bluuebunny/crossref_metadata_2025 huggingface dataset:
PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning
Authors: Wang Huandong , Gao Changzheng , Wu Yuchen , Jin Depeng , Yao Lina , Li Yong
Published: June 2023

Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. However, existing mobility trajectory generation methods still require real-world human trajectories centrally collected as the training data, where there exists an inescapable risk of privacy leakage. To overcome this limitation, in this paper, we propose PateGail, a privacy-preserving imitation learning model to generate mobility trajectories, which utilizes the powerful generative adversary imitation learning model to simulate the decision-making process of humans. Further, in order to protect user privacy, we train this model collectively based on decentralized mobility data stored in user devices, where personal discriminators are trained locally to distinguish and reward the real and generated human trajectories. In the training process, only the generated trajectories and their rewards obtained based on personal discriminators are shared between the server and devices, whose privacy is further preserved by our proposed perturbation mechanisms with theoretical proof to satisfy differential privacy. Further, to better model the human decision-making process, we propose a novel aggregation mechanism of the rewards obtained from personal discriminators. We theoretically prove that under the reward obtained based on the aggregation mechanism, our proposed model maximizes the lower bound of the discounted total rewards of users. Extensive experiments show that the trajectories generated by our model are able to resemble real-world trajectories in terms of five key statistical metrics, outperforming state-of-the-art algorithms by over 48.03%. Furthermore, we demonstrate that the synthetic trajectories are able to efficiently support practical applications, including mobility prediction and location recommendation.
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
Authors: Xiang Sheng , Zhu Mingzhi , Cheng Dawei , Li Enxia , Zhao Ruihui , Ouyang Yi , Chen Ling , Zheng Yefeng
Published: June 2023

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.
A Continual Pre-training Approach to Tele-Triaging Pregnant Women in Kenya
Authors: Zhang Wenbo , Guo Hangzhi , Ranganathan Prerna , Patel Jay , Rajasekharan Sathyanath , Danayak Nidhi , Gupta Manan , Yadav Amulya
Published: June 2023

Access to high-quality maternal health care services is limited in Kenya, which resulted in ∼36,000 maternal and neonatal deaths in 2018. To tackle this challenge, Jacaranda Health (a non-profit organization working on maternal health in Kenya) developed PROMPTS, an SMS based tele-triage system for pregnant and puerperal women, which has more than 350,000 active users in Kenya. PROMPTS empowers pregnant women living far away from doctors and hospitals to send SMS messages to get quick answers (through human helpdesk agents) to questions about their medical symptoms and pregnancy status. Unfortunately, ∼1.1 million SMS messages are received by PROMPTS every month, which makes it challenging for helpdesk agents to ensure that these messages can be interpreted correctly and evaluated by their level of emergency to ensure timely responses and/or treatments for women in need. This paper reports on a collaborative effort with Jacaranda Health to develop a state-of-the-art natural language processing (NLP) framework, TRIM-AI (TRIage for Mothers using AI), which can automatically predict the emergency level (or severity of medical condition) of a pregnant mother based on the content of their SMS messages. TRIM-AI leverages recent advances in multi-lingual pre-training and continual pre-training to tackle code-mixed SMS messages (between English and Swahili), and achieves a weighted F1 score of 0.774 on real-world datasets. TRIM-AI has been successfully deployed in the field since June 2022, and is being used by Jacaranda Health to prioritize the provision of services and care to pregnant women with the most critical medical conditions. Our preliminary A/B tests in the field show that TRIM-AI is ∼17% more accurate at predicting high-risk medical conditions from SMS messages sent by pregnant Kenyan mothers, which reduces the helpdesk’s workload by ∼12%.
Formally Verified SAT-Based AI Planning
Authors: Abdulaziz Mohammad , Kurz Friedrich
Published: June 2023

