Generalizable Neural Fields as Partially Observed Neural Processes

Neural fields, which represent signals as a function parameterized by a neural network, are a promising alternative to traditional discrete vector or grid-based representations. Compared to discrete representations, neural representations both scale well with increasing resolution, are continuous, and can be many-times differentiable. However, given a dataset of signals that we would like to represent, having to optimize a separate neural field for each signal is inefficient, and cannot capitalize on shared information or structures among signals. Existing generalization methods view this as a meta-learning problem and employ gradient-based meta-learning to learn an initialization which is then fine-tuned with test-time optimization, or learn hypernetworks to produce the weights of a neural field. We instead propose a new paradigm that views the large-scale training of neural representations as a part of a partially-observed neural process framework, and leverage neural process algorithms to solve this task. We demonstrate that this approach outperforms both state-of-the-art gradient-based meta-learning approaches and hypernetwork approaches.Comment: To appear ICCV 202

New Chinese Music in New York City: From Revival to Musical Transnationalism

The Pulitzer Prize (2011, Zhou Long’s Madame White Snake), a Metropolitan opera commission (Tan Dun’s The First Emperor, premiered 2006), and the Ives Living Award (Chen Yi, 2001) are just some of the high-profile awards and commission bestowed upon Chinese émigré composers who have studied and built their professional reputation in New York City. The works of Chinese composers constitute what I call “new Chinese music,” which I argue has played a defining role in New York City’s cultural landscape and in the development of Western art music in general. The influence of Chinese composers and their works is twofold: Chinese composers have participated in the various musico-stylistic movements in New York City in the latter twentieth century and have contributed to the City as a site for musical transnationalism. Furthermore, new Chinese music has led to the change in public perception of Chinese musicians and composers from voiceless exotics to agents of innovation. This dissertation frames the phenomenon of the rise of the first two generations of Chinese composers in New York City as a revival movement. The first chapter examines Chou Wen-Chung’s (1923–2019) execution of literary governance (a concept borrowed from Jing Tsu). As the patriarch of the “New York School” of new Chinese music, Chou devoted himself to the revival of the Chinese scholarly (wenren) tradition. The second chapter discusses the revival of Tang-Dynasty cosmopolitanism in the works of Zhou Long, a student of Chou’s. The revival of Chinese nationalism in musical memorials by Bright Sheng and Chen Yi is the topic of the third chapter, while the fourth chapter delves into the revival of shamanic ritual theatre in Tan Dun’s musical activities in New York City. Ultimately, the various revival movements form the foundation of musical transnationalism, which I define as a mode of musical production informed by transnational consciousness, and an aesthetics characterized by flexible accumulation and combination of musical resources

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry

From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare. In healthcare, Dranove et al. (2003) showed that publishing surgery mortality metrics actually harmed the welfare of sicker patients by increasing provider selection behavior. We analyze the incentive misalignments that arise from such average treated outcome metrics, and show that the incentives driving treatment decisions would align with maximizing total patient welfare if the metrics (i) accounted for counterfactual untreated outcomes and (ii) considered total welfare instead of averaging over treated patients. Operationalizing this, we show how counterfactual metrics can be modified to behave reasonably in patient-facing ranking systems. Extending to realistic settings when providers observe more about patients than the regulatory agencies do, we bound the decay in performance by the degree of information asymmetry between principal and agent. In doing so, our model connects principal-agent information asymmetry with unobserved heterogeneity in causal inference