5 papers · 1 filter
Divergence Results and Convergence of a Variance Reduced Version of ADAM
Ruiqi Wang, Diego Klabjan
Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications…
Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis
Adam Sandler, Diego Klabjan, Yuan Luo
We analyze large, multi-dimensional, sparse counting data sets, finding unsupervised groups to provide unique insights into genetic data. We create gene and biological pathway grou…
Non-Convex Optimization with Spectral Radius Regularization
Adam Sandler, Diego Klabjan, Yuan Luo
We develop regularization methods to find flat minima while training deep neural networks. These minima generalize better than sharp minima, yielding models outperforming baselines…
Multi-Layer Attention-Based Explainability via Transformers for Tabular Data
Andrea Treviño Gavito, Diego Klabjan, Jean Utke
We propose a graph-oriented attention-based explainability method for tabular data. Tasks involving tabular data have been solved mostly using traditional tree-based machine learni…
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms
Mengfan Xu, Diego Klabjan
We study the challenging exploration incentive problem in both bandit and reinforcement learning, where the rewards are scale-free and potentially unbounded, driven by real-world s…