We present an executable formally verified SAT encoding of ground classical AI planning problems. We use the theorem prover Isabelle/HOL to perform the verification. We experimentally test the verified encoding and show that it can be used for reasonably sized standard planning benchmarks. We also use it as a reference to test a state-of-the-art SAT-based
planner, showing that it sometimes falsely claims that problems have no solutions of certain lengths.
Two Wrongs Don’t Make a Right: Combating Confirmation Bias in Learning with Label Noise
Authors: Chen Mingcai , Cheng Hao , Du Yuntao , Xu Ming , Jiang Wenyu , Wang Chongjun
Published: June 2023

Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervised training. However, discarding part of noisy labels could result in a loss of information, especially when the corruption has a dependency on data, e.g., class-dependent or instance-dependent. Moreover, from the training dynamics of a representative two-stage method DivideMix, we identify the domination of confirmation bias: pseudo-labels fail to correct a considerable amount of noisy labels, and consequently, the errors accumulate. To sufficiently exploit information from noisy labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR)—a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. As a result, it achieves state-of-the-art performance across datasets and noise types, namely CIFAR under different levels of synthetic noise and mini-WebVision and ANIMAL-10N with real-world noise.
READ: Aggregating Reconstruction Error into Out-of-Distribution Detection
Authors: Jiang Wenyu , Ge Yuxin , Cheng Hao , Chen Mingcai , Feng Shuai , Wang Chongjun
Published: June 2023

Detecting out-of-distribution (OOD) samples is crucial to the safe deployment of a classifier in the real world. However, deep neural networks are known to be overconfident for abnormal data. Existing works directly design score function by mining the inconsistency from classifier for in-distribution (ID) and OOD. In this paper, we further complement this inconsistency with reconstruction error, based on the assumption that an autoencoder trained on ID data cannot reconstruct OOD as well as ID. We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder. Specifically, the reconstruction error of raw pixels is transformed to latent space of classifier. We show that the transformed reconstruction error bridges the semantic gap and inherits detection performance from the original. Moreover, we propose an adjustment strategy to alleviate the overconfidence problem of autoencoder according to a fine-grained characterization of OOD data. Under two scenarios of pre-training and retraining, we respectively present two variants of our method, namely READ-MD (Mahalanobis Distance) only based on pre-trained classifier and READ-ED (Euclidean Distance) which retrains the classifier. Our methods do not require access to test time OOD data for fine-tuning hyperparameters. Finally, we demonstrate the effectiveness of the proposed methods through extensive comparisons with state-of-the-art OOD detection algorithms. On a CIFAR-10 pre-trained WideResNet, our method reduces the average FPR@95TPR by up to 9.8% compared with previous state-of-the-art.
Formally Verified Solution Methods for Markov Decision Processes
Authors: Schäffeler Maximilian , Abdulaziz Mohammad
Published: June 2023

We formally verify executable algorithms for solving Markov decision processes (MDPs) in the interactive theorem prover Isabelle/HOL. We build on existing formalizations of probability theory to analyze the expected total reward criterion on finite and infinite-horizon problems. Our developments formalize the Bellman equation and give conditions under which optimal policies exist. Based on this analysis, we verify dynamic programming algorithms to solve tabular MDPs. We evaluate the formally verified implementations experimentally on standard problems, compare them with state-of-the-art systems, and show that they are practical.
Robust Average-Reward Markov Decision Processes
Authors: Wang Yue , Velasquez Alvaro , Atia George , Prater-Bennette Ashley , Zou Shaofeng
Published: June 2023

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor goes to 1, and moreover when it is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.
Perception for General-purpose Robot Manipulation
Authors: Desingh Karthik
Published: June 2023

To autonomously perform tasks, a robot should continually perceive the state of its environment, reason with the task at hand, plan and execute appropriate actions. In this pipeline, perception is largely unsolved and one of the more challenging problems. Common indoor environments typically pose two main problems: 1) inherent occlusions leading to unreliable observations of objects, and 2) the presence and involvement of a wide range of objects with varying physical and visual attributes (i.e., rigid, articulated, deformable, granular, transparent, etc.). Thus, we need algorithms that can accommodate perceptual uncertainty in the state estimation and generalize to a wide range of objects. Probabilistic inference methods have been highly suitable for modeling perceptual uncertainty, and data-driven approaches using deep learning techniques have shown promising advancements toward generalization. Perception for manipulation is a more intricate setting requiring the best from both worlds. My research aims to develop robot perception algorithms that can generalize over objects and tasks while accommodating perceptual uncertainty to support robust task execution in the real world. In this presentation, I will briefly highlight my research in these two research threads.
Accountability Layers: Explaining Complex System Failures by Parts
Authors: Gilpin Leilani H.
Published: June 2023

