Author

Tim G. J. Rudner

University of Toronto

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#machine learning Preprint Mar 2026

Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data

This work proposes MedCertAIn, a predictive uncertainty framework that leverages multimodal clinical data to improve model performance and reliability for in-hospital mortality risk prediction as an indicator of patient deterioration and highlights the promise of data-driven priors in advancing robust, uncertainty-awar...

L. J. L. López, Tim G. J. Rudner, Farah E. Shamout · 0 citations

#machine learning Preprint Sep 2026

Entangled Representations Amplify Collateral Damage in Unlearning

By repurposing Selective Gradient Masking, a suite of six 254M-parameter language models on English Wikipedia with graded levels of disentanglement between biology and non-biology knowledge are trained, it is found that more disentangled models consistently achieve better retain-forget trade-offs.

Evžen Wybitul, Tim G. J. Rudner, C. D. de Witt · 0 citations

#machine learning Preprint Sep 2026

A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization

Discrete visual tokenization, predominantly driven by vector, scalar, and product quantization, lacks a unified conceptual framework for understanding quantization tradeoffs. In this paper, we propose a unified rate--distortion perspective on modern discrete visual tokenization. By viewing quantization as lossy compres...

Xianghong Fang, Wenlong Mou, Yuan Yuan et al. · 0 citations

Jul 2026

VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers

VQ-Transplant democratizes quantization research by enabling resource-efficient integration of novel VQ techniques while matching industry-level reconstruction performance.

Xianghong Fang, Yuan Yuan, Dehan Kong et al. · arXiv.org · 1 citation

#machine learning May 2026

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models

Relay introduces a differentiable per-token channel that passes information between forward passes and is trained via truncated backpropagation through time (BPTT), demonstrating that state-of-the-art DLMs can be explicitly trained to relay latent information forward across decoding steps, advancing the performance-lat...

Benjamin Rozonoyer, Jacopo Minniti, Dhruvesh Patel et al. · arXiv.org · 0 citations