old tasks and the new task. In the second work, I will discuss unsupervised continual learning algorithms which learn representation continually without access to the labels. In the third work, I will elaborate an efficient continual learning algorithm that can learn multiple modalities continually without forgetting.
Scaling Offline Evaluation of Reinforcement Learning Agents through Abstraction
Authors: Josiah P. Hanna
Venue: AAAI 2024 (main)

Abstract: A critical challenge for the widescale adoption of reinforcement learning (RL) is the need to give domain experts assurance that learned policies will improve decision-making -- and not lead to unacceptable behavior. To meet this challenge, my work aims to develop new methods for offline policy evaluation in real world RL domains. There has been much recent interest in offline evaluation and many advances. However, recent benchmarking efforts have also shown that there remains a substantial gap between current state-of-the-art methods and real world domains such as robotics. Towards scalable offline evaluation, my group is investigating the use of methods for abstraction and representation learning. In this new faculty highlight, I will present our recent results that show the promise of this direction for scaling offline evaluation in RL domains. I will then describe future directions in this line of that work which will further realize the promise of offline policy evaluation for increasing confidence in deployed RL.
Collaborative Learning across Heterogeneous Systems with Pre-Trained Models
Authors: Trong Nghia Hoang
Venue: AAAI 2024 (main)

Abstract: The increasingly decentralized and private nature of data in our digital society has  motivated the development of personalized, collaborative intelligent systems that enable knowledge aggregation across multiple data owners while accommodating for their data privacy and system constraints. However, collaborative learning has only been investigated in simple and limited settings: isolated task scenarios where learning begins from scratch and does not build on prior expertise; learned model is represented in task-specific forms which are not generalizable to unseen, emerging scenarios; and more often, a universal model representation is assumed across collaborators, ignoring their local compute constraints or input representations. This restricts its practicality in continual learning scenarios with limited task data, which demand continuous adaptation and knowledge transfer across different information silos, tasks, and learning models, as well as the utilization of prior solution expertises. To overcome these limitations, my research has been focused on developing effective and scalable resource-aware collaborative learning frameworks across heterogeneous systems.
Understanding Surprising Generalization Phenomena in Deep Learning
Authors: Wei Hu
Venue: AAAI 2024 (main)

Abstract: Deep learning has exhibited a number of surprising generalization phenomena that are not captured by classical statistical learning theory. This talk will survey some of my work on the theoretical characterizations of several such intriguing phenomena: (1) Implicit regularization: A major mystery in deep learning is that deep neural networks can often generalize well despite their excessive expressive capacity. Towards explaining this mystery, it has been suggested that commonly used gradient-based optimization algorithms enforce certain implicit regularization which effectively constrains the model capacity. (2) Benign overfitting: In certain scenarios, a model can perfectly fit noisily labeled training data, but still archives near-optimal test error at the same time, which is very different from the classical notion of overfitting. (3) Grokking: In certain scenarios, a model initially achieves perfect training accuracy but no generalization (i.e. no better than a random predictor), and upon further training, transitions to almost perfect generalization. Theoretically establishing these properties often involves making appropriate high-dimensional assumptions on the problem as well as a careful analysis of the training dynamics.
Fostering Trustworthiness in Machine Learning Algorithms
Authors: Mengdi Huai
Venue: AAAI 2024 (main)

Abstract: Recent years have seen a surge in research that develops and applies machine learning algorithms to create intelligent learning systems. However, traditional machine learning algorithms have primarily focused on optimizing accuracy and efficiency, and they often fail to consider how to foster trustworthiness in their design. As a result, machine learning models usually face a trust crisis in real-world applications. Driven by these urgent concerns about trustworthiness, in this talk, I will introduce my research efforts towards the goal of making machine learning trustworthy. Specifically, I will delve into the following key research topics: security vulnerabilities and robustness, model explanations, and privacy-preserving mechanisms.
Deep Learning on Graphs: A Data-Centric Exploration
Authors: Wei Jin
Venue: AAAI 2024 (main)

Abstract: Many learning tasks in Artificial Intelligence (AI) require dealing with graph data, ranging from biology and chemistry to finance and education. As powerful deep learning tools for graphs,  graph neural networks (GNNs) have demonstrated remarkable performance in various graph-related applications.  Despite the significant accomplishments of GNNs, recent studies have highlighted that their efficiency and effectiveness face significant challenges such as adversarial robustness and scalability, which are fundamentally linked to data. While major attention has been devoted to improving GNNs from the model perspective, the potential of directly enhancing data has often been overlooked.  It underscores a critical gap in GNN research---while model improvements are undoubtedly important, we also need to recognize and address the data-related factors contributing to the challenges. Hence, my research is to investigate solutions for these challenges from the data perspective, employing strategies such as data characterization, reduction, augmentation, transformation, and detection.
Quantifying Political Polarization through the Lens of Machine Translation and Vicarious Offense
Authors: Ashiqur R. KhudaBukhsh
Venue: AAAI 2024 (main)

Abstract: This talk surveys three related research contributions that shed light on the current US political divide: 

1. a novel machine-translation-based framework to quantify political polarization; 
2. an analysis of disparate media portrayal of US policing in major cable news outlets; and 
3. a novel perspective of vicarious offense that examines a timely and important question --  how well do Democratic-leaning users perceive what content would be deemed as offensive by their Republican-leaning counterparts or vice-versa?
Learning Representations for Robust Human-Robot Interaction
Authors: Yen-Ling Kuo
Venue: AAAI 2024 (main)

Abstract: For robots to robustly and flexibly interact with humans, they need to acquire skills to use across scenarios. One way to enable the generalization of skills is to learn representations that are useful for downstream tasks. Learning a representation for interactions requires an understanding of what (e.g., objects) as well as how (e.g., actions, controls, and manners) to interact with. However, most existing language or visual representations mainly focus on objects. To enable robust human-robot interactions, we need a representation that is not just grounded at the object level but to reason at the action level. The ability to reason about an agent’s own actions and other’s actions will be crucial for long-tail interactions. My research focuses on leveraging the compositional nature of language and reward functions to learn representations that generalize to novel scenarios. Together with the information from multiple modalities, the learned representation can reason about task progress, future behaviors, and the goals/beliefs of an agent. The above ideas have been demonstrated in my research on building robots to understand language and engage in social interactions.