2 citations · 3 across the 8 of their papers we have counts for
8 papers
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 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…
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…
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…
Optimizing the Wasserstein GAN for TeV Gamma Ray Detection with VERITAS
Deivid Ribeiro, Yuping Zheng, Ramana Sankar +1
The observation of very-high-energy (VHE, E>100 GeV) gamma rays is mediated by the imaging atmospheric Cherenkov technique (IACTs). At these energies, gamma rays interact with the…
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model
Kameswara Bharadwaj Mantha, Ramanakumar Sankar, Yuping Zheng +11
Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the ra…
Constrained Langevin Algorithms with L-mixing External Random Variables
Yuping Zheng, Andrew Lamperski
Langevin algorithms are gradient descent methods augmented with additive noise, and are widely used in Markov Chain Monte Carlo (MCMC) sampling, optimization, and machine learning.…