Kang Du

Kang Du, Zhihao Liang, Yulin Shen et al.

Gaussian Splatting (GS) has emerged as an effective representation for photorealistic rendering, but the underlying geometry, material, and lighting remain entangled, hindering scene editing. Existing GS-based methods struggle to disentangle these components under non-Lambertian conditions, especially in the presence of specularities and shadows. We propose \textbf{GS-ID}, an end-to-end framework for illumination decomposition that integrates adaptive light aggregation with diffusion-based material priors. In addition to a learnable environment map for ambient illumination, we model spatially-varying local lighting using anisotropic spherical Gaussian mixtures (SGMs) that are jointly optimized with scene content. To better capture cast shadows, we associate each splat with a learnable unit vector that encodes shadow directions from multiple light sources, further improving material and lighting estimation. By combining SGMs with intrinsic priors from diffusion models, GS-ID significantly reduces ambiguity in light-material-geometry interactions and achieves state-of-the-art performance on inverse rendering and relighting benchmarks. Experiments also demonstrate the effectiveness of GS-ID for downstream applications such as relighting and scene composition.

Austin Goddard, Kang Du, Yu Xiang

Making predictions in an unseen environment given data from multiple training environments is a challenging task. We approach this problem from an invariance perspective, focusing on binary classification to shed light on general nonlinear data generation mechanisms. We identify a unique form of invariance that exists solely in a binary setting that allows us to train models invariant over environments. We provide sufficient conditions for such invariance and show it is robust even when environmental conditions vary greatly. Our formulation admits a causal interpretation, allowing us to compare it with various frameworks. Finally, we propose a heuristic prediction method and conduct experiments using real and synthetic datasets.

Kang Du, Yu Xiang

Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time series remains a challenging task. In this work, we propose a new class of restricted SCM, via a time-varying filter and stationary noise, and exploit the asymmetry from nonstationarity for causal identification in both bivariate and network settings. We propose efficient procedures by leveraging powerful estimates of the bivariate evolutionary spectra for slowly varying processes. Various synthetic and real datasets that involve high-order and non-smooth filters are evaluated to demonstrate the effectiveness of our proposed methodology.