GenAI4ELab/papercli-papers huggingface dataset:
AlphaSnake: Policy Iteration on a Nondeterministic NP-Hard Markov Decision Process (Student Abstract)
Authors: Kevin Du; Ian Gemp; Yi Wu; Yingying Wu
Venue: AAAI 2023 (main)

Abstract: Reinforcement learning has been used to approach well-known NP-hard combinatorial problems in graph theory. Among these, Hamiltonian cycle problems are exceptionally difficult to analyze, even when restricted to individual instances of structurally complex graphs. In this paper, we use Monte Carlo Tree Search (MCTS), the search algorithm behind many state-of-the-art reinforcement learning algorithms such as AlphaZero, to create autonomous agents that learn to play the game of Snake, a game centered on properties of Hamiltonian cycles on grid graphs. The game of Snake can be formulated as a single-player discounted Markov Decision Process (MDP), where the agent must behave optimally in a stochastic environment. Determining the optimal policy for Snake, defined as the policy that maximizes the probability of winning -- or win rate -- with higher priority and minimizes the expected number of time steps to win with lower priority, is conjectured to be NP-hard. Performance-wise, compared to prior work in the Snake game, our algorithm is the first to achieve a win rate over 0.5 (a uniform random policy achieves a win rate < 2.57 x 10^{-15}), demonstrating the versatility of AlphaZero in tackling NP-hard problems.
Transformer-Based Multi-Hop Question Generation (Student Abstract)
Authors: John Emerson; Yllias Chali
Venue: AAAI 2023 (main)

Abstract: 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.
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Authors: Muhammad Hasan Ferdous; Uzma Hasan; Md Osman Gani
Venue: AAAI 2023 (main)

Abstract: Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations. In this study, we present a novel constraint-based CD approach for autocorrelated and non-stationary time series data (eCDANs) capable of detecting lagged and contemporaneous causal relationships along with temporal changes. eCDANs addresses high dimensionality by optimizing the conditioning sets while conducting conditional independence (CI) tests and identifies the changes in causal relations by introducing a surrogate variable to represent time dependency. Experiments on synthetic and real-world data show that eCDANs can identify time influence and outperform the baselines.
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)
Authors: Joseph A. Gallego-Mejia; Oscar A. Bustos-Brinez; Fabio A. González
Venue: AAAI 2023 (main)

Abstract: 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.
Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)
Authors: Briti Gangopadhyay; Pallab Dasgupta; Soumyajit Dey
Venue: AAAI 2023 (main)

Abstract: Neural network pruning is a technique of network compression by removing weights of lower importance from an optimized neural network. Often, pruned networks are compared
in terms of accuracy, which is realized in terms of rewards for Deep Reinforcement Learning (DRL) networks. However, networks that estimate control actions for safety-critical tasks, must also adhere to safety requirements along with obtaining rewards. We propose a methodology to iteratively refine the weights of a pruned neural network such that we get a sparse high-performance network without significant side effects on safety.
Towards Fair and Selectively Privacy-Preserving Models Using Negative Multi-Task Learning (Student Abstract)
Authors: Liyuan Gao; Huixin Zhan; Austin Chen; Victor S. Sheng
Venue: AAAI 2023 (main)

Abstract: Deep learning models have shown great performances in natural language processing tasks. While much attention has been paid to improvements in utility, privacy leakage and social bias are two major concerns arising in trained models. In order to tackle these problems, we protect individuals' sensitive information and mitigate gender bias simultaneously. First, we propose a selective privacy-preserving method that only obscures individuals' sensitive information. Then we propose a negative multi-task learning framework to mitigate the gender bias which contains a main task and a gender prediction task.  We analyze two existing word embeddings and evaluate them on sentiment analysis and a medical text classification task. Our experimental results show that our negative multi-task learning framework can mitigate the gender bias while keeping models’ utility.
Towards Safe Reinforcement Learning via OOD Dynamics Detection in Autonomous Driving System (Student Abstract)
Authors: Arnaud Gardille; Ola Ahmad
Venue: AAAI 2023 (main)

