GenAI4ELab/papercli-papers huggingface dataset:
The Role of Over-Parameterization in Machine Learning – the Good, the Bad, the Ugly
Authors: Fanghui Liu
Venue: AAAI 2024 (main)

Abstract: The conventional wisdom of simple models in machine learning misses the bigger picture, especially over-parameterized neural networks (NNs), where the number of parameters are much larger than the number of training data. Our goal is to explore the mystery behind over-parameterized models from a theoretical side.

In this talk, I will discuss the role of over-parameterization in neural networks, to theoretically understand why they can perform well. First, I will discuss the role of over-parameterization in neural networks from the perspective of models, to theoretically understand why they can genralize well. Second, the effects of over-parameterization in robustness, privacy are discussed. Third, I will talk about the over-parameterization from kernel methods to neural networks in a function space theory view.  Besides, from classical statistical learning to sequential decision making, I will talk about the benefits of over-parameterization on how deep reinforcement learning works well for function approximation. Potential future directions on theory of over-parameterization ML will also be discussed.
Algorithmic Foundation of Federated Learning with Sequential Data
Authors: Mingrui Liu
Venue: AAAI 2024 (main)

Abstract: The current analysis of federated optimization algorithms for training deep neural networks assumes that the data is non-sequential (e.g., images), which incurs a smooth loss objective. In contrast, edge devices generate lots of sequential data every day, where these sequences exhibit significant sequential correlation at different time stamps (e.g., text messages). In order to learn from such sequential data, people typically use a class of neural networks that is inherently nonsmooth, with a potentially unbounded smoothness parameter. Examples include recurrent neural networks, long-short-term memory networks, and transformers. It remains unclear how to design provably efficient algorithms for training these neural networks to learn from sequential data. My goal is to lay the algorithmic foundation of federated learning with sequential data, which contributes novel algorithms for learning from a range of real-world sequential data (e.g., natural language, electronic health record, transportation, time series, etc.) using state-of-the-art deep neural networks.


In this talk, I will first motivate the problem by showing that the transformer, which is widely used for sequential data learning, has an unbounded smooth landscape. Then, I will introduce provably efficient federated deep learning algorithms in the presence of unbounded smoothness. In particular, I will introduce a few efficient algorithms for various settings of federated learning, including homogeneous data, heterogeneous data, and partial client participation. The main result is twofold. First, we show that the designed algorithms provably small computational and communication complexities. Second, we establish fundamental hardness results in the unbounded smoothness setting. Ultimately, I will discuss the future challenges of extending our research framework from small-scale neural networks to large language models.
When Causal Inference Meets Graph Machine Learning
Authors: Jing Ma
Venue: AAAI 2024 (main)

Abstract: Graphs (i.e., networks) are ubiquitous in daily life, as they can effectively model a plethora of real-world systems with connected units, such as social networks and biological networks. Recent years have witnessed rapid development in graph-based machine learning (GML) in various high-impact domains. Currently, the mainstream GML methods are based on statistical learning, e.g., utilizing the statistical correlations between node features, graph structure, and labels for node classification. However, statistical learning has been widely criticized for only capturing the superficial relations between variables in the data system, and consequently, rendering the lack of trustworthiness in real-world applications. Therefore, it is crucial to understand the causality in the data system and the learning process. Causal inference is the discipline that investigates the causality inside a system, for example, to identify and estimate the causal effect of a certain treatment (e.g., wearing a face mask) on an important outcome (e.g., COVID-19 infection). Involving the concepts and philosophy of causal inference in ML methods is often considered significant for human-level intelligence and can serve as the foundation of artificial intelligence (AI). However, most traditional causal inference studies rely on strong assumptions, and focus on independent and identically distributed (i.i.d.) data, while causal inference on graphs is faced with many barriers. Therefore, we aim to bridge the gap between causal inference and GML.
Towards Holistic, Pragmatic and Multimodal Conversational Systems
Authors: Pranava Madhyastha
Venue: AAAI 2024 (main)

Abstract: Language acquisition and utilization transcend the mere exchange of lexical units. Visual cues, prosody, gestures, body movements, and context play an undeniably crucial role. Humans naturally communicate multimodally, employing multiple channels and synthesizing information from diverse modalities. My research delves into the characterization and construction of multimodal models that seamlessly integrate data from multiple independent modalities. I will cover recent work that highlights the challenges, achievements, and opportunities towards developing capable multimodal discursive models.
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Authors: Giuseppe Marra
Venue: AAAI 2024 (main)

