Statistical Learning
Structured Semidefinite Programming for Recovering Structured Preconditioners Arun Jambulapati Simons Institute
We develop a general framework for finding approximately-optimal preconditioners for solving linear systems.
Scalarization for Multi-Task and Multi-Domain Learning at Scale Amélie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi Qualcomm AI Research Amsterdam, The Netherlands
It also enables potential positive knowledge transfer across tasks/domains, leading to improved accuracy and data-efficient training.
Flow Matching for Scalable Simulation-Based Inference
Figure 1: Comparison of network architectures (left) and flow trajectories (right). Discrete flows (NPE, top) require a specialized architecture for the density estimator. Continuous flows (FMPE, bottom) are based on a vector field parametrized with an unconstrained architecture.
Flow Matching for Scalable Simulation-Based Inference
Figure 1: Comparison of network architectures (left) and flow trajectories (right). Discrete flows (NPE, top) require a specialized architecture for the density estimator. Continuous flows (FMPE, bottom) are based on a vector field parametrized with an unconstrained architecture.
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
However, it is unclear how the style-independence property benefits ACL-learned robust representations.
Expert load matters: operating networks at high accuracy and low manual effort
In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the decision should be delegated to human experts.