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08/29/2023
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
As powerful tools for representation learning on graphs, graph neural ne...
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05/14/2023
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
This paper considers a novel application of deep AUC maximization (DAM) ...
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03/01/2022
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
In this paper, we propose systematic and efficient gradient-based method...
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02/24/2022
Finite-Sum Compositional Stochastic Optimization: Theory and Applications
This paper studies stochastic optimization for a sum of compositional fu...
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02/15/2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
In this paper, we investigate the problem of stochastic multi-level comp...
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06/09/2021
Memory-based Optimization Methods for Model-Agnostic Meta-Learning
Recently, model-agnostic meta-learning (MAML) has garnered tremendous at...
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06/07/2021
Smoothness-Aware Quantization Techniques
Distributed machine learning has become an indispensable tool for traini...
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02/16/2021
IntSGD: Floatless Compression of Stochastic Gradients
We propose a family of lossy integer compressions for Stochastic Gradien...
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05/03/2020
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
Riemannian optimization has drawn a lot of attention due to its wide app...
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01/29/2019