8 papers
Soft-Constrained Optimization of Latent Space in Variational Autoencoders
Ye Shi
The usefulness of a variational autoencoder (VAE) depends on two properties of its latent space that are hard to obtain together: high encoding capacity in the individual latent va…
A Multi-stage Constrained Optimization Framework for Data-driven Problems
Ye Shi
Variational autoencoders (VAEs) transform high-dimensional, often noisy data into a compact latent representation, making downstream optimization more tractable. Three challenges p…
Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent
Zeyuan Wang, Da Li, Yulin Chen +6
Combining entropy regularisation with mirror descent in online off-policy reinforcement learning requires expressive policies with efficient sampling and explicit entropy control.…
Distributional Reinforcement Learning with Diffusion Bridge Critics
Shutong Ding, Yimiao Zhou, Ke Hu +5
Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in t…
Fine-grained Classification of A Million Life Trajectories from Wikipedia
Zhaoyang Liu, Xiaocong Du, Yixi Zhou +2
Life trajectories of notable people convey essential messages for human dynamics research. These trajectories consist of (\textit{person, time, location, activity type}) tuples rec…
One-Step Generative Policies with Q-Learning: A Reformulation of MeanFlow
Zeyuan Wang, Da Li, Yulin Chen +4
We introduce a one-step generative policy for offline reinforcement learning that maps noise directly to actions via a residual reformulation of MeanFlow, making it compatible with…
Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement
Shutong Ding, Yimiao Zhou, Ke Hu +4
Recent advances in diffusion models show promising potential to accelerate nonconvex problem solving by leveraging their multimodality. However, most existing diffusion-based optim…
Uniform Transformation: Refining Latent Representation in Variational Autoencoders
Ye Shi, C. S. George Lee
Irregular distribution in latent space causes posterior collapse, misalignment between posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs). In this pap…