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Found 6 papers, 3 papers with code
Preference Alignment with Flow Matching
We present Preference Flow Matching (PFM), a new framework for preference-based reinforcement learning (PbRL) that streamlines the integration of preferences into an arbitrary class of pre-trained models.
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist.
Meta-learning Amidst Heterogeneity and Ambiguity
Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution.
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
In the machine learning algorithms, the choice of the hyperparameter is often an art more than a science, requiring labor-intensive search with expert experience.
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
Federated learning has emerged as an innovative paradigm of collaborative machine learning.
Efficient Model for Image Classification With Regularization Tricks
Our final score is 0. 0054, which represents 370x improvements over the baseline for the CIFAR100 dataset.