Abstract: 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.
Neural Implicit Surface Reconstruction from Noisy Camera Observations (Student Abstract)
Authors: Sarthak Gupta; Patrik Huber
Venue: AAAI 2023 (main)

Abstract: Representing 3D objects and scenes with neural radiance fields has become very popular over the last years. Recently, surface-based representations have been proposed, that allow to reconstruct 3D objects from simple photographs. However, most current techniques require an accurate camera calibration, i.e. camera parameters corresponding to each image, which is often a difficult task to do in real-life situations. To this end, we propose a method for learning 3D surfaces from noisy camera parameters. We show that we can learn camera parameters together with learning the surface representation, and demonstrate good quality 3D surface reconstruction even with noisy camera observations.
Expert Data Augmentation in Imitation Learning (Student Abstract)
Authors: Fuguang Han; Zongzhang Zhang
Venue: AAAI 2023 (main)

Abstract: 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.
Unsupervised Contrastive Representation Learning for 3D Mesh Segmentation (Student Abstract)
Authors: Ayaan Haque; Hankyu Moon; Heng Hao; Sima Didari; Jae Oh Woo; Patrick Bangert
Venue: AAAI 2023 (main)

Abstract: 3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats. Triangular meshes are an efficient representation for irregular, non-uniform 3D objects. However, meshes are often challenging to annotate due to their high computational complexity. Therefore, it is desirable to train segmentation networks with limited-labeled data. Self-supervised learning (SSL), a form of unsupervised representation learning, is a growing alternative to fully-supervised learning which can decrease the burden of supervision for training. Specifically, contrastive learning (CL), a form of SSL, has recently been explored to solve limited-labeled data tasks. We propose SSL-MeshCNN, a CL method for pre-training CNNs for mesh segmentation. We take inspiration from prior CL frameworks to design a novel CL algorithm specialized for meshes. Our preliminary experiments show promising results in reducing the heavy labeled data requirement needed for mesh segmentation by at least 33%.
Invertible Conditional GAN Revisited: Photo-to-Manga Face Translation with Modern Architectures (Student Abstract)
Authors: Taro Hatakeyama; Ryusuke Saito; Komei Hiruta; Atsushi Hashimoto; Satoshi Kurihara
Venue: AAAI 2023 (main)

Abstract: Recent style translation methods have extended their transferability from texture to geometry. However, performing translation while preserving image content when there is a significant style difference is still an open problem. To overcome this problem, we propose Invertible Conditional Fast GAN (IcFGAN) based on GAN inversion and cFGAN. It allows for unpaired photo-to-manga face translation. Experimental results show that our method could translate styles under significant style gaps, while the state-of-the-art methods could hardly preserve image content.
Exploring Hypergraph of Earnings Call for Risk Prediction (Student Abstract)
Authors: Yi He; Wenxin Tai; Fan Zhou; Yi Yang
Venue: AAAI 2023 (main)

Abstract: In financial economics, studies have shown that the textual content in the earnings conference call transcript has predictive power for a firm's future risk. However, the conference call transcript is very long and contains diverse non-relevant content, which poses challenges for the text-based risk forecast. This study investigates the structural dependency within a conference call transcript by explicitly modeling the dialogue between managers and analysts. Specifically, we utilize TextRank to extract information and exploit the semantic correlation within a discussion using hypergraph learning. This novel design can improve the transcript representation performance and reduce the risk of forecast errors. Experimental results on a large-scale dataset show that our approach can significantly improve prediction performance compared to state-of-the-art text-based models.
An Analysis of the Deliberation and Task Performance of an Active Logic Based Agent (Student Abstract)
Authors: Anthony Herron; Darsana P. Josyula
Venue: AAAI 2023 (main)

