no code implementations • 23 Feb 2024 • Kaihong Zhang, Heqi Yin, Feng Liang, Jingbo Liu
As a consequence, this yields an $\widetilde{O}\left(n^{-1/2} t^{-\frac{d}{4}}\right)$ upper bound for the total variation error of the distribution of the sample generated by the diffusion model under a mere sub-Gaussian assumption.
no code implementations • 17 Sep 2023 • Leighton P. Barnes, Alex Dytso, Jingbo Liu, H. Vincent Poor
Consider the problem of estimating a random variable $X$ from noisy observations $Y = X+ Z$, where $Z$ is standard normal, under the $L^1$ fidelity criterion.
no code implementations • ICCV 2023 • Jingyang Zhang, Yao Yao, Shiwei Li, Jingbo Liu, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan
We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images.
1 code implementation • 14 Mar 2022 • Yao Yao, Jingyang Zhang, Jingbo Liu, Yihang Qu, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan
We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry.
no code implementations • NeurIPS 2021 • Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran
In contrast, when the MDP transition structure is known to the learner such as in the case of simulators, we demonstrate fundamental differences compared to the tabular setting in terms of the performance of an optimal algorithm, Mimic-MD (Rajaraman et al. (2020)) when extended to the function approximation setting.
no code implementations • 10 Nov 2020 • Paxton Turner, Jingbo Liu, Philippe Rigollet
We study the problem of space and time efficient evaluation of a nonparametric estimator that approximates an unknown density.
no code implementations • 10 Nov 2020 • Paxton Turner, Jingbo Liu, Philippe Rigollet
Coresets have emerged as a powerful tool to summarize data by selecting a small subset of the original observations while retaining most of its information.
no code implementations • NeurIPS 2019 • Jingbo Liu, Philippe Rigollet
We introduce a simple functional called effective signal deficiency (ESD) of the covariance matrix $\Sigma$ that predicts consistency of various variable selection methods.
no code implementations • 24 May 2019 • Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
The analysis of Belief Propagation and other algorithms for the {\em reconstruction problem} plays a key role in the analysis of community detection in inference on graphs, phylogenetic reconstruction in bioinformatics, and the cavity method in statistical physics.
no code implementations • 25 Jan 2019 • Uri Hadar, Jingbo Liu, Yury Polyanskiy, Ofer Shayevitz
Our results also imply an $\Omega(n)$ lower bound on the information complexity of the Gap-Hamming problem, for which we show a direct information-theoretic proof.
no code implementations • ICCV 2015 • Jingbo Liu, Jinglu Wang, Tian Fang, Chiew-Lan Tai, Long Quan
In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments.