SAGE-ICP: Semantic Information-Assisted ICP

1 code implementation • 11 Oct 2023 • Jiaming Cui, Jiming Chen, Liang Li

Robust and accurate pose estimation in unknown environments is an essential part of robotic applications.

Pose Estimation

DF2: Distribution-Free Decision-Focused Learning

no code implementations • 11 Aug 2023 • Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang

However, existing end-to-end DFL methods are hindered by three significant bottlenecks: model mismatch error, sample average approximation error, and gradient approximation error.

Autoregressive Diffusion Model for Graph Generation

1 code implementation • 17 Jul 2023 • Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang

However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space.

Denoising Graph Generation

End-to-End Stochastic Optimization with Energy-Based Model

1 code implementation • 25 Nov 2022 • Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.

Scheduling Stochastic Optimization

EINNs: Epidemiologically-informed Neural Networks

1 code implementation • 21 Feb 2022 • Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash

We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information.

Inductive Bias