Zhihan Yang

I'm a first-year PhD student at Cornell CS, where I focus on machine learning and specifically generative models.

I have a BA in Mathematics and a BA in Statistics from Carleton College. During those years, I was fortunate to work with Seth Cooper on generative models, Anna Rafferty on bandits, Christopher Amato on deep reinforcement learning, and Adam Loy on MCMC algorithms.

I received the CRA Outstanding Undergraduate Researcher Award.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

Research

Currently, I'm interested in developing principled, controllable and efficient generative models for various modalities.

Esoteric Language Models

We are the first to propose KV-caching for masked diffusion language models.

Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

We introduce a class of block diffusion language models that interpolate between discrete denoising diffusion and autoregressive models.

Adversarial Bandits for Drawing Generalizable Conclusions in Non-Adversarial Experiments: An Empirical Study

We empirically analyse how adversarial bandit algorithms can enhance the reliability of conclusions drawn from large-scale educational experiments.

Hierarchical Reinforcement Learning under Mixed Observability

We present a hierarchical RL framework that handles mixed observability settings, enabling modular policies that scale to complex robotic tasks.

Recurrent Off-policy Baselines for Memory-based Continuous Control

We establish strong recurrent off-policy baselines for tasks requiring long-term memory.

Game Level Clustering and Generation using Gaussian Mixture VAEs

We leverage the Gaussian-Mixture VAE framework to cluster game levels in an unsupervised manner and synthesize novel game levels from the learned clusters.

Other Projects

Upcoming.

Design and source code from Jon Barron's website