Tianyang Li — accepted AI-conference papers

2026

ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning

Yichao Liang, Thanh Dat Nguyen, Cambridge Yang, Tianyang Li, Joshua B. Tenenbaum, Carl Edward Rasmussen +4

ICLR 2026poster

Long‑horizon embodied planning is challenging because the world does not only change through an agent’s actions: exogenous processes (e.g., water heating, dominoes cascading) unfold concurrently with the agent's actions. We propose a framework for abstract world models that jointly learns (i) symbol…

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2022

Learning Deep Implicit Functions for 3D Shapes With Dynamic Code Clouds

Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, Zhizhong Han

CVPR 2022poster

Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape representation. To capture geometry details, current methods usually learn DIF using local latent codes, which discretize the space into a regular 3D grid (or octree) and store local codes in grid points (or octree nodes). G…

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2020

High Dimensional Robust Sparse Regression

Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis

AISTATS 2020poster

We provide a novel – and to the best of our knowledge, the first – algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or response variables. Our algorithm recovers the true sparse parameters with sub-linear sample complexity,in the presence of a…

Cited by 83 Source ↗PDF ↗Scholar ↗

2020

Point Cloud Completion by Skip-Attention Network With Hierarchical Folding

Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu

CVPR 2020poster

Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffer…

Cited by 325 PDF ↗Scholar ↗

2017

Minimax Gaussian Classification & Clustering

Tianyang Li, Xinyang Yi, Constantine Carmanis, Pradeep Ravikumar

AISTATS 2017poster

We present minimax bounds for classification and clustering error in the setting where covariates are drawn from a mixture of two isotropic Gaussian distributions. Here, we define clustering error in a discriminative fashion, demonstrating fundamental connections between classification (supervised)…

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2015

Fast Classification Rates for High-dimensional Gaussian Generative Models

Tianyang Li, Adarsh Prasad, Pradeep K Ravikumar

NeurIPS 2015poster

We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions. We focus on the setting where the covariance matrices for the two conditional distributions are the same. The corresponding generative model…

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