Mission Critical — Satellite Data is a Distinct Modality in Machine Learning
The paper argues that satellite data could create a significant shift in machine learning, suggesting a need…
Improving importance estimation in covariate shift for providing accurate prediction error
This article presents research on how to improve the accuracy of predictions in Machine Learning when there…
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
The research paper presents a new methodology for causal inference that integrates large language models (LLM) into…
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
The study proposes Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) for building uncertainty-aware self-attention in transformers. The…
Multi-level protein pre-training with Vabs-Net
The study presents Vabs-Net, a new approach in protein modeling that considers both residue and atom levels….
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
The research delves into the challenge of sample-based inference (SBI) for Bayesian neural networks (BNNs) due to…
Mapping the Multiverse of Latent Representations
In response to concerns over reliability and robustness in machine learning, the authors propose a multiverse analysis…
Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence
This article presents a unified framework to address the challenging convergence analysis under non-convex conditions in stochastic…
Adaptive Optimization for Prediction with Missing Data
The paper presents a new approach to predicting with missing data by viewing it as a two-stage…
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
The article discusses the development of a Secure-Aggregation (SecAgg) algorithm for residential short-term load forecasting, which maintains…