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Found 5 papers, 1 papers with code
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
In this approach a model trained for a source task, where plenty of labeled training data is available, is used as a starting point for training a model on a related target task with only few labeled training data.
Fitting ReLUs via SGD and Quantized SGD
Perhaps unexpectedly, we show that QSGD maintains the fast convergence of SGD to a globally optimal model while significantly reducing the communication cost.
Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms.
Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding
In particular, PCR requires a recovery threshold that scales inversely proportionally with the amount of computation/storage available at each worker.
Fundamental Resource Trade-offs for Encoded Distributed Optimization
We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy), and straggler toleration in this framework.