Abstract: Active logic is a time-situated reasoner that can track the history of inferences, detect contradictions, and make parallel inferences in time. In this paper, we explore the behavior of an active-logic based agent on different sets of action selection axioms for a time-constrained target search task. We compare the performance of a baseline set of axioms that does not avoid redundant actions with five other axiom sets that avoid repeated actions but vary in their knowledge content. The results of these experiments show the importance of balancing boldness and caution for target search.
Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)
Authors: Jinyu Hong; Fan Zhou; Qiang Gao; Ping Kuang; Kunpeng Zhang
Venue: AAAI 2023 (main)

Abstract: Accurately predicting human mobility is a critical task in location-based recommendation. Most prior approaches focus on fusing multiple semantics trajectories to forecast the future movement of people, and fail to consider the distinct relations in underlying context of human mobility, resulting in a narrow perspective to comprehend human motions. Inspired by recent advances in disentanglement learning, we propose a novel self-supervised method called SelfMove for next POI prediction. SelfMove seeks to disentangle the potential time-invariant and time-varying factors from massive trajectories, which provides an interpretable view to understand the complex semantics underlying human mobility representations. To address the data sparsity issue, we present two realistic trajectory augmentation approaches to help understand the intrinsic periodicity and constantly changing intents of humans. In addition, a POI-centric graph structure is proposed to explore both homogeneous and heterogeneous collaborative signals behind historical trajectories. Experiments on two real-world datasets demonstrate the superiority of SelfMove compared to the state-of-the-art baselines.
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
Authors: Li-Chun Huang; Nai-Zen Hsueh; Yen-Che Chien; Wei-Yao Wang; Kuang-Da Wang; Wen-Chih Peng
Venue: AAAI 2023 (main)

Abstract: Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement.
However, existing approaches are only able to evaluate offline performance since testing in real-time matches requires exhaustive costs and cannot be replicated.
To test in a safe and reproducible simulator, we focus on turn-based sports and introduce a badminton environment by simulating rallies with different angles of view and designing the states, actions, and training procedures.
This benefits not only coaches and players by simulating past matches for tactic investigation, but also researchers from rapidly evaluating their novel algorithms.
Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.
Less Is More: Volatility Forecasting with Contrastive Representation Learning (Student Abstract)
Authors: Yanlong Huang; Wenxin Tai; Ting Zhong; Kunpeng Zhang
Venue: AAAI 2023 (main)

Abstract: 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.
Understand Restart of SAT Solver Using Search Similarity Index (Student Abstract)
Authors: Yoichiro Iida; Tomohiro Sonobe; Mary Inaba
Venue: AAAI 2023 (main)

Abstract: SAT solvers are widely used to solve many industrial problems because of their high performance, which is achieved by various heuristic methods.
Understanding why these methods are effective is essential to improving them. One approach to this is analyzing them using qualitative measurements.
In our previous study, we proposed search similarity index (SSI), a metric to quantify the similarity between searches. SSI significantly improved the performance of the parallel SAT solver.
Here, we apply SSI to analyze the effect of restart, a key SAT solver technique.
Experiments using SSI reveal the correlation between the difficulty of instances and the search change effect by restart, and the reason behind the effectiveness of the state-of-the-art restart method is also explained.
In-Game Toxic Language Detection: Shared Task and Attention Residuals (Student Abstract)
Authors: Yuanzhe Jia; Weixuan Wu; Feiqi Cao; Soyeon Caren Han
Venue: AAAI 2023 (main)

Abstract: In-game toxic language becomes the hot potato in the gaming industry and community. There have been several online game toxicity analysis frameworks and models proposed. However, it is still challenging to detect toxicity due to the nature of in-game chat, which has extremely short length. In this paper, we describe how the in-game toxic language shared task has been established using the real-world in-game chat data. In addition, we propose and introduce the model/framework for toxic language token tagging (slot filling) from the in-game chat. The data and code will be released.
CKS: A Community-Based K-shell Decomposition Approach Using Community Bridge Nodes for Influence Maximization (Student Abstract)
Authors: Inder Khatri; Aaryan Gupta; Arjun Choudhry; Aryan Tyagi; Dinesh Kumar Vishwakarma; Mukesh Prasad
Venue: AAAI 2023 (main)

