pwc-archive/papers-with-abstracts huggingface dataset:
Does religiosity influence corporate greenwashing behavior?
Authors: Mathieu Gomes, Sylvain Marsat, Jonathan Peillex, Guillaume Pijourlet
Date: 2023-12-22 00:00:00

We analyze the influence of religious social norms on corporate greenwashing behavior. Specifically, we focus on a specific form of greenwashing: selective disclosure. Using a large sample of US firms between 2005 and 2019, we show that firms located in counties where religious adherence is high are less likely to engage in greenwashing. We also find that a stronger religious adherence within the county in which a company is located reduces the magnitude of greenwashing, when observed. We further analyze the mechanism underlying this relationship and show that religious adherence impacts greenwashing behaviors through the channel of risk aversion. A comprehensive set of robustness tests aimed at addressing potential endogeneity concerns confirms that religion is a relevant driver of corporate greenwashing behavior.
Time-inconsistent mean field and n-agent games under relative performance criteria
Authors: Zongxia Liang, Keyu Zhang
Date: 2023-12-22 00:00:00
Tasks: Portfolio Optimization

In this paper we study a time-inconsistent portfolio optimization problem for competitive agents with CARA utilities and non-exponential discounting. The utility of each agent depends on her own wealth and consumption as well as the relative wealth and consumption to her competitors. Due to the presence of a non-exponential discount factor, each agent's optimal strategy becomes time-inconsistent. In order to resolve time-inconsistency, each agent makes a decision in a sophisticated way, choosing open-loop equilibrium strategy in response to the strategies of all the other agents. We construct explicit solutions for the $n$-agent games and the corresponding mean field games (MFGs) where the limit of former yields the latter. This solution is unique in a special class of equilibria.
A hypothesis test for the long-term calibration in rating systems with overlapping time windows
Authors: Patrick Kurth, Max Nendel, Jan Streicher
Date: 2023-12-22 00:00:00

We present a statistical test that can be used to verify supervisory requirements concerning overlapping time windows for the long-term calibration in rating systems. In a first step, we show that the long-run default rate is approximately normally distributed with respect to random effects in default realization. We then perform a detailed analysis of the correlation effects caused by the overlapping time windows and solve the problem of an unknown distribution of default probabilities for the long-run default rate. In this context, we present several methods for a conservative calibration test that can deal with the unknown variance in the test statistic. We present a test for individual rating grades, and then pass to the portfolio level by suitably adapting the test statistic. We conclude with comparative statics analysing the effect of persisting customers and the number of customers per reference date.
Deep Reinforcement Learning for Quantitative Trading
Authors: Maochun Xu, Zixun Lan, Zheng Tao, Jiawei Du, Zongao Ye
Date: 2023-12-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especially in effectively handling noisy and high-frequency financial data. Striking a balance between exploration and exploitation poses another challenge for AI-driven trading agents. To surmount these hurdles, our proposed solution, QTNet, introduces an adaptive trading model that autonomously formulates QT strategies through an intelligent trading agent. Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model. To tackle the challenges posed by volatile financial datasets, we conceptualize the QT mechanism within the framework of a Partially Observable Markov Decision Process (POMDP). Moreover, by embedding imitative learning, the model can capitalize on traditional trading tactics, nurturing a balanced synergy between discovery and utilization. For a more realistic simulation, our trading agent undergoes training using minute-frequency data sourced from the live financial market. Experimental findings underscore the model's proficiency in extracting robust market features and its adaptability to diverse market conditions.
PerCNet: Periodic Complete Representation for Crystal Graphs
Authors: Jiao Huang, Qianli Xing, Jinglong Ji, Bo Yang
Date: 2023-12-03 00:00:00
Tasks: Graph Learning, Representation Learning

