H. Vincent Poor
13 papers · 2019–2024 · 6 venues · across top CS/AI venues
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Machine Learning
(13)
Optimization & Theory
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Deep Learning
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Mathematics & Optimization
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Artificial Intelligence
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Learning Types · Machine Learning
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Optimization
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non-convex optimization
(3)
convergence analysis
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graph neural network
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tensor completion
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higher-order interaction
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convergence rate
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simplicial neural network
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link prediction
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stochastic gradient descent
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dimensionality reduction
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data poisoning
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convex optimization
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graph learning
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few-shot learning
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reinforcement learning
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model aggregation
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topological data analysis
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temporal difference learning
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distributed optimization
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distributed learning
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Papers
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
AISTATS 2024
Alternating Differentiation for Optimization Layers
ICLR 2023
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
NeurIPS 2023
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence
AISTATS 2022
BScNets: Block Simplicial Complex Neural Networks
AAAI 2022
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
JMLR 2022
Learning Mixtures of Linear Dynamical Systems
ICML 2022
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
NeurIPS 2022
DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation
AAAI 2021
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
NeurIPS 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
NeurIPS 2020
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
ICML 2020
Nonconvex Low-Rank Tensor Completion from Noisy Data
NeurIPS 2019