2 citations · 2 across the 5 of their papers we have counts for
5 papers
Understanding Behavior Cloning with Action Quantization
Haoqun Cao, Tengyang Xie
Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and generative models. Auto…
Score Matching for Estimating Finite Point Processes
Haoqun Cao, Yixuan Zhang, Feng Zhou
Score matching estimators have garnered significant attention in recent years because they eliminate the need to compute normalizing constants, thereby mitigating the computational…
Is Score Matching Suitable for Estimating Point Processes?
Haoqun Cao, Zizhuo Meng, Tianjun Ke +1
Score matching estimators have gained widespread attention in recent years partly because they are free from calculating the integral of normalizing constant, thereby addressing th…
Accelerating Convergence in Bayesian Few-Shot Classification
Tianjun Ke, Haoqun Cao, Feng Zhou
Bayesian few-shot classification has been a focal point in the field of few-shot learning. This paper seamlessly integrates mirror descent-based variational inference into Gaussian…
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
Tianjun Ke, Haoqun Cao, Zenan Ling +1
Meta-learning has demonstrated promising results in few-shot classification (FSC) by learning to solve new problems using prior knowledge. Bayesian methods are effective at charact…