Showing 21–40 of 56 results
/ Date/ Name
Nov 19, 2021DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization AbilityOct 17, 2023Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition FunctionsOct 17, 2022Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax ProblemsJun 24, 2020Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural NetworksOct 28, 2021On Provable Benefits of Depth in Training Graph Convolutional NetworksMar 3, 2021On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance ReductionMay 20, 2017Learning Feature Nonlinearities with Non-Convex Regularized Binned RegressionOct 10, 2016Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional DataOct 22, 2020Online Structured Meta-learningOct 26, 2021Meta-learning with an Adaptive Task SchedulerNov 16, 2021Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural NetworksFeb 8, 2021Communication-efficient k-Means for Edge-based Machine LearningSep 22, 2023Understanding Deep Gradient Leakage via Inversion Influence FunctionsFeb 22, 2023Do We Really Need Complicated Model Architectures For Temporal Networks?Aug 22, 2024Stochastic Compositional Minimax Optimization with Provable Convergence GuaranteesJun 18, 2012Multiple Kernel Learning from Noisy Labels by Stochastic ProgrammingNov 19, 2013Beating the Minimax Rate of Active Learning with Prior KnowledgeDec 24, 2025Model Merging via Multi-Teacher Knowledge DistillationSep 23, 2025NaviSense: A Multimodal Assistive Mobile application for Object Retrieval by Persons with Visual ImpairmentJul 2, 2020Federated Learning with Compression: Unified Analysis and Sharp Guarantees