With the rise of AI used for critical decision-making, many important predictions are made by complex and opaque AI algorithms. The aim of eXplainable Artificial Intelligence (XAI) is to make these opaque decision-making algorithms more transparent and trustworthy. This is often done by constructing an ``explainable model'' for a single modality or subsystem. However, this approach fails for complex systems that are made out of multiple parts. In this paper, I discuss how to explain complex system failures. I represent a complex machine as a hierarchical model of introspective sub-systems working together towards a common goal. The subsystems communicate in a common symbolic language. This work creates a set of explanatory accountability layers for trustworthy AI.
Enhance Robustness of Machine Learning with Improved Efficiency
Authors: Yan Yan
Published: June 2023

Robustness of machine learning, often referring to securing performance on different data, is always an active field due to the ubiquitous variety and diversity of data in practice. Many studies have been investigated to enhance the learning process robust in recent years. To this end, there is usually a trade-off that results in somewhat extra cost, e.g., more data samples, more complicated objective functions, more iterations to converge in optimization, etc. Then this problem boils down to finding a better trade-off under some conditions. My recent research focuses on robust machine learning with improved efficiency. Particularly, the efficiency here represents learning speed to find a model, and the number of data required to secure the robustness. In the talk, I will survey three pieces of my recent research by elaborating the algorithmic idea and theoretical analysis as technical contributions --- (i) epoch stochastic gradient descent ascent for min-max problems, (ii) stochastic optimization algorithm for non-convex inf-projection problems, and (iii) neighborhood conformal prediction. In the first two pieces of work, the proposed optimization algorithms are general and cover objective functions for robust machine learning. In the third one, I will elaborate an efficient conformal prediction algorithm that guarantee the robustness of prediction after model is trained. Particularly, the efficiency of conformal prediction is measured by its bandwidth.
Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning
Authors: Kapoor Maya , Napolitano Michael , Quance Jonathan , Moyer Thomas , Krishnan Siddharth
Published: June 2023

The use of voice-over-IP technology has rapidly expanded over the past several years, and has thus become a significant portion of traffic in the real, complex network environment. Deep packet inspection and middlebox technologies need to analyze call flows in order to perform network management, load-balancing, content monitoring, forensic analysis, and intelligence gathering. Because the session setup and management data can be sent on different ports or out of sync with VoIP call data over the Real-time Transport Protocol (RTP) with low latency, inspection software may miss calls or parts of calls. To solve this problem, we engineered two different deep learning models based on hidden representation learning. MAPLE, a matrix-based encoder which transforms packets into an image representation, uses convolutional neural networks to determine RTP packets from data flow. DATE is a density-analysis based tensor encoder which transforms packet data into a three-dimensional point cloud representation. We then perform density-based clustering over the point clouds as latent representations of the data, and classify packets as RTP or non-RTP based on their statistical clustering features. In this research, we show that these tools may allow a data collection and analysis pipeline to begin detecting and buffering RTP streams for later session association, solving the initial drop problem. MAPLE achieves over ninety-nine percent accuracy in RTP/non-RTP detection. The results of our experiments show that both models can not only classify RTP versus non-RTP packet streams, but could extend to other network traffic classification problems in real deployments of network analysis pipelines.
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
Authors: Adams Jadie , Lu Steven , Gorski Krzysztof M. , Rocha Graca , Wagstaff Kiri L.
Published: June 2023

