5 papers
Non-Asymptotic Analysis of Classical Spectrum Estimators for -mixing Time-series Data with Estimated Means
Yuping Zheng, Andrew Lamperski
Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estima…
Non-Asymptotic Error Bounds for Causally Conditioned Directed Information Rates of Gaussian Sequences
Yuping Zheng, Andrew Lamperski
Directed information and its causally conditioned variations are often used to measure causal influences between random processes. In practice, these quantities must be measured fr…
Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
Yuping Zheng, Andrew Lamperski
Stochastic gradient descent (SGD) is the main algorithm behind a large body of work in machine learning. In many cases, constraints are enforced via projections, leading to project…
Non-Asymptotic Analysis of Classical Spectrum Estimators with -mixing Time-series Data
Yuping Zheng, Andrew Lamperski
Spectral estimation is a fundamental problem for time series analysis, which is widely applied in economics, speech analysis, seismology, and control systems. The asymptotic conver…
Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts
Guorui Zheng, Xidong Wang, Juhao Liang +3
Adapting medical Large Language Models to local languages can reduce barriers to accessing healthcare services, but data scarcity remains a significant challenge, particularly for…