6 papers
Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite Moments
Qianqian Lei, Soham Bonnerjee, Yuefeng Han +1
While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically rely on uniform boundedness or…
PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation
Blake Gella, Wei Wu, Yuhao Yin +6
Recommender systems trained on user interaction data are susceptible to behavioral intensity imbalance--a systematic distortion arising from heterogeneous engagement patterns acros…
Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
Ziyang Wei, Wanrong Zhu, Jingyang Lyu +1
We study online inference and asymptotic covariance estimation for the stochastic gradient descent (SGD) algorithm. While classical methods (such as plug-in and batch-means estimat…
Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization
Soham Bonnerjee, Zhipeng Lou, Wei Biao Wu
Despite the sustained popularity of Q-learning as a practical tool for policy determination, a majority of relevant theoretical literature deals with either constant ($η_{t}\equiv…
Sharp Gaussian approximations for Decentralized Federated Learning
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
Federated Learning has gained traction in privacy-sensitive collaborative environments, with local SGD emerging as a key optimization method in decentralized settings. While its co…
Gaussian Approximation For Non-stationary Time Series with Optimal Rate and Explicit Construction
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these…