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Zhanxing Zhu

40 papers · 2015–2026 · 8 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (64) 🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (10) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (11) πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (153) ⚑ Prolific Year (5) πŸ’Ž Century Club (39) πŸ”₯ Unstoppable (9)

Conferences

NIPS (12) ICML (8) AAAI (7) ICLR (6) ACML (2) CVPR (2) IJCAI (2) ACL (1)

Top co-authors

Nanyang Ye (5) Dinghuai Zhang (4) Bochen Lyu (4) Bing Yu (4) Jingfeng Wu (3) Zhouchen Lin (3) Ke Sun (3) Qianxiao Li (2) Jinwen Ma (2) Tianyuan Zhang (2)

Research topics

Optimization (1)

Keywords

adversarial training (6) stochastic gradient descent (5) markov chain monte carlo (3) langevin dynamics (3) adversarial attack (3) semi-supervised learning (3) stochastic optimization (3) bayesian inference (3) convolutional neural network (2) stochastic gradient (2) adversarial perturbation (2) hamiltonian monte carlo (2) computational efficiency (2) deep learning (2) neural architecture search (2) representation learning (2) domain generalization (2) virtual adversarial training (2) posterior distribution (2) implicit bia (2)

Papers

ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction AAAI 2026
Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems ICML 2025
A Solvable Attention for Neural Scaling Laws ICLR 2025
DyCAST: Learning Dynamic Causal Structure from Time Series ICLR 2025
Effects of Momentum in Implicit Bias of Gradient Flow for Diagonal Linear Networks AAAI 2025
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization NIPS 2024
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy ACML 2023
Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling NIPS 2023
Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network NIPS 2023
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows ICML 2023
Implicit Bias of Adversarial Training for Deep Neural Networks ICLR 2022
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics ICLR 2022
Adversarial Invariant Learning CVPR 2021
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models ICLR 2021
Neural Approximate Sufficient Statistics for Implicit Models ICLR 2021
Amata: An Annealing Mechanism for Adversarial Training Acceleration AAAI 2021
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization ICML 2021
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay NIPS 2021
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting AAAI 2021
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective ICML 2020
On the Noisy Gradient Descent that Generalizes as SGD ICML 2020
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes AAAI 2020
Efficient Neural Architecture Search via Proximal Iterations AAAI 2020
Towards Understanding and Improving the Transferability of Adversarial Examples in Deep Neural Networks ACML 2020
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher NIPS 2020
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework NIPS 2020
On Breaking Deep Generative Model-based Defenses and Beyond ICML 2020
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects ICML 2019
SpHMC: Spectral Hamiltonian Monte Carlo AAAI 2019
Interpreting Adversarially Trained Convolutional Neural Networks ICML 2019
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning CVPR 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle NIPS 2019
Stochastic Fractional Hamiltonian Monte Carlo IJCAI 2018
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning NIPS 2018
Reinforced Continual Learning NIPS 2018
Bayesian Adversarial Learning NIPS 2018
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting IJCAI 2018
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NIPS 2017
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix ACL 2017
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling NIPS 2015