James Cheng
31 papers · 2014–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (9) π Academic Marathon (11) π§ Keyword Pioneer π Interdisciplinary Bridge π Cross-Pollinator (13)
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Cross-Pollinator
(13)
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Renaissance Researcher
(7)
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Taxonomy Completionist
(41)
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Deep Specialist
(10)
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Triple Crown
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Dynamic Duo
(15)
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Grand Slam
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Keyword Collector
(129)
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Prolific Year
(5)
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Century Club
(31)
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Trend Setter
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Unstoppable
(10)
β
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
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 Nesterovβs 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