← Authors

Xiaohan Chen

28 papers · 2018–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (7) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (44) 🀝 Dynamic Duo (26) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (92) πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (28) ⚑ Prolific Year (8) πŸ“ˆ Trend Setter ❓ The Questioner (2)

Conferences

NIPS (11) ICLR (8) ICML (3) AAAI (2) ACL (1) AISTATS (1) IJCNLP (1) JMLR (1)

Top co-authors

Zhangyang Wang (26) Wotao Yin (10) Jialin Liu (9) Tianlong Chen (8) Decebal Constantin Mocanu (4) Mykola Pechenizkiy (4) Shiwei Liu (4) Jingjing Liu (3) Zhe Gan (3) Yu Cheng (3)

Keywords

model compression (6) lottery ticket hypothesis (3) network pruning (3) convolutional neural network (2) neural network (2) efficient computing (2) hyperparameter tuning (2) learning to optimize (2) sparse recovery (2) sparse training (2) few-shot learning (1) universal approximation (1) attention mechanism (1) deep learning (1) neural network training (1) uncertainty quantification (1) non-iid learning (1) compressive sensing (1) continuous optimization (1) out-of-distribution generalization (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 NIPS 2024
Safeguarded Learned Convex Optimization AAAI 2023
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity ICLR 2023
Towards Constituting Mathematical Structures for Learning to Optimize ICML 2023
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training ICLR 2022
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently ICLR 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity ICLR 2022
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets NIPS 2022
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better AAAI 2022
Learning to Optimize: A Primer and A Benchmark JMLR 2022
Learning A Minimax Optimizer: A Pilot Study ICLR 2021
Hyperparameter Tuning is All You Need for LISTA NIPS 2021
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets ACL 2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration NIPS 2021
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets IJCNLP 2021
The Elastic Lottery Ticket Hypothesis NIPS 2021
A Design Space Study for LISTA and Beyond ICLR 2021
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? NIPS 2021
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery AISTATS 2020
ShiftAddNet: A Hardware-Inspired Deep Network NIPS 2020
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning NIPS 2020
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks ICLR 2020
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers ICML 2019
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 NIPS 2019
Can We Gain More from Orthogonality Regularizations in Training Deep Networks? NIPS 2018
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds NIPS 2018