Xinmeng Huang
16 papers · 2021–2025 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (32) π Interdisciplinary Bridge Β· Bronze π Conference Polyglot Β· Silver π¬ Deep Specialist (10) π€ Dynamic Duo (10)
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Triple Crown
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Keyword Collector
(79)
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Prolific Year Β· Productive
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Renaissance Researcher
(5)
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Century Club Β· Bronze
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Research topics
Machine Learning
(16)
Optimization & Theory Β· Machine Learning
(16)
Mathematics & Optimization
(9)
Optimization
(9)
Artificial Intelligence
(5)
Learning Types
(4)
Deep Learning
(3)
Core AI
(3)
Optimization & Theory Β· Deep Learning
(3)
Learning Paradigms
(2)
Data Science & Analytics
(1)
Natural Language Processing
(1)
Reinforcement Learning
(1)
Application Areas
(1)
Core Methods
(1)
Keywords
stochastic optimization
(5)
decentralized optimization
(5)
distributed learning
(4)
communication compression
(4)
bilevel optimization
(3)
convergence rate
(3)
multi-agent system
(2)
uncertainty quantification
(2)
federated learning
(2)
gradient compression
(2)
variance reduction
(2)
convergence analysis
(2)
non-convex optimization
(2)
network topology
(2)
stochastic gradient
(2)
communication efficiency
(2)
distributed optimization
(2)
language model
(2)
model evaluation
(1)
preference alignment
(1)
Papers
Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
JMLR 2025
Distributed Bilevel Optimization with Communication Compression
ICML 2024
Momentum Benefits Non-iid Federated Learning Simply and Provably
ICLR 2024
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
NeurIPS 2024
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization
NeurIPS 2024
Stochastic Controlled Averaging for Federated Learning with Communication Compression
ICLR 2024
Uncertainty in Language Models: Assessment through Rank-Calibration
EMNLP 2024
Demystifying Disagreement-on-the-Line in High Dimensions
ICML 2023
Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD
JMLR 2023
T-Cal: An Optimal Test for the Calibration of Predictive Models
JMLR 2023
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
NeurIPS 2023
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
NeurIPS 2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
NeurIPS 2022
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
NeurIPS 2022
An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders
NeurIPS 2021
DecentLaM: Decentralized Momentum SGD for Large-Batch Deep Training
ICCV 2021