5 citations · 9 across the 7 of their papers we have counts for
7 papers
Users Mispredict Their Own Preferences for AI Writing Assistance
Vivian Lai, Zana Buçinca, Nil-Jana Akpinar +5
Proactive AI writing assistants need to predict when users want drafting help, yet we lack empirical understanding of what drives preferences. Through a factorial vignette study wi…
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Namjoon Suh, Yuning Yang, Din-Yin Hsieh +4
We present TimeAutoDiff, a unified latent-diffusion framework for four fundamental time-series tasks: unconditional generation, missing-data imputation, forecasting, and time-varyi…
Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data
Lan Tao, Shirong Xu, Chi-Hua Wang +2
With the proliferation of generative AI and the increasing volume of generative data (also called as synthetic data), assessing the fidelity of generative data has become a critica…
Neural Operators for Nonlinear Functionals on RKHS
Tian-Yi Zhou, Namjoon Suh, Guang Cheng +1
Motivated by the abundance of functional data, such as time series and images, we study the approximation and statistical learning of nonlinear functionals defined on reproducing k…
Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
Yue Xing, Xiaofeng Lin, Chenheng Xu +3
Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023train…
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
Namjoon Suh, Guang Cheng
In this article, we review the literature on statistical theories of neural networks from three perspectives: approximation, training dynamics and generative models. In the first p…
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh +2
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesi…