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Found 27 papers, 13 papers with code
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information commonly encountered in real-world scenarios, remains in its infancy.
Enterprise Benchmarks for Large Language Model Evaluation
The advancement of large language models (LLMs) has led to a greater challenge of having a rigorous and systematic evaluation of complex tasks performed, especially in enterprise applications.
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
Understanding the causal interaction of time series variables can contribute to time series data analysis for many real-world applications, such as climate forecasting and extreme weather alerts.
When Heterophily Meets Heterogeneity: New Graph Benchmarks and Effective Methods
However, existing benchmarks for graph learning often focus on heterogeneous graphs with homophily or homogeneous graphs with heterophily, leaving a gap in understanding how methods perform on graphs that are both heterogeneous and heterophilic.
AIM: Attributing, Interpreting, Mitigating Data Unfairness
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals.
Neural Active Learning Beyond Bandits
We study both stream-based and pool-based active learning with neural network approximations.
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models.
Adversarial Attacks on Fairness of Graph Neural Networks
Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e. g., female) in graph-based applications.
Self-Specialization: Uncovering Latent Expertise within Large Language Models
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is aligned to follow general instructions using instructional data generated from the model itself starting from a handful of human-written seeds.
Class-Imbalanced Graph Learning without Class Rebalancing
In this work, we approach the root cause of class-imbalance bias from an topological paradigm.
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
To bridge this gap, we propose a generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i. e., each task corresponds to the prediction of one particular class.
FairGen: Towards Fair Graph Generation
In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.
Fairness-aware Multi-view Clustering
We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.
STERLING: Synergistic Representation Learning on Bipartite Graphs
Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs.
Retrieval Based Time Series Forecasting
In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.
ARIEL: Adversarial Graph Contrastive Learning
In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints.
COIN: Co-Cluster Infomax for Bipartite Graphs
We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.
Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph representation learning.
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
Heterogeneous Contrastive Learning
We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue.
Network of Tensor Time Series
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making.
PAGAN: Portfolio Analysis with Generative Adversarial Networks
Sound financial theories demonstrate that in an efficient marketplace all information available today, including expectations on future events, are represented in today prices whereas future price trend is driven by the uncertainty.
Computational Finance Statistical Finance