Abstract: The integration of learning and reasoning is one of the key challenges in artificial intelligence and machine learning today. The area of Neuro-Symbolic AI (NeSy) tackles this challenge by integrating symbolic reasoning with neural networks. In our recent work, we provided an introduction to NeSy by drawing several parallels to another field that has a rich tradition in integrating learning and reasoning, namely Statistical Relational Artificial Intelligence (StarAI).
Harmonious Mobility for Robots that Work with and around People
Authors: Christoforos Mavrogiannis
Venue: AAAI 2024 (main)

Abstract: The integration of advances from machine learning and computer vision with the classical autonomy stack has brought successful robot deployments in fulfilment, manufacturing, and transportation. However, unstructured and dynamic environments such as pedestrian spaces and streets, workplaces, and homes pose additional challenges such as modeling human behavior, understanding user perceptions, and ensuring human safety and comfort. My work addresses such challenges to enable robots to fluently work with and around people to increase productivity and assist users.
Exploiting Data Geometry in Machine Learning
Authors: Melanie Weber
Venue: AAAI 2024 (main)

Abstract: A key challenge in Machine Learning (ML) is the identification of geometric structure in high-dimensional data. Most algorithms assume that data lives in a high-dimensional vector space; however, many applications involve non-Euclidean data, such as graphs, strings and matrices, or data whose structure is determined by symmetries in the underlying system. Here, we discuss methods for identifying geometric structure in data and how leveraging data geometry can give rise to efficient ML algorithms with provable guarantees.
Towards Trustworthy Deep Learning
Authors: Tsui-Wei (Lily) Weng
Venue: AAAI 2024 (main)

Abstract: Deep neural networks (DNNs) have achieved unprecedented success across many scientific and engineering fields in the last decades. Despite its empirical success, unfortunately, recent studies have shown that there are various failure modes and blindspots in DNN models which may result in unexpected serious failures and potential harms, e.g. the existence of adversarial examples and small perturbations. This is not acceptable especially for safety critical and high stakes applications in the real-world, including healthcare, self-driving cars, aircraft control systems, hiring and malware detection protocols. Moreover, it has been challenging to understand why and when DNNs will fail due to their complicated structures and black-box behaviors. Lacking interpretability is one critical issue that may seriously hinder the deployment of DNNs in high-stake applications, which need interpretability to trust the prediction, to understand potential failures, and to be able to mitigate harms and eliminate biases in the model.


To make DNNs trustworthy and reliable for deployment, it is necessary and urgent to develop methods and tools that can (i) quantify and improve their robustness against adversarial and natural perturbations, and (ii) understand their underlying behaviors and further correct errors to prevent injuries and damages. These are the important first steps to enable Trustworthy AI and Trustworthy Machine Learning. In this talk, I will survey a series of research efforts in my lab contributed to tackling the grand challenges in (i) and (ii). In the first part of my talk, I will overview our research effort in Robust Machine Learning since 2017, where we have proposed the first attack-agnostic robustness evaluation metric, the first efficient robustness certification algorithms for various types of perturbations, and efficient robust learning algorithms across supervised learning to deep reinforcement learning. 


In the second part of my talk, I will survey a series of exciting results in my lab on accelerating interpretable machine learning and explainable AI. Specifically, I will show how we could bring interpretability into deep learning by leveraging recent advances in multi-modal models. I'll present recent works in our group on automatically dissecting neural networks with open vocabulary concepts, designing interpretable neural networks without concept labels, and briefly overview our recent efforts on demystifying black-box DNN training process, automated neuron explanations for Large Language Models and the first robustness evaluation of a family of neuron-level interpretation techniques.
Towards Reliable Learning in the Wild: Generalization and Adaptation
Authors: Huaxiu Yao
Venue: AAAI 2024 (main)

Abstract: The real-world deployment of machine learning algorithms often poses challenges due to shifts in data distributions and tasks. These shifts can lead to a degradation in model performance, as the model may not have encountered such changes during training. Additionally, they can make it difficult for the model to generalize to new scenarios and can result in poor performance in real-world applications. In this talk, I will present our research on building machine learning models that are highly generalizable and easily adaptable to different shifts. Specifically, I will first discuss our approach to improving out-of-distribution robustness and mitigating spurious correlations by training environment-invariant models through selective augmentation and post-hoc rectification. Second, I will present our techniques for continuous and rapid adaptation of models to new tasks and environments. This includes methods to facilitate compositional generalization and adaptation by extracting relationships from historical observations and to enhance reliable adaptation even in the face of imperfect observations. Additionally, I will showcase our successful practices for addressing shifts in real-world applications, such as in the healthcare, e-commerce, and transportation industries. The talk will also touch upon the remaining challenges and outline future research directions in this area.
Towards Human-like Learning from Relational Structured Data
Authors: Quanming Yao
Venue: AAAI 2024 (main)