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model ac- accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.
Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks
Authors: Bandlamudi Jayachandu , Mukherjee Kushal , Agarwal Prerna , Dechu Sampath , Huo Siyu , Isahagian Vatche , Muthusamy Vinod , Purushothaman Naveen , Sindhgatta Renuka
Published: June 2023

Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.
Vessel-to-Vessel Motion Compensation with Reinforcement Learning
Authors: Herland Sverre , Bach Kerstin
Published: June 2023

Actuation delay poses a challenge for robotic arms and cranes. This is especially the case in dynamic environments where the robot arm or the objects it is trying to manipulate are moved by exogenous forces. In this paper, we consider the task of using a robotic arm to compensate for relative motion between two vessels at sea. We construct a hybrid controller that combines an Inverse Kinematic (IK) solver with a Reinforcement Learning (RL) agent that issues small corrections to the IK input. The solution is empirically evaluated in a simulated environment under several sea states and actuation delays. We observe that more intense waves and larger actuation delays have an adverse effect on the IK controller's ability to compensate for vessel motion. The RL agent is shown to be effective at mitigating large parts of these errors, both in the average case and in the worst case. Its modest requirement for sensory information, combined with the inherent safety in only making small adjustments, also makes it a promising approach for real-world deployment.
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
Authors: Lee Julian , Viswanath Kamal , Sharma Alisha , Geder Jason , Pruessner Marius , Zhou Brian
Published: June 2023

Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide high maneuverability for naval tasks such as surveillance and terrain exploration. Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics. We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. Our inverse model finds a set of fin kinematics with the multi-objective goal of reaching a target thrust and creating a smooth kinematic transition between flapping cycles. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.
Exploring Social Biases of Large Language Models in a College Artificial Intelligence Course
Authors: Kolisko Skylar , Anderson Carolyn Jane
Published: June 2023

Large neural network-based language models play an increasingly important role in contemporary AI. Although these models demonstrate sophisticated text generation capabilities, they have also been shown to reproduce harmful social biases contained in their training data. This paper presents a project that guides students through an exploration of social biases in large language models.

As a final project for an intermediate college course in Artificial Intelligence, students developed a bias probe task for a previously-unstudied aspect of sociolinguistic or sociocultural bias they were interested in exploring. Through the process of constructing a dataset and evaluation metric to measure bias, students mastered key technical concepts, including how to run contemporary neural networks for natural language processing tasks; construct datasets and evaluation metrics; and analyze experimental results. Students reported their findings in an in-class presentation and a final report, recounting patterns of predictions that surprised, unsettled, and sparked interest in advocating for technology that reflects a more diverse set of backgrounds and experiences.

Through this project, students engage with and even contribute to a growing body of scholarly work on social biases in large language models.
Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling
Authors: Nkambou Roger , Brisson Janie , Tato Ange , Robert Serge
Published: June 2023

In our previous works, we presented Logic-Muse as an Intelligent Tutoring System that helps learners improve logical reasoning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians, and reasoning psychologists). A catalog of reasoning errors (syntactic and semantic) has been established, in addition to an explicit representation of semantic knowledge and the structures and meta-structures underlying conditional reasoning. A Bayesian network with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner skills.
This paper presents an evaluation of the learner-model components in Logic-Muse (a bayesian learner model). We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. These data were used to develop a psychometric model for initializing the learner's model and validating the structure of the initial Bayesian network. We have also developed a neural architecture on which a model was trained to support a deep knowledge tracing (DKT) process. The proposed neural architecture improves the initial version of DKT by allowing the integration of expert knowledge (through the Bayesian Expert Validation Network) and allowing better generalization of knowledge with few samples. The results show a significant improvement in the predictive power of the learner model. The analysis of the results of the psychometric model also illustrates an excellent potential for improving the Bayesian network's structure and the learner model's initialization process.
An Introduction to Rule-Based Feature and Object Perception for Middle School Students
Authors: DiPaola Daniella , Malachowsky Parker , Blair Black Nancye , Alghowinem Sharifa , Du Xiaoxue , Breazeal Cynthia
Published: June 2023

