Non-asymptotic Analysis of Biased Stochastic Approximation Scheme. - Belhal Karimi; Blazej Miasojedow; Eric Moulines; Hoi-To Wai (COLT, 2019)

On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. - Belhal Karimi; Hoi-To Wai; Eric Moulines; Marc Lavielle (NeurIPS, 2019)

Towards Better Generalization of Adaptive Gradient Methods. - Yingxue Zhou; Belhal Karimi; Jinxing Yu; Zhiqiang Xu; Ping Li 001 (NeurIPS, 2020)

FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching. - Farzin Haddadpour; Belhal Karimi; Ping Li 001; Xiaoyun Li (CoRR, 2020)

f-SAEM: A fast stochastic approximation of the EM algorithm for nonlinear mixed effects models. - Belhal Karimi; Marc Lavielle; Eric Moulines (Comput. Stat. Data Anal., 2020)

An Optimistic Acceleration of AMSGrad for Nonconvex Optimization. - Jun-Kun Wang; Xiaoyun Li; Belhal Karimi; Ping Li 001 (ACML, 2021)

Two-Timescale Stochastic EM Algorithms. - Belhal Karimi; Ping Li 001 (ISIT, 2021)

Fed-LAMB: Layerwise and Dimensionwise Locally Adaptive Optimization Algorithm. - Belhal Karimi; Xiaoyun Li; Ping Li 001 (CoRR, 2021)

On the Convergence of Decentralized Adaptive Gradient Methods. - Xiangyi Chen; Belhal Karimi; Weijie Zhao 001; Ping Li 001 (ACML, 2022)

Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems. - Belhal Karimi; Hoi-To Wai; Eric Moulines; Ping Li 001 (ALT, 2022)

FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems. - Weijie Zhao 001; Xuewu Jiao; Xinsheng Luo; Jingxue Li; Belhal Karimi; Ping Li 001 (IEEE Big Data, 2022)

On Distributed Adaptive Optimization with Gradient Compression. - Xiaoyun Li; Belhal Karimi; Ping Li 001 (ICLR, 2022)

Joint Learning of Object Graph and Relation Graph for Visual Question Answering. - Hao Li 073; Xu Li 001; Belhal Karimi; Jie Chen 001; Mingming Sun 001 (ICME, 2022)

Variational Flow Graphical Model. - Shaogang Ren; Belhal Karimi; Dingcheng Li; Ping Li 001 (KDD, 2022)

A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent Variable Models. - Belhal Karimi; Ping Li 001 (CoRR, 2022)

Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. - Belhal Karimi; Ping Li 001; Xiaoyun Li (UAI, 2023)

STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models. - Belhal Karimi; Jianwen Xie; Ping Li 001 (CoRR, 2023)