
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
Sampling methods (e.g., nodewise, layerwise, or subgraph) has become a...
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Adaptive Personalized Federated Learning
Investigation of the degree of personalization in federated learning alg...
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Learning Feature Nonlinearities with NonConvex Regularized Binned Regression
For various applications, the relations between the dependent and indepe...
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Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and Highdimensional Data
Sketching techniques have become popular for scaling up machine learning...
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Train and Test Tightness of LP Relaxations in Structured Prediction
Structured prediction is used in areas such as computer vision and natur...
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Binary Excess Risk for Smooth Convex Surrogates
In statistical learning theory, convex surrogates of the 01 loss are hi...
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Excess Risk Bounds for Exponentially Concave Losses
The overarching goal of this paper is to derive excess risk bounds for l...
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Beating the Minimax Rate of Active Learning with Prior Knowledge
Active learning refers to the learning protocol where the learner is all...
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Sparse Multiple Kernel Learning with Geometric Convergence Rate
In this paper, we study the problem of sparse multiple kernel learning (...
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An Improved Bound for the Nystrom Method for Large Eigengap
We develop an improved bound for the approximation error of the Nyström ...
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On the Convergence of Local Descent Methods in Federated Learning
In federated distributed learning, the goal is to optimize a global trai...
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Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Communication overhead is one of the key challenges that hinders the sca...
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Efficient Fair Principal Component Analysis
The flourishing assessments of fairness measure in machine learning algo...
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Federated Learning with Compression: Unified Analysis and Sharp Guarantees
In federated learning, communication cost is often a critical bottleneck...
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Mehrdad Mahdavi
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