5 citations · 5 across the 8 of their papers we have counts for
8 papers
Warm-Start Flow Matching for Guaranteed Fast Text/Image Generation
Minyoung Kim
Current auto-regressive (AR) LLMs, diffusion-based text/image generative models, and recent flow matching (FM) algorithms are capable of generating premium quality text/image sampl…
Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning
Taehoon Kim, Henry Gouk, Minyoung Kim +1
Certifying the IID generalisation ability of deep networks is the first of many requirements for trusting AI in high-stakes applications from medicine to security. However, when in…
Weight Space Learning for Certifiable Few-shot Transfer Learning
Fady Rezk, Royson Lee, Henry Gouk +2
In contemporary deep learning, a prevalent and effective workflow for solving low-data problems is adapting powerful pre-trained foundation models (FMs) to new tasks via parameter-…
FedPEFT: Federated Learning to Personalize PEFT for Multilingual LLMs
Royson Lee, Minyoung Kim, Fady Rezk +3
Federated learning (FL) has enabled the training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especially on low-resource languages.…
A Bayesian Approach to Data Point Selection
Xinnuo Xu, Minyoung Kim, Royson Lee +2
Data point selection (DPS) is becoming a critical topic in deep learning due to the ease of acquiring uncurated training data compared to the difficulty of obtaining curated or pro…
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning
Minyoung Kim, Timothy M. Hospedales
We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learn…
FedL2P: Federated Learning to Personalize
Royson Lee, Minyoung Kim, Da Li +4
Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those comm…
BayesDLL: Bayesian Deep Learning Library
Minyoung Kim, Timothy Hospedales
We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our library implements mainstream approximate Bayesian inference algorithms: variational…