Search Results for author:
Found 5 papers, 2 papers with code
Minimax Regret Learning for Data with Heterogeneous Subgroups
Given the observed groups of data, we develop a min-max-regret (MMR) learning framework for general supervised learning, which targets to minimize the worst-group regret.
KL-divergence Based Deep Learning for Discrete Time Model
Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis.
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms.
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown.
A Flexible Generative Framework for Graph-based Semi-supervised Learning
In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure.