Crystal material representation is the foundation of crystal material research. Existing works consider crystal molecules as graph data with different representation methods and leverage the advantages of techniques in graph learning. A reasonable crystal representation method should capture the local and global information. However, existing methods only consider the local information of crystal molecules by modeling the bond distance and bond angle of first-order neighbors of atoms, which leads to the issue that different crystals will have the same representation. To solve this many-to-one issue, we consider the global information by further considering dihedral angles, which can guarantee that the proposed representation corresponds one-to-one with the crystal material. We first propose a periodic complete representation and calculation algorithm for infinite extended crystal materials. A theoretical proof for the representation that satisfies the periodic completeness is provided. Based on the proposed representation, we then propose a network for predicting crystal material properties, PerCNet, with a specially designed message passing mechanism. Extensive experiments are conducted on two real-world material benchmark datasets. The PerCNet achieves the best performance among baseline methods in terms of MAE. In addition, our results demonstrate the importance of the periodic scheme and completeness for crystal representation learning.
TypeFly: Flying Drones with Large Language Model
Authors: Guojun Chen, Xiaojing Yu, Neiwen Ling, Lin Zhong
Date: 2023-12-08 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model, Prompt Engineering

Recent advancements in robot control using large language models (LLMs) have demonstrated significant potential, primarily due to LLMs' capabilities to understand natural language commands and generate executable plans in various languages. However, in real-time and interactive applications involving mobile robots, particularly drones, the sequential token generation process inherent to LLMs introduces substantial latency, i.e. response time, in control plan generation. In this paper, we present a system called ChatFly that tackles this problem using a combination of a novel programming language called MiniSpec and its runtime to reduce the plan generation time and drone response time. That is, instead of asking an LLM to write a program (robotic plan) in the popular but verbose Python, ChatFly gets it to do it in MiniSpec specially designed for token efficiency and stream interpretation. Using a set of challenging drone tasks, we show that design choices made by ChatFly can reduce up to 62% response time and provide a more consistent user experience, enabling responsive and intelligent LLM-based drone control with efficient completion.
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
Authors: Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, Sheng Li
Date: 2023-12-20 00:00:00
Tasks: Prediction

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task. The causal feature attribution helps describe how much contribution the value of this feature can make to the risk prediction result. After the causal feature distillation, a deep neural network is applied to produce trustworthy prediction results with causal interpretability and high precision/recall. We evaluate the performance of our TDCFD method on several synthetic and real datasets, and the results demonstrate its superiority over the state-of-the-art methods regarding precision, recall, interpretability, and causality.
Second-Order Global Attention Networks for Graph Classification and Regression
Authors: Fenyu Hu, Zeyu Cui, Shu Wu, Qiang Liu, Jinlin Wu, Liang Wang & Tieniu Tan
Date: 2022-08-27 00:00:00
Proceeding: Conference 2022 8
Tasks: Classification, Graph Classification, Graph Regression, regression

Graph Neural Networks (GNNs) are powerful to learn representation of graph-structured data, which fuse both attributive and topological information. Prior researches have investigated the expressive power of GNNs by comparing it with Weisfeiler-Lehman algorithm. In spite of having achieved promising performance for the isomorphism test, existing methods assume overly restrictive requirement, which might hinder the performance on other graph-level tasks, e.g., graph classification and graph regression. In this paper, we argue the rationality of adaptively emphasizing important information. We propose a novel global attention module from two levels: channel level and node level. Specifically, we exploit second-order channel correlation to extract more discriminative representations. We validate the effectiveness of the proposed approach through extensive experiments on eight benchmark datasets. The proposed method performs better than the other state-of-the-art methods in graph classification and graph regression tasks. Notably, It achieves 2.7% improvement on DD dataset for graph classification and 7.1% absolute improvement on ZINC dataset for graph regression.
Unraveling the Temporal Dynamics of the Unet in Diffusion Models
Authors: Vidya Prasad, Chen Zhu-Tian, Anna Vilanova, Hanspeter Pfister, Nicola Pezzotti, Hendrik Strobelt
Date: 2023-12-17 00:00:00
Tasks: Denoising