The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule-based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons. The objective of the first lesson is to introduce students to the concept of feature detection, or how a computer can break down visual input into lower-level features. The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to classify higher-order objects. We discuss how this tool can be used as a "first step" to the more complex concept ideas of data representation and neural networks.
“How Can I Code A.I. Responsibly?”: The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I.
Authors: Pang H. Nicole , Parks Robert , Breazeal Cynthia , Abelson Hal
Published: June 2023

Teaching young people about artificial intelligence (A.I.) is recognized globally as an important education effort by organizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12. A common theme among K-12 A.I. education programs is teaching how A.I. can impact society in both positive and negative ways. We present an effective tool that teaches young people about the societal impact of A.I. that goes one step further: empowering K-12 students to use tools and frameworks to create socially responsible A.I. The computational action process is a curriculum and toolkit that gives students the lessons and tools to evaluate positive and negative impacts of A.I. and consider how they can create beneficial solutions that involve A.I. and computing technology. In a human-subject research study, 101 U.S. and international students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational action process. Pre-post questionnaires measured on the Likert scale students’ perception of A.I. in society and students' desire to use A.I. in their projects. Analysis of the results shows that students who identified as female agreed more strongly with having a concern about the impacts of A.I. than those who identified as male. Students also wrote open-ended responses to questions about what socially responsible technology means to them pre- and post-study. Analysis shows that post-intervention, students were more aware of ethical considerations and what tools they can use to code A.I. responsibly. In addition, students engaged actively with tools in the computational action toolkit, specifically the novel impact matrix, to describe the positive and negative impacts of A.I. technologies like facial recognition. Students demonstrated breadth and depth of discussion of various A.I. technologies' far-reaching positive and negative impacts. These promising results indicate that the computational action process can be a helpful addition to A.I. education programs in furnishing tools for students to analyze the effects of A.I. on society and plan how they can create and use socially responsible A.I.
Guiding Students to Investigate What Google Speech Recognition Knows about Language
Authors: Touretzky David S. , Gardner-McCune Christina
Published: June 2023

Today, children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonological, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appreciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could potentially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine translation, or question answering.
Learning Adaptive Game Soundtrack Control
Authors: Dorsey Aaron , Neller Todd W. , Tran Hien G. , Yilmaz Veysel
Published: June 2023

In this paper, we demonstrate a novel technique for dynamically generating an emotionally-directed video game soundtrack. We begin with a human Conductor observing gameplay and directing associated emotions that would enhance the observed gameplay experience. We apply supervised learning to data sampled from synchronized input gameplay features and Conductor output emotional direction features in order to fit a mathematical model to the Conductor's emotional direction. Then, during gameplay, the emotional direction model maps gameplay state input to emotional direction output, which is then input to a music generation module that dynamically generates emotionally-relevant music during gameplay. Our empirical study suggests that random forests serve well for modeling the Conductor for our two experimental game genres.
Modeling Strategies as Programs: How to Study Strategy Differences in Intelligent Systems with Program Synthesis
Authors: Ainooson James
Published: June 2023

When faced with novel tasks, humans have the ability to form successful strategies, seemingly without much effort. Artificial systems, on the other, hand cannot, at least when the flexibility at which humans perform is considered. For my dissertation, I am using program synthesis as a tool to study the factors that affect strategy choices in intelligent systems. I am evaluating my work through agents that reason through problems from the Abstract Reasoning Corpus and The Block Design Task.
Assessing Learned Representations under Open-World Novelty
Authors: Clary Kaleigh
Published: June 2023

My dissertation research focuses on sequential decision-making (SDM) in complex environments, and how agents can perform well even when novelty is introduced to those environments. The problem of how agents can respond intelligently to novelty has been a long-standing challenge in AI, and poses unique problems across approaches to SDM. This question has been studied in various formulations, including open-world learning and reasoning, transfer learning, concept drift, and statistical relational learning. Classical and modern approaches in agent design offer tradeoffs in human effort for feature encoding, ease of deployment in new domains, and the development of both provably and empirically reliable policies. I propose a formalism for studying open-world novelty in SDM processes with feature-rich observations. I study the conditions under which causal-relational queries can be estimated from non-novel observations, and empirically examine the effects of open-world novelty on agent behavior.
Efficient Non-parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
Authors: Gallego-Mejia Joseph A.
Published: June 2023