Abstract: Relational structured data is a way of representing knowledge using nodes and edges, while also capturing the meaning of that knowledge in a structured form that can be used for machine learning. Compared with vision and natural language data, relational structured data represents and manipulates structured knowledge, which can be beneficial for tasks that involve reasoning or inference. On the other hand, vision and NLP deal more with unstructured data (like images and text), and they often require different types of models and algorithms to extract useful information or features from the data. Human-like Learning develops methods that can harness relational structures and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. With Human-like Learning, the learning algorithm is efficient and can adapt to new or unseen situations, which is crucial in real-world applications where environments may change unpredictably. Moreover, the models are easier for humans to understand and interpret, which is important for transparency and trust in AI systems. In this talk, we present our recent attempts towards human-like learning from relational structured data.
Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment
Authors: Wenbin Zhang
Venue: AAAI 2024 (main)

Abstract: Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistics. Most assume the availability of class label which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. To this end, this talk revisits fairness and reveals idiosyncrasies of existing fairness literature assuming the availability of class label that limits their real-world utility. The primary artifacts are formulating fairness with censorship to account for scenarios where the class label is not guaranteed, and a suite of corresponding new fairness notions, algorithms, and theoretical constructs to bridge the gap between the design of a ``fair'' model in the lab and its deployment in the real-world.
Fair and Optimal Prediction via Post-Processing
Authors: Han Zhao
Venue: AAAI 2024 (main)

Abstract: In this talk I will discuss our recent work on characterizing the inherent tradeoff between fairness and accuracy in both classification and regression problems. I will also present a post-processing algorithm that derives optimal fair predictors from Bayes score functions.
Towards Reproducible, Automated, and Scalable Anomaly Detection
Authors: Yue Zhao
Venue: AAAI 2024 (main)

Abstract: Anomaly detection (AD), often termed outlier detection, is a key machine learning (ML) task, aiming to identify uncommon yet crucial patterns in data. With the increasing complexity of the modern world, the applications of AD span wide—from NASA's spacecraft monitoring to early patient prioritization at University of Pittsburgh Medical Center. Technology giants like Google and Amazon also leverage AD for service disruption identification. Here, I will traverse my AD works with promising new directions, particularly emphasizing reproducible benchmarks (Part 1), automated algorithms (Part 2), and scalable systems (Part 3).
Combating Insider Threat in the Open-World Environments: Identification, Monitoring, and Data Augmentation
Authors: Dawei Zhou
Venue: AAAI 2024 (main)

Abstract: Recent years have witnessed a dramatic increase in a class of security threats known as "insider threats". These threats occur when individuals with authorized access to an organization's network engage in harmful activities, potentially leading to the disclosure of vital information or adversely affecting the organization's systems (e.g., financial loss, system crashes, and national security challenges).  Distinct from other types of terror attacks, combating insider threats exhibits several unique challenges, including (1) rarity, (2) non-separability, (3) label scarcity, (4) dynamics, and (5) heterogeneity, making themselves extremely difficult to identify and mitigate. We target the challenging problem of combating insider threats in open-world environments by leveraging a variety of data sources (e.g., internal system logs, employee networks, human trafficking, and smuggling networks). To effectively combat these intricate threats, we introduce an interactive learning mechanism that is composed of three mutually beneficial learning modules: insider identification, insider monitoring, and data augmentation. Each module plays a crucial role in enhancing our ability to detect and mitigate insider threats, thereby contributing to a more secure and resilient organizational environment.
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation (Abstract Reprint)
Authors: Micheal Abaho; Yousef H. Alfaifi
Venue: AAAI 2024 (main)