Diffusion models have garnered significant attention since they can effectively learn complex multivariate Gaussian distributions, resulting in diverse, high-quality outcomes. They introduce Gaussian noise into training data and reconstruct the original data iteratively. Central to this iterative process is a single Unet, adapting across time steps to facilitate generation. Recent work revealed the presence of composition and denoising phases in this generation process, raising questions about the Unets' varying roles. Our study dives into the dynamic behavior of Unets within denoising diffusion probabilistic models (DDPM), focusing on (de)convolutional blocks and skip connections across time steps. We propose an analytical method to systematically assess the impact of time steps and core Unet components on the final output. This method eliminates components to study causal relations and investigate their influence on output changes. The main purpose is to understand the temporal dynamics and identify potential shortcuts during inference. Our findings provide valuable insights into the various generation phases during inference and shed light on the Unets' usage patterns across these phases. Leveraging these insights, we identify redundancies in GLIDE (an improved DDPM) and improve inference time by ~27% with minimal degradation in output quality. Our ultimate goal is to guide more informed optimization strategies for inference and influence new model designs.
Dynamic Syntax Mapping: A New Approach to Unsupervised Syntax Parsing
Authors: Buvarp Gohsh, Woods Ali, Anders Michael
Date: 2023-12-18 00:00:00

The intricate hierarchical structure of syntax is fundamental to the intricate and systematic nature of human language. This study investigates the premise that language models, specifically their attention distributions, can encapsulate syntactic dependencies. We introduce Dynamic Syntax Mapping (DSM), an innovative approach for the agnostic induction of these structures. Our method diverges from traditional syntax models which rely on predefined annotation schemata. Instead, we focus on a core characteristic inherent in dependency relations: syntactic substitutability. This concept refers to the interchangeability of words within the same syntactic category at either end of a dependency. By leveraging this property, we generate a collection of syntactically invariant sentences, which serve as the foundation for our parsing framework. Our findings reveal that the use of an increasing array of substitutions notably enhances parsing precision on natural language data. Specifically, in the context of long-distance subject-verb agreement, DSM exhibits a remarkable advancement over prior methodologies. Furthermore, DSM's adaptability is demonstrated through its successful application in varied parsing scenarios, underscoring its broad applicability.
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Authors: Meiying Zhang, Huan Zhao, Sheldon Ebron, Ruitao Xie, Kan Yang
Date: 2023-12-05 00:00:00
Tasks: Fairness, Federated Learning, Scheduling

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the `integrated' dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid.
GraphDIVE: Graph Classification by Mixture of Diverse Experts
Authors: Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
Date: 2021-03-21 00:00:00
Proceeding: journal 2021 3
Tasks: Classification, Graph Classification, Graph Neural Network

Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world datasets. Despite their success, most of current GNN models largely overlook the important setting of imbalanced class distribution, which typically results in prediction bias towards majority classes. To alleviate the prediction bias, we propose to leverage semantic structure of dataset based on the distribution of node embedding. Specifically, we present GraphDIVE, a general framework leveraging mixture of diverse experts (i.e., graph classifiers) for imbalanced graph classification. With a divide-and-conquer principle, GraphDIVE employs a gating network to partition an imbalanced graph dataset into several subsets. Then each expert network is trained based on its corresponding subset. Experiments on real-world imbalanced graph datasets demonstrate the effectiveness of GraphDIVE.
Object-Centric Slot Diffusion
Authors: Jindong Jiang, Fei Deng, Gautam Singh, Sungjin Ahn
Date: 2023-03-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Image Generation, Image Segmentation, Object, Semantic Segmentation

The recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes. However, despite the high expressiveness of diffusion models in image generation, their integration into object-centric learning remains largely unexplored in this domain. In this paper, we explore the feasibility and potential of integrating diffusion models into object-centric learning and investigate the pros and cons of this approach. We introduce Latent Slot Diffusion (LSD), a novel model that serves dual purposes: it is the first object-centric learning model to replace conventional slot decoders with a latent diffusion model conditioned on object slots, and it is also the first unsupervised compositional conditional diffusion model that operates without the need for supervised annotations like text. Through experiments on various object-centric tasks, including the first application of the FFHQ dataset in this field, we demonstrate that LSD significantly outperforms state-of-the-art transformer-based decoders, particularly in more complex scenes, and exhibits superior unsupervised compositional generation quality. In addition, we conduct a preliminary investigation into the integration of pre-trained diffusion models in LSD and demonstrate its effectiveness in real-world image segmentation and generation. Project page is available at https://latentslotdiffusion.github.io
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Authors: Yiyou Sun, Zhenmei Shi, Yixuan Li
Date: 2023-11-06 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Clustering, Open-World Semi-Supervised Learning, Representation Learning

Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes. Despite its importance, there is a lack of theoretical foundations for this problem. This paper bridges the gap by formalizing a graph-theoretic framework tailored for the open-world setting, where the clustering can be theoretically characterized by graph factorization. Our graph-theoretic framework illuminates practical algorithms and provides guarantees. In particular, based on our graph formulation, we apply the algorithm called Spectral Open-world Representation Learning (SORL), and show that minimizing our loss is equivalent to performing spectral decomposition on the graph. Such equivalence allows us to derive a provable error bound on the clustering performance for both known and novel classes, and analyze rigorously when labeled data helps. Empirically, SORL can match or outperform several strong baselines on common benchmark datasets, which is appealing for practical usage while enjoying theoretical guarantees.
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
Authors: Sarah Rastegar, Hazel Doughty, Cees G. M. Snoek
Date: 2023-10-30 00:00:00
Proceeding: NeurIPS 2023 11

In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persists: what exactly delineates a category? In this paper, we conceptualize a category through the lens of optimization, viewing it as an optimal solution to a well-defined problem. Harnessing this unique conceptualization, we propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time. A salient feature of our approach is the assignment of minimum length category codes to individual data instances, which encapsulates the implicit category hierarchy prevalent in real-world datasets. This mechanism affords us enhanced control over category granularity, thereby equipping our model to handle fine-grained categories adeptly. Experimental evaluations, bolstered by state-of-the-art benchmark comparisons, testify to the efficacy of our solution in managing unknown categories at test time. Furthermore, we fortify our proposition with a theoretical foundation, providing proof of its optimality. Our code is available at https://github.com/SarahRastegar/InfoSieve.
Towards Distribution-Agnostic Generalized Category Discovery
Authors: Jianhong Bai, Zuozhu Liu, Hualiang Wang, Ruizhe Chen, Lianrui Mu, Xiaomeng Li, Joey Tianyi Zhou, Yang Feng, Jian Wu, Haoji Hu
Date: 2023-10-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Contrastive Learning, Transfer Learning

Data imbalance and open-ended distribution are two intrinsic characteristics of the real visual world. Though encouraging progress has been made in tackling each challenge separately, few works dedicated to combining them towards real-world scenarios. While several previous works have focused on classifying close-set samples and detecting open-set samples during testing, it's still essential to be able to classify unknown subjects as human beings. In this paper, we formally define a more realistic task as distribution-agnostic generalized category discovery (DA-GCD): generating fine-grained predictions for both close- and open-set classes in a long-tailed open-world setting. To tackle the challenging problem, we propose a Self-Balanced Co-Advice contrastive framework (BaCon), which consists of a contrastive-learning branch and a pseudo-labeling branch, working collaboratively to provide interactive supervision to resolve the DA-GCD task. In particular, the contrastive-learning branch provides reliable distribution estimation to regularize the predictions of the pseudo-labeling branch, which in turn guides contrastive learning through self-balanced knowledge transfer and a proposed novel contrastive loss. We compare BaCon with state-of-the-art methods from two closely related fields: imbalanced semi-supervised learning and generalized category discovery. The effectiveness of BaCon is demonstrated with superior performance over all baselines and comprehensive analysis across various datasets. Our code is publicly available.