{"total":36,"rows":[{"id":"2303.04947","title":"InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning","created":"2023-03-08","cited_by_count":90,"area":null},{"id":"2110.10031","title":"Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference","created":"2021-10-19","cited_by_count":67,"area":null},{"id":"2209.14981","title":"Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging","created":"2022-09-29","cited_by_count":54,"area":null},{"id":"2207.04338","title":"Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning","created":"2022-07-09","cited_by_count":33,"area":null},{"id":"2505.17799","title":"A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances","created":"2025-05-23","cited_by_count":28,"area":null},{"id":"2103.08933","title":"Reweighting Augmented Samples by Minimizing the Maximal Expected Loss","created":"2021-03-16","cited_by_count":21,"area":null},{"id":"2209.10888","title":"Amortized Variational Inference: A Systematic Review","created":"2022-09-22","cited_by_count":16,"area":null},{"id":"2103.02062","title":"Variance Reduced Training with Stratified Sampling for Forecasting Models","created":"2021-03-02","cited_by_count":14,"area":null},{"id":"2110.15590","title":"Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff","created":"2021-10-29","cited_by_count":13,"area":null},{"id":"2210.09634","title":"DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling","created":"2022-10-18","cited_by_count":13,"area":null},{"id":"2103.12243","title":"Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes","created":"2021-03-23","cited_by_count":10,"area":null},{"id":"2307.14642","title":"Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?","created":"2023-07-27","cited_by_count":10,"area":null},{"id":"2107.12211","title":"A General Theory for Client Sampling in Federated Learning","created":"2021-07-26","cited_by_count":8,"area":null},{"id":"2103.12293","title":"Stochastic Reweighted Gradient Descent","created":"2021-03-23","cited_by_count":7,"area":null},{"id":"2112.14332","title":"Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback","created":"2021-12-28","cited_by_count":7,"area":null},{"id":"2108.01662","title":"Uniform Sampling over Episode Difficulty","created":"2021-08-03","cited_by_count":6,"area":null},{"id":"2306.16484","title":"Towards a Better Theoretical Understanding of Independent Subnetwork Training","created":"2023-06-28","cited_by_count":6,"area":null},{"id":"2407.20611","title":"The Entrapment Problem in Random Walk Decentralized Learning","created":"2024-07-30","cited_by_count":4,"area":null},{"id":"2403.00853","title":"Parallel Momentum Methods Under Biased Gradient Estimations","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2510.09685","title":"Deep Neural Networks Inspired by Differential Equations","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2601.21624","title":"Training Memory in Deep Neural Networks: Mechanisms, Evidence, and Measurement Gaps","created":"2026-01-29","cited_by_count":2,"area":null},{"id":"2205.01988","title":"Modelling calibration uncertainty in networks of environmental sensors","created":"2022-05-04","cited_by_count":1,"area":null},{"id":"2203.00761","title":"Tricks and Plugins to GBM on Images and Sequences","created":"2022-03-01","cited_by_count":0,"area":null},{"id":"2312.05021","title":"A Negative Result on Gradient Matching for Selective Backprop","created":"2023-12-08","cited_by_count":0,"area":null},{"id":"2404.09861","title":"Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2404.16917","title":"Grad Queue : A probabilistic framework to reinforce sparse gradients","created":"2024-04-25","cited_by_count":0,"area":null},{"id":"2506.15387","title":"Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimization","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2507.04269","title":"Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need","created":"2025-07-06","cited_by_count":0,"area":null},{"id":"2507.22842","title":"Tricks and Plug-ins for Gradient Boosting in Image Classification","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2508.02924","title":"Tricks and Plug-ins for Gradient Boosting with Transformers","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2509.21746","title":"HyperCore: Coreset Selection under Noise via Hypersphere Models","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2509.21748","title":"SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2512.10188","title":"The Interplay of Statistics and Noisy Optimization: Learning Linear Predictors with Random Data Weights","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2604.12260","title":"Decentralized Learning via Random Walk with Jumps","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2605.07551","title":"Disagreement-Regularized Importance Sampling for Adversarial Label Corruption","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2609.31157","title":"Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection","created":"2026-09-25","cited_by_count":0,"area":null}],"next_cursor":null}