On statistics, computation and scalability
How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of statistics. With the goal of identifying "time-data tradeoffs," we investigate some of the statistical consequences of computational perspectives on scability, in particular divide-and-conquer methodology and hierarchies of convex relaxations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Permutation-Free High-Order Interaction Tests
Kernel-based hypothesis tests offer a flexible, non-parametric tool to detect high-order interactions in multivariate data, beyond pairwise relationships. Yet the scalability of such tests is limited by the computational…
Causal Discoveryfeature selectionCoordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning. Nonconvex penalized least squares have been attracted a lot of attentions since they enjoy…
PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality
Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with ot…
Federated LearningOn Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis fir…
Offline RLreinforcement-learningReinforcement LearningReinforcement Learning (RL)A General Framework for Learning from Weak Supervision
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical d…
Weakly-supervised Learning