The main goal of this thesis is to develop efficient non-parametric density estimation methods that can be integrated with deep learning architectures, for instance, convolutional neural networks and transformers. Density estimation methods can be applied to different problems in statistics and machine learning. They may be used to solve tasks such as anomaly detection, generative models, semi-supervised learning, compression, text-to-speech, among others. The present work will mainly focus on the application of the method in anomaly and outlier detection tasks such as medical anomaly detection, fraud detection, video surveillance, time series anomaly detection, industrial damage detection, among others. A recent approach to non-parametric density estimation is neural density estimation. One advantage of these methods is that they can be integrated with deep learning architectures and trained using gradient descent. Most of these methods are based on neural network implementations of normalizing flows which transform an original simpler distribution to a more complex one. The approach of this thesis is based on a different idea that combines random Fourier features with density matrices to estimate the underlying distribution function. The method can be seen as an approximation of the popular kernel density estimation method but without the inherent computational cost.
Learning and Planning under Uncertainty for Conservation Decisions
Authors: Xu Lily
Published: June 2023

My research focuses on new techniques in machine learning and game theory to optimally allocate our scarce resources in multi-agent settings to maximize environmental sustainability. Drawing scientific questions from my close partnership with conservation organizations, I have advanced new lines of research in learning and planning under uncertainty, inspired by the low-data, noisy, and dynamic settings faced by rangers on the frontlines of protected areas.
Model Selection of Graph Signage Models Using Maximum Likelihood (Student Abstract)
Authors: Brilliantova Angelina , Bezáková Ivona
Published: June 2023

Complex systems across various domains can be naturally modeled as signed networks with positive and negative edges. In this work, we design a new class of signage models and show how to select the model parameters that best fit real-world datasets using maximum likelihood.
Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)
Authors: Chen Feng , Wang Chenghe , Zhang Fuxiang , Ding Hao , Zhong Qiaoyong , Pu Shiliang , Zhang Zongzhang
Published: June 2023

Multi-agent pathfinding (MAPF) is essential to large-scale robotic coordination tasks. Planning-based algorithms show their advantages in collision avoidance while avoiding exponential growth in the number of agents. Reinforcement-learning (RL)-based algorithms can be deployed efficiently but cannot prevent collisions entirely due to the lack of hard constraints. This paper combines the merits of planning-based and RL-based MAPF methods to propose a deployment-efficient and collision-free MAPF algorithm. The experiments show the effectiveness of our approach.
SkateboardAI: The Coolest Video Action Recognition for Skateboarding (Student Abstract)
Authors: Chen Hanxiao
Published: June 2023

Impressed by the coolest skateboarding sports program from 2021 Tokyo Olympic Games, we are the first to curate the original real-world video datasets "SkateboardAI" in the wild, even self-design and implement diverse uni-modal and multi-modal video action recognition approaches to recognize different tricks accurately. For uni-modal methods, we separately apply (1)CNN and LSTM; (2)CNN and BiLSTM; (3)CNN and BiLSTM with effective attention mechanisms; (4)Transformer-based action recognition pipeline. Transferred to the multi-modal conditions, we investigated the two-stream Inflated-3D architecture on "SkateboardAI" datasets to compare its performance with uni-modal cases. In sum, our objective is developing an excellent AI sport referee for the coolest skateboarding competitions.
Transformer-Based Multi-Hop Question Generation (Student Abstract)
Authors: Emerson John , Chali Yllias
Published: June 2023

Question generation is the parallel task of question answering, where given an input context and, optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts to produce increasingly detailed questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this work, we apply a transformer model to the task of multi-hop question generation without utilizing any sentence-level supporting fact information. We utilize concepts that have proven effective in single-hop question generation, including a copy mechanism and placeholder tokens. We evaluate our model’s performance on the HotpotQA dataset using automated evaluation metrics, including BLEU, ROUGE and METEOR and show an improvement over the previous work.
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)
Authors: Gallego-Mejia Joseph A. , Bustos-Brinez Oscar A. , González Fabio A.
Published: June 2023

