Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling

Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images. How to use it to model the distributions of both continuous real-valued data and categorical data has been well studied in recently proposed diffusion models. However, it is found in this paper to have limited ability in modeling some other types of data, such as count and non-negative continuous data, that are often highly sparse, skewed, heavy-tailed, and/or overdispersed. To this end, we propose learning to jump as a general recipe for generative modeling of various types of data. Using a forward count thinning process to construct learning objectives to train a deep neural network, it employs a reverse count thickening process to iteratively refine its generation through that network. We demonstrate when learning to jump is expected to perform comparably to learning to denoise, and when it is expected to perform better. For example, learning to jump is recommended when the training data is non-negative and exhibits strong sparsity, skewness, heavy-tailedness, and/or heterogeneity.Comment: ICML 202

Boardroom Networks and Political Ideology in Shaping Firms’ Environmental Strategies

This thesis aims to study how firms’ environmental strategies are shaped with a focus on board directors. For this study, I compile the Database on Director Network, Toxic Releases and Political Activities and use toxic releases from the US Environmental Protection Agency (EPA)’s Toxics Release Inventory (TRI) Program as the key environmental performance indicator. By using the database compiled, I first study how director networks are formed. My findings show that firms are likely to appoint influential directors with good environmental performances. Further, directors with environmental characteristics similar to the other board members or their firm are more likely to be chosen as board members. I also show that boards of directors with good environmental performances or in which directors have diverse environmental performance backgrounds will improve firms’ environmental quality. Then I examine the effect of political ideology in shaping firms’ environmental strategies. My results show that although political ideology is less significant in determining a firm’s environmental strategy than board directors’ previous environmental performance records, Republican-leaning firms have poorer environmental performances. To address the endogenous concerns, I also follow a similar approach to study network formation with the inclusion of politics-related measures and find firms also tend to appoint directors who share similar political ideologies. These findings help to explain the political polarization in the private sector from a network formation aspect and provide further evidence of the role of political ideology in shaping environmental strategies

On the Trustworthiness Landscape of State-of-the-art Generative Models: A Comprehensive Survey

Diffusion models and large language models have emerged as leading-edge generative models and have sparked a revolutionary impact on various aspects of human life. However, the practical implementation of these models has also exposed inherent risks, highlighting their dual nature and raising concerns regarding their trustworthiness. Despite the abundance of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, This paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: privacy, security, fairness, and responsibility. In this way, we construct an extensive map outlining the trustworthiness of these models, while also providing practical recommendations and identifying future directions. These efforts are crucial for promoting the trustworthy deployment of these models, ultimately benefiting society as a whole.Comment: draft versio