Ax Xiaoguang Guo, Zehong Wang, Ziming Li, Shawn Spitzel, Soonwoo Kwon, Tianyi Ma, Yanfang Ye, Chuxu Zhang 5/8/2026

On the Safety of Graph Representation Learning

arXiv paper on safety and robustness of graph representation learning under distribution shifts. ML robustness research.

Ax Vinit Ranjan, Jisun Park, Bartolomeo Stellato 5/8/2026

Distributionally-Robust Learning to Optimize

arXiv paper on distributionally robust hyperparameter optimization for convex methods. Theoretical optimization research.

Ax Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang, Aviral Kumar, Graham Neubig 5/8/2026

Recursive Agent Optimization

Reinforcement learning approach for training recursive agents that spawn sub-tasks to solve longer context problems.

Ax Philippe Hansen-Estruch, Jiahui Chen, Vivek Ramanujan, Orr Zohar, Yan Ping, Animesh Sinha, Markos Georgopoulos, Edgar Schoenfeld, Ji Hou, Felix Juefei-Xu, Sriram Vishwanath, Ali Thabet 5/8/2026

ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters

ViTok-v2: Scaled Vision Transformer autoencoders to 5B parameters for image tokenization at native resolution.

Ax Siyan Liu, Yi Chang, Manli Cheng, Qinglong Tian, Pengfei Li 5/8/2026

In-Context Positive-Unlabeled Learning

Pretrained transformer (PUICL) for in-context positive-unlabeled learning enabling quick binary classification with only positive labels available.

Ax Jannis Chemseddine, Gregor Kornhardt, Gabriele Steidl 5/8/2026

Spherical Flows for Sampling Categorical Data

Generative model for discrete sequences using spherical flows and von Mises-Fisher distributions with closed-form conditional scores.

Ax Lena Helgerth, Andreas Christmann 5/8/2026

Ratio-based Loss Functions

Survey of ratio-based loss functions for supervised and unsupervised learning algorithms.