This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, that learns a low-dimensional representation of the data, with a density-estimation model based on density matrices in an end-to-end architecture that can be trained using gradient-based optimization techniques. A systematic experimental evaluation was performed on different benchmark datasets. The experimental results show that the method is able to outperform other state-of-the-art methods.
Towards Safe Reinforcement Learning via OOD Dynamics Detection in Autonomous Driving System (Student Abstract)
Authors: Gardille Arnaud , Ahmad Ola
Published: June 2023

Deep reinforcement learning (DRL) has proven effective in training agents to achieve goals in complex environments. However, a trained RL agent may exhibit, during deployment, unexpected behavior when faced with a situation where its state transitions differ even slightly from the training environment. Such a situation can arise for a variety of reasons. Rapid and accurate detection of anomalous behavior appears to be a prerequisite for using DRL in safety-critical systems, such as autonomous driving. We propose a novel OOD detection algorithm based on modeling the transition function of the training environment. Our method captures the bias of model behavior when encountering subtle changes of dynamics while maintaining a low false positive rate. Preliminary evaluations on the realistic simulator CARLA corroborate the relevance of our proposed method.
Expert Data Augmentation in Imitation Learning (Student Abstract)
Authors: Han Fuguang , Zhang Zongzhang
Published: June 2023

Behavioral Cloning (BC) is a simple and effective imitation learning algorithm, which suffers from compounding error due to covariate shift. One solution is to use enough data for training. However, the amount of expert demonstrations available is usually limited. So we propose an effective method to augment expert demonstrations to alleviate the problem of compounding error in BC. It operates by estimating the similarity of states and filtering out transitions that can go back to the states similar to ones in expert demonstrations during the process of sampling. The data filtered out along with original expert demonstrations are used for training. We evaluate the performance of our method on several Atari tasks and continuous MuJoCo control tasks. Empirically, BC trained with the augmented data significantly outperform BC trained with the original expert demonstrations.
Less Is More: Volatility Forecasting with Contrastive Representation Learning (Student Abstract)
Authors: Huang Yanlong , Tai Wenxin , Zhong Ting , Zhang Kunpeng
Published: June 2023

Earnings conference calls are indicative information events for volatility forecasting, which is essential for financial risk management and asset pricing. Although recent volatility forecasting models have explored the textual content of conference calls for prediction, they suffer from modeling the long-text and representing the risk-relevant information. This work proposes to identify key sentences for robust and interpretable transcript representation learning based on the cognitive theory. Specifically, we introduce TextRank to find key sentences and leverage attention mechanism to screen out the candidates by modeling the semantic correlations. Upon on the structural information of earning conference calls, we propose a structure-based contrastive learning method to facilitate the effective transcript representation. Empirical results on the benchmark dataset demonstrate the superiority of our model over competitive baselines in volatility forecasting.
Evaluating Robustness of Vision Transformers on Imbalanced Datasets (Student Abstract)
Authors: Li Kevin , Duggal Rahul , Chau Duen Horng
Published: June 2023

Data in the real world is commonly imbalanced across classes. Training neural networks on imbalanced datasets often leads to poor performance on rare classes. Existing work in this area has primarily focused on Convolution Neural Networks (CNN), which are increasingly being replaced by Self-Attention-based Vision Transformers (ViT). Fundamentally, ViTs differ from CNNs in that they offer the flexibility in learning the appropriate inductive bias conducive to improving performance. This work is among the first to evaluate the performance of ViTs under class imbalance. We find that accuracy degradation in the presence of class imbalance is much more prominent in ViTs compared to CNNs. This degradation can be partially mitigated through loss reweighting - a popular strategy that increases the loss contributed by rare classes. We investigate the impact of loss reweighting on different components of a ViT, namely, the patch embedding, self-attention backbone, and linear classifier. Our ongoing investigations reveal that loss reweighting impacts mostly the linear classifier and self-attention backbone while having a small and negligible effect on the embedding layer.
A Highly Efficient Marine Mammals Classifier Based on a Cross-Covariance Attended Compact Feed-Forward Sequential Memory Network (Student Abstract)
Authors: Liu Xiangrui , Cheng Julian
Published: June 2023

