← Authors

James Cheng

31 papers · 2014–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (41) πŸ”¬ Deep Specialist (10) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (15) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (129) ⚑ Prolific Year (5) πŸ’Ž Century Club (31) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (10) ❓ The Questioner (2)

Conferences

NIPS (10) ICLR (5) AAAI (4) AISTATS (4) ICML (4) ACML (1) EMNLP (1) IJCAI (1) UAI (1)

Top co-authors

Kaiwen Zhou (15) Yongqiang Chen (14) Bo Han (11) Yatao Bian (7) Binghui Xie (7) Fanhua Shang (7) Han Yang (5) MA Kaili (4) Yuanyuan Liu (4) Xinyan Dai (3)

Keywords

convex optimization (5) domain generalization (4) variance reduction (3) stochastic variance reduced gradient (3) out-of-distribution generalization (3) graph neural network (3) maximum inner product search (3) representation learning (2) causal model (2) stochastic optimization (2) convergence rate (2) first-order method (2) oracle complexity (2) causal inference (2) nearest neighbor search (2) accelerated gradient (2) invariant learning (2) matrix completion (2) causal discovery (1) logistic regression (1)

Papers

Hierarchical Graph Tokenization for Molecule-Language Alignment ICML 2025
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis ICLR 2025
Retrieval-Augmented Generation with Hierarchical Knowledge EMNLP 2025
HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection NIPS 2024
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations ICLR 2024
Discovery of the Hidden World with Large Language Models NIPS 2024
How Interpretable Are Interpretable Graph Neural Networks? ICML 2024
Enhancing Evolving Domain Generalization through Dynamic Latent Representations AAAI 2024
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NIPS 2023
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization ICLR 2023
Understanding and Improving Feature Learning for Out-of-Distribution Generalization NIPS 2023
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs NIPS 2022
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability ICLR 2022
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums AISTATS 2022
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack ICML 2022
Exact Shape Correspondence via 2D graph convolution NIPS 2022
Rethinking Graph Regularization for Graph Neural Networks AAAI 2021
Tight Convergence Rate of Gradient Descent for Eigenvalue Computation IJCAI 2020
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates NIPS 2020
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search AAAI 2020
Understanding and Improving Proximity Graph Based Maximum Inner Product Search AAAI 2020
Amortized Nesterov’s Momentum: A Robust Momentum and Its Application to Deep Learning UAI 2020
Measuring and Improving the Use of Graph Information in Graph Neural Networks ICLR 2020
Direct Acceleration of SAGA using Sampled Negative Momentum AISTATS 2019
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization AISTATS 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates ICML 2018
ASVRG: Accelerated Proximal SVRG ACML 2018
Norm-Ranging LSH for Maximum Inner Product Search NIPS 2018
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds NIPS 2017
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization AISTATS 2016
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion NIPS 2014