Abstract: Social networks have enabled user-specific advertisements and recommendations on their platforms, which puts a significant focus on Influence Maximisation (IM) for target advertising and related tasks. The aim is to identify nodes in the network which can maximize the spread of information through a diffusion cascade. We propose a community structures-based approach that employs K-Shell algorithm with community structures to generate a score for the connections between seed nodes and communities. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We validate our approach on four publicly available networks and show its superiority to four state-of-the-art approaches while still being relatively efficient.
Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)
Authors: Jeong-Hun Kim; Tserenpurev Chuluunsaikhan; Jong-Hyeok Choi; Aziz Nasridinov
Venue: AAAI 2023 (main)

Abstract: DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch to update clusters when new data is inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates clusters by identifying in advance regions where cluster updates will occur. Also, through extensive experiments, we show that our method provides clustering results similar to those of DBSCAN.
Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)
Authors: Tzu-Ya Lai; Wen Jung Cheng; Jun-En Ding
Venue: AAAI 2023 (main)

Abstract: The stock market is characterized by a complex relationship between companies and the market. This study combines a sequential graph structure with attention mechanisms to learn global and local information within temporal time. Specifically, our proposed “GAT-AGNN” module compares model performance across multiple industries as well as within single industries. The results show that the proposed framework outperforms the state-of-the-art methods in predicting stock trends across multiple industries on Taiwan Stock datasets.
Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies (Student Abstract)
Authors: Anish Lakkapragada; Essam Sleiman; Saimourya Surabhi; Dennis P. Wall
Venue: AAAI 2023 (main)

Abstract: Multi-Task Learning (MTL) is a growing subject of interest in deep learning, due to its ability to train models more efficiently on multiple tasks compared to using a group of conventional single-task models. However, MTL can be impractical as certain tasks can dominate training and hurt performance in others, thus making some tasks perform better in a single-task model compared to a multi-task one. Such problems are broadly classified as negative transfer, and many prior approaches in the literature have been made to mitigate these issues. One such current approach to alleviate negative transfer is to weight each of the losses so that they are on the same scale. Whereas current loss balancing approaches rely on either optimization or complex numerical analysis, none directly scale the losses based on their observed magnitudes. We propose multiple techniques for loss balancing based on scaling by the exponential moving average and benchmark them against current best-performing methods on three established datasets. On these datasets, they achieve comparable, if not higher, performance compared to current best-performing methods.
A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract)
Authors: Taejoon Lee; Hyunsu Mun; Youngseok Lee
Venue: AAAI 2023 (main)

Abstract: We propose CARLA-FLMon, which can monitor the progress of running federated learning (FL) training in the open-source autonomous driving simulation software, CARLA. The purpose of CARLA-FLMon is to visually present the status and results of federated learning training, and to provide an extensible FL training environment with which FL training can be performed repeatedly with updated learning strategies through analysis. With CARLA-FLMon, we can determine what factors have positive or negative influences on learning by visualizing training data. Then, we can optimize the parameters of the FL training model to improve the accuracy of FL. With preliminary experiments of CARLA-FLMon on lane recognition, we demonstrate that CARLA-FLmon can increase the overall accuracy from 80.33% to 93.82% by identifying lowly-contributing clients and excluding them.
Summarization Attack via Paraphrasing (Student Abstract)
Authors: Jiyao Li; Wei Liu
Venue: AAAI 2023 (main)

Abstract: Many natural language processing models are perceived to be fragile on adversarial attacks. Recent work on adversarial attack has demonstrated a high success rate on sentiment analysis as well as classification models. However, attacks to summarization models have not been well studied. Summarization tasks are rarely influenced by word substitution, since advanced abstractive summary models utilize sentence level information. In this paper, we propose a paraphrasing-based attack method to attack summarization models. We first rank the sentences in the document according to their impacts to summarization. Then, we apply paraphrasing procedure to generate adversarial samples. Finally, we test our algorithm on benchmarks datasets against others methods. Our approach achieved the highest success rate and the lowest sentence substitution rate. In addition, the adversarial samples have high semantic similarity with the original sentences.