Military active sonar and marine transportation are detrimental to the livelihood of marine mammals and the ecosystem. Early detection and classification of marine mammals using machine learning can help humans to mitigate the harm to marine mammals. This paper proposes a cross-covariance attended compact Feed-Forward Sequential Memory Network (CC-FSMN). The proposed framework shows improved efficiency over multiple convolutional neural network (CNN) backbones. It also maintains a relatively decent performance.
Risk-Aware Decentralized Safe Control via Dynamic Responsibility Allocation (Student Abstract)
Authors: Lyu Yiwei , Luo Wenhao , Dolan John M.
Published: June 2023

In this work, we present a novel risk-aware decentralized Control Barrier Function (CBF)-based controller for multi-agent systems. The proposed decentralized controller is composed based on pairwise agent responsibility shares (a percentage), calculated from the risk evaluation of each individual agent faces in a multi-agent interaction environment. With our proposed CBF-inspired risk evaluation framework, the responsibility portions between pairwise agents are dynamically updated based on the relative risk they face. Our method allows agents with lower risk to enjoy a higher level of freedom in terms of a wider action space, and the agents exposed to higher risk are constrained more tightly on action spaces, and are therefore forced to proceed with caution.
Counting Knot Mosaics with ALLSAT (Student Abstract)
Authors: Miller Hannah
Published: June 2023

Knot mosaics are a model of a quantum knot system. A knot mosaic is a m-by-n grid where each location on the grid may contain any of 11 possible tiles such that the final layout has closed loops. Oh et al. proved a recurrence relation of state matrices to count the number of m-by-n knot mosaics. Our contribution is to use ALLSAT solvers to count knot mosaics and to experimentally try different ways to encode the AT MOST ONE constraint in SAT. We plan to use our SAT method as a tool to list knot mosaics of interest for specific classes of knots.
Novel Intent Detection and Active Learning Based Classification (Student Abstract)
Authors: Mullick Ankan
Published: June 2023

Novel intent class detection is an important problem in real world scenario for conversational agents for continuous interaction. Several research works have been done to detect novel intents in a mono-lingual (primarily English) texts and
images. But, current systems lack an end-to-end universal framework to detect novel intents across various different languages with less human annotation effort for mis-classified and system rejected samples. This paper proposes
NIDAL (Novel Intent Detection and Active Learning based
classification), a semi-supervised framework to detect novel
intents while reducing human annotation cost. Empirical results on various benchmark datasets demonstrate that this system outperforms the baseline methods by more than 10%
margin for accuracy and macro-F1. The system achieves this while maintaining overall annotation cost to be just ~6-10% of the unlabeled data available to the system.
Can You Answer This? – Exploring Zero-Shot QA Generalization Capabilities in Large Language Models (Student Abstract)
Authors: Sengupta Saptarshi , Ghosh Shreya , Nakov Preslav , Mitra Prasenjit
Published: June 2023

The buzz around Transformer-based language models (TLM) such as BERT, RoBERTa, etc. is well-founded owing to their impressive results on an array of tasks. However, when applied to areas needing specialized knowledge (closed-domain), such as medical, finance, etc. their performance takes drastic hits, sometimes more than their older recurrent/convolutional counterparts. In this paper, we explore zero-shot capabilities of large LMs for extractive QA. Our objective is to examine performance change in the face of domain drift i.e. when the target domain data is vastly different in semantic and statistical properties from the source domain and attempt to explain the subsequent behavior. To this end, we present two studies in this paper while planning further experiments later down the road. Our findings indicate flaws in the current generation of TLM limiting their performance on closed-domain tasks.