Abstract: Injecting textual information into knowledge graph (KG) entity representations has
been a worthwhile expedition in terms of improving performance in KG oriented tasks
within the NLP community. External knowledge often adopted to enhance KG embeddings
ranges from semantically rich lexical dependency parsed features to a set of relevant key
words to entire text descriptions supplied from an external corpus such as wikipedia and
many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made,
the proposal in this work suggests that it can be improved even further. Instead of using
a single text description (which would not sufficiently represent an entity because of the
inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a
set of text descriptions relevant to KG entities as well as align or augment KG embeddings
with text descriptions. Different from prior work that plugs formal entity descriptions
declared in knowledge bases, this framework leverages a retriever model to selectively identify
richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the
framework treats the number of descriptions to use in augmentation process as a parameter,
which allows the flexibility of enumerating across several numbers before identifying an
appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5%
percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively,
in comparison to text-enhanced knowledge graph augmentation methods using traditional
CNNs.
Program Synthesis with Best-First Bottom-Up Search (Abstract Reprint)
Authors: Saqib Ameen; Levi H. S. Lelis
Venue: AAAI 2024 (main)

Abstract: Cost-guided bottom-up search (BUS) algorithms use a cost function to guide the search to solve program synthesis tasks. In this paper, we show that current state-of-the-art cost-guided BUS algorithms suffer from a common problem: they can lose useful information given by the model and fail to perform the search in a best-first order according to a cost function. We introduce a novel best-first bottom-up search algorithm, which we call Bee Search, that does not suffer information loss and is able to perform cost-guided bottom-up synthesis in a best-first manner. Importantly, Bee Search performs best-first search with respect to the generation of programs, i.e., it does not even create in memory programs that are more expensive than the solution program. It attains best-first ordering with respect to generation by performing a search in an abstract space of program costs. We also introduce a new cost function that better uses the information provided by an existing cost model. Empirical results on string manipulation and bit-vector tasks show that Bee Search can outperform existing cost-guided BUS approaches when employing more complex domain-specific languages (DSLs); Bee Search and previous approaches perform equally well with simpler DSLs. Furthermore, our new cost function with Bee Search outperforms previous cost functions on string manipulation tasks.
Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-Based Fault Detection and Identification (Abstract Reprint)
Authors: Pasquale Antonante; Heath Nilsen; Luca Carlone
Venue: AAAI 2024 (main)

Abstract: This paper investigates runtime monitoring of perception systems. Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies requires the development of methodologies to guarantee and monitor safe operation. Despite the paramount importance of perception, currently there is no formal approach for system-level perception monitoring. In this paper, we formalize the problem of runtime fault detection and identification in perception systems and present a framework to model diagnostic information using a diagnostic graph. We then provide a set of deterministic, probabilistic, and learning-based algorithms that use diagnostic graphs to perform fault detection and identification. Moreover, we investigate fundamental limits and provide deterministic and probabilistic guarantees on the fault detection and identification results. We conclude the paper with an extensive experimental evaluation, which recreates several realistic failure modes in the LGSVL open-source autonomous driving simulator, and applies the proposed system monitors to a state-of-the-art autonomous driving software stack (Baidu's Apollo Auto). The results show that the proposed system monitors outperform baselines, have the potential of preventing accidents in realistic autonomous driving scenarios, and incur a negligible computational overhead.
A General Model for Aggregating Annotations AcrossSimple, Complex, and Multi-object Annotation Tasks (Abstract Reprint)
Authors: Alexander Braylan; Madalyn Marabella; Omar Alonso; Matthew Lease
Venue: AAAI 2024 (main)

Abstract: Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. To date, many aggregation models have been proposed for simple categorical or numerical annotation tasks, but far less work has considered more complex annotation tasks, such as those involving open-ended, multivariate, or structured responses. Similarly, while a variety of bespoke models have been proposed for specific tasks, our work is the first we are aware of to introduce aggregation methods that generalize across many, diverse complex tasks, including sequence labeling, translation, syntactic parsing, ranking, bounding boxes, and keypoints. This generality is achieved by applying readily available task-specific distance functions, then devising a task-agnostic method to model these distances between labels, rather than the labels themselves.

This article presents a unified treatment of our prior work on complex annotation modeling and extends that work with investigation of three new research questions. First, how do complex annotation task and dataset properties impact aggregation accuracy? Second, how should a task owner navigate the many modeling choices in order to maximize aggregation accuracy? Finally, what tests and diagnoses can verify that aggregation models are specified correctly for the given data? To understand how various factors impact accuracy and to inform model selection, we conduct large-scale simulation studies and broad experiments on real, complex datasets. Regarding testing, we introduce the concept of unit tests for aggregation models and present a suite of such tests to ensure that a given model is not mis-specified and exhibits expected behavior.

Beyond investigating these research questions above, we discuss the foundational concept and nature of annotation complexity, present a new aggregation model as a conceptual bridge between traditional models and our own, and contribute a new general semisupervised learning method for complex label aggregation that outperforms prior work.