Jialin Liu
21 papers · 2018–2026 · 9 conferences · across top CS/AI conferences
Achievements
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🐝 Cross-Pollinator (7) 🧭 Keyword Pioneer 🏃 Academic Marathon (7) 🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5)
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Conference Polyglot
(7)
🏃
Academic Marathon
(7)
🤝
Dynamic Duo
(12)
👑
Triple Crown
🏆
Grand Slam
🗃️
Keyword Collector
(73)
💎
Century Club
(18)
🔥
Unstoppable
(5)
Conferences
ICLR (6)
NIPS (5)
ICML (3)
ACL (2)
AAAI (1)
CVPR (1)
EMNLP (1)
IJCAI (1)
JMLR (1)
Top co-authors
Keywords
learning to optimize
(2)
graph neural network
(2)
sparse recovery
(2)
neural network optimization
(2)
bayesian inference
(1)
compressive sensing
(1)
continual learning
(1)
universal approximation
(1)
non-convex optimization
(1)
autoregressive generation
(1)
out-of-distribution generalization
(1)
continuous optimization
(1)
image denoising
(1)
markov chain monte carlo
(1)
branch and bound
(1)
outlier detection
(1)
message passing
(1)
posterior inference
(1)
sparse signal recovery
(1)
text generation
(1)
Papers
MoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommender
AAAI 2026
EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation
ACL 2026
Triviality Corrected Endogenous Reward
ACL 2026
LiBOG: Lifelong Learning for Black-Box Optimizer Generation
IJCAI 2025
Convolutional LoRA Aggregation for Unseen Tasks Adaptation
EMNLP 2025
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
ICML 2025
StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation
CVPR 2025
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
NIPS 2024
Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation
ICLR 2024
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
ICLR 2023
On Representing Linear Programs by Graph Neural Networks
ICLR 2023
Towards Constituting Mathematical Structures for Learning to Optimize
ICML 2023
Learning to Optimize: A Primer and A Benchmark
JMLR 2022
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
ICLR 2021
Learning A Minimax Optimizer: A Pilot Study
ICLR 2021
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
NIPS 2021
Hyperparameter Tuning is All You Need for LISTA
NIPS 2021
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
ICML 2019
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
NIPS 2019
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
ICLR 2019
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
NIPS 2018