Class Type: Spring 2024
SILO: Generalized Tensor Decompositions: Algorithms and Applications
Abstract: Tensor decompositions generalize matrix decompositions from matrix data (i.e., 2-D arrays) to tensor data (i.e., N-D arrays) and are a fundamental technique for uncovering low-dimensional structure in high-dimensional datasets, with applications across all of science and engineering. Conventional tensor decompositions seek low-rank tensors that best fit the data with …
SILO: Discovering underlying dynamics in time series of networks
Abstract: Analyzing changes in network evolution is central to statistical network inference, as underscored by recent challenges of predicting and distinguishing pandemic-induced transformations in organizational and communication networks. We consider a joint network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection …
SILO: Towards Plurality: Foundations for Learning from Diverse Human Preferences
Abstract: Large pre-trained models trained on internet-scale data are often not ready for safe deployment out of the box. They are heavily fine-tuned and aligned using large quantities of human preference data. When we want to align an AI/ML model to human preference or values, it is worthwhile to ask …
SILO: Robust and minimax estimation in a two-groups model
Abstract: The advent of large scale inference has spurred reexamination of conventional statistical thinking. In a series of highly original articles Efron showed in some examples that the ensemble of the null distributed test statistics grossly deviated from the theoretical null distribution and Efron persuasively illustrated the danger in assuming …
SILO: Reinforcement Learning with Robustness and Safety Guarantees
Abstract: Reinforcement Learning (RL) is the class of machine learning that addresses the problem of learning to control unknown dynamical systems. RL has achieved remarkable success recently in applications like playing games and robotics. However, most of these successes are limited to very structured or simulated environments. When applied to …