Shuiwang Ji

papers

1,116

total citations

papers (23)

Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence

NEURIPS 2021arXiv

citations

A new perspective on building efficient and expressive 3D equivariant graph neural networks

NEURIPS 2023arXiv

citations

Eliminating Position Bias of Language Models: A Mechanistic Approach

ICLR 2025arXiv

citations

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

NEURIPS 2023arXiv

citations

Towards Symmetry-Aware Generation of Periodic Materials

NEURIPS 2023arXiv

citations

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

NEURIPS 2023arXiv

citations

Task-Agnostic Graph Explanations

NEURIPS 2022arXiv

citations

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

ICLR 2024arXiv

citations

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency

ICML 2024arXiv

citations

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

ICML 2025arXiv

citations

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design

ICML 2025arXiv

citations

Geometry Informed Tokenization of Molecules for Language Model Generation

ICML 2025arXiv

citations

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction

ICML 2024arXiv

citations

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

ICLR 2025arXiv

citations

Video Timeline Modeling For News Story Understanding

NEURIPS 2023arXiv

citations

Discovering Physics Laws of Dynamical Systems via Invariant Function Learning

ICML 2025arXiv

citations

Position: TrustLLM: Trustworthiness in Large Language Models

ICML 2024

citations

Graph Structure Extrapolation for Out-of-Distribution Generalization

ICML 2024

citations

Shuiwang Ji

papers (23)

GOOD: A Graph Out-of-Distribution Benchmark

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Periodic Graph Transformers for Crystal Material Property Prediction

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence

A new perspective on building efficient and expressive 3D equivariant graph neural networks

Eliminating Position Bias of Language Models: A Mechanistic Approach

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Towards Symmetry-Aware Generation of Periodic Materials

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Task-Agnostic Graph Explanations

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design

Geometry Informed Tokenization of Molecules for Language Model Generation

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Video Timeline Modeling For News Story Understanding

Discovering Physics Laws of Dynamical Systems via Invariant Function Learning

Position: TrustLLM: Trustworthiness in Large Language Models

Graph Structure Extrapolation for Out-of-Distribution Generalization

papers (23)

GOOD: A Graph Out-of-Distribution Benchmark

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Periodic Graph Transformers for Crystal Material Property Prediction

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence

A new perspective on building efficient and expressive 3D equivariant graph neural networks

Eliminating Position Bias of Language Models: A Mechanistic Approach

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Towards Symmetry-Aware Generation of Periodic Materials

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Task-Agnostic Graph Explanations

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design

Geometry Informed Tokenization of Molecules for Language Model Generation

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Video Timeline Modeling For News Story Understanding

Discovering Physics Laws of Dynamical Systems via Invariant Function Learning

Position: TrustLLM: Trustworthiness in Large Language Models

Graph Structure Extrapolation for Out-of-Distribution Generalization