Xiaohan Chen
27 papers · 2018–2025 · 7 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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πΊοΈ Taxonomy Completionist (45) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
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Triple Crown
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Grand Slam
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Deep Specialist
(11)
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Keyword Pioneer
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Dynamic Duo
(25)
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Prolific Year Β· Productive
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Unstoppable Β· Bronze
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The Questioner
(2)
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Century Club Β· Bronze
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Trend Setter
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Keyword Collector
(122)
Top co-authors
Research topics
Machine Learning
(25)
Deep Learning
(18)
Optimization & Theory Β· Machine Learning
(17)
Application Areas
(12)
Artificial Intelligence
(8)
Optimization & Theory Β· Deep Learning
(7)
Techniques
(7)
Architectures
(6)
Computer Vision
(4)
Core AI
(4)
Core Methods
(4)
Learning Types Β· Machine Learning
(4)
Analysis
(3)
Learning Paradigms
(3)
Learning Types Β· Deep Learning
(3)
Keywords
lottery ticket hypothesis
(5)
model compression
(5)
network pruning
(4)
sparse training
(4)
convolutional neural network
(3)
learning to optimize
(3)
sparse recovery
(3)
neural network
(3)
early bird ticket
(2)
winning ticket
(2)
sparse signal recovery
(2)
sparse neural network
(2)
hyperparameter tuning
(2)
efficient computing
(2)
message passing
(2)
convergence guarantee
(2)
uncertainty quantification
(1)
curriculum learning
(1)
semantic segmentation
(1)
compressive sensing
(1)
Papers
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
ICML 2025
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
NeurIPS 2024
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
ICLR 2023
Safeguarded Learned Convex Optimization
AAAI 2023
Towards Constituting Mathematical Structures for Learning to Optimize
ICML 2023
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
ICLR 2022
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better
AAAI 2022
Learning to Optimize: A Primer and A Benchmark
JMLR 2022
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently
ICLR 2022
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets
NeurIPS 2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
ICLR 2022
A Design Space Study for LISTA and Beyond
ICLR 2021
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets
ACL 2021
Hyperparameter Tuning is All You Need for LISTA
NeurIPS 2021
Learning A Minimax Optimizer: A Pilot Study
ICLR 2021
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
NeurIPS 2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
NeurIPS 2021
The Elastic Lottery Ticket Hypothesis
NeurIPS 2021
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks
ICLR 2020
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
NeurIPS 2020
ShiftAddNet: A Hardware-Inspired Deep Network
NeurIPS 2020
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
AISTATS 2020
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
ICLR 2019
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
NeurIPS 2019
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
ICML 2019
Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
NeurIPS 2018
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
NeurIPS 2018