← Authors

Le Song

135 papers · 2007–2025 · 13 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (13) πŸ—ΊοΈ Taxonomy Completionist (46) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (18) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (50) 🌟 Keyword Trendsetter Combo (14) 🀝 Dynamic Duo (23) πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ† Grand Slam 🌱 Topic Pioneer πŸ”¬ Deep Specialist (29) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (19) ⚑ Prolific Year (16) πŸ’Ž Century Club (135) πŸ—ƒοΈ Keyword Collector (169) ❓ The Questioner

Conferences

NIPS (50) ICML (25) ICLR (18) AISTATS (17) CVPR (5) JMLR (5) AAAI (4) ACL (4) EMNLP (2) ICCV (2) COLT (1) IJCAI (1) IJCNLP (1)

Top co-authors

Bo Dai (23) Hanjun Dai (19) Hongyuan Zha (15) Weiyang Liu (13) Nan Du (12) Niao He (11) Yichen Wang (10) Arthur Gretton (10) Yuan Qi (10) Xinshi Chen (9)

Research topics

Optimization (1) Statistics (1) Education (1)

Keywords

kernel methods (17) convex optimization (11) graph neural network (9) point process (9) social network (8) graphical model (8) sample complexity (8) representation learning (7) hawkes process (7) network inference (6) reproducing kernel hilbert space (6) reinforcement learning (6) variational inference (6) convolutional neural network (6) diffusion network (5) temporal point process (5) active learning (5) bayesian inference (5) stochastic optimization (5) neural network (5)

Papers

Size-Generalizable RNA Structure Evaluation by Exploring Hierarchical Geometries ICLR 2025
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs ACL 2025
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training NIPS 2024
Optimistic Bayesian Optimization with Unknown Constraints ICLR 2024
Training Compute-Optimal Protein Language Models NIPS 2024
Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization NIPS 2023
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data NIPS 2023
XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images ICCV 2023
Uncovering the Structural Fairness in Graph Contrastive Learning NIPS 2022
Spanning Tree-based Graph Generation for Molecules ICLR 2022
GNN is a Counter? Revisiting GNN for Question Answering ICLR 2022
Explaining Point Processes by Learning Interpretable Temporal Logic Rules ICLR 2022
Provable Learning-based Algorithm For Sparse Recovery ICLR 2022
PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning ACL 2022
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select EMNLP 2022
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition ACL 2021
Molecule Optimization by Explainable Evolution ICLR 2021
Orthogonal Over-Parameterized Training CVPR 2021
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition IJCNLP 2021
RoMA: Robust Model Adaptation for Offline Model-based Optimization NIPS 2021
A Biased Graph Neural Network Sampler with Near-Optimal Regret NIPS 2021
Multi-task Learning of Order-Consistent Causal Graphs NIPS 2021
Locality Sensitive Teaching NIPS 2021
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning NIPS 2021
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees ICLR 2020
GLAD: Learning Sparse Graph Recovery ICLR 2020
Learn to Explain Efficiently via Neural Logic Inductive Learning ICLR 2020
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS ICLR 2020
Learning To Stop While Learning To Predict ICML 2020
Understanding Deep Architecture with Reasoning Layer NIPS 2020
Bandit Samplers for Training Graph Neural Networks NIPS 2020
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models NIPS 2020
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction AAAI 2020
Cost-Effective Incentive Allocation via Structured Counterfactual Inference AAAI 2020
Temporal Logic Point Processes ICML 2020
RNA Secondary Structure Prediction By Learning Unrolled Algorithms ICLR 2020
Double Neural Counterfactual Regret Minimization ICLR 2020
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search ICML 2020
Question Directed Graph Attention Network for Numerical Reasoning over Text EMNLP 2020
Efficient Probabilistic Logic Reasoning with Graph Neural Networks ICLR 2020
Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020
GeniePath: Graph Neural Networks with Adaptive Receptive Paths AAAI 2019
Large Scale Evolving Graphs with Burst Detection IJCAI 2019
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System ICML 2019
Particle Flow Bayes’ Rule ICML 2019
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data ICLR 2019
Retrosynthesis Prediction with Conditional Graph Logic Network NIPS 2019
Exponential Family Estimation via Adversarial Dynamics Embedding NIPS 2019
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning NIPS 2019
Neural Similarity Learning NIPS 2019
Meta Architecture Search NIPS 2019
Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019
Language Modeling with Shared Grammar ACL 2019
Learning a Meta-Solver for Syntax-Guided Program Synthesis ICLR 2019
Latent Dirichlet Allocation for Internet Price War AAAI 2019
Boosting the Actor with Dual Critic ICLR 2018
Towards Black-box Iterative Machine Teaching ICML 2018
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation ICML 2018
Adversarial Attack on Graph Structured Data ICML 2018
Learning Steady-States of Iterative Algorithms over Graphs ICML 2018
Stochastic Training of Graph Convolutional Networks with Variance Reduction ICML 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation ICML 2018
Learning towards Minimum Hyperspherical Energy NIPS 2018
Learning Temporal Point Processes via Reinforcement Learning NIPS 2018
Learning Loop Invariants for Program Verification NIPS 2018
Coupled Variational Bayes via Optimization Embedding NIPS 2018
Multi-scale Nystrom Method AISTATS 2018
A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop AISTATS 2018
Decoupled Networks CVPR 2018
Iterative Learning With Open-Set Noisy Labels CVPR 2018
Syntax-Directed Variational Autoencoder for Structured Data ICLR 2018
Learning from Conditional Distributions via Dual Embeddings AISTATS 2017
Fake News Mitigation via Point Process Based Intervention ICML 2017
Iterative Machine Teaching ICML 2017
Predicting User Activity Level In Point Processes With Mass Transport Equation NIPS 2017
Deep Hyperspherical Learning NIPS 2017
SphereFace: Deep Hypersphere Embedding for Face Recognition CVPR 2017
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs ICML 2017
Variational Policy for Guiding Point Processes ICML 2017
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks JMLR 2017
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution JMLR 2017
Stochastic Generative Hashing ICML 2017
Wasserstein Learning of Deep Generative Point Process Models NIPS 2017
Learning Combinatorial Optimization Algorithms over Graphs NIPS 2017
On the Complexity of Learning Neural Networks NIPS 2017
Diverse Neural Network Learns True Target Functions AISTATS 2017
Linking Micro Event History to Macro Prediction in Point Process Models AISTATS 2017
The Nonparametric Kernel Bayes Smoother AISTATS 2016
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions NIPS 2016
Multistage Campaigning in Social Networks NIPS 2016
Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016
Isotonic Hawkes Processes ICML 2016
Discriminative Embeddings of Latent Variable Models for Structured Data ICML 2016
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm JMLR 2016
Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades AISTATS 2015
A la Carte – Learning Fast Kernels AISTATS 2015
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution NIPS 2015
Deep Fried Convnets ICCV 2015
M-Statistic for Kernel Change-Point Detection NIPS 2015
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression NIPS 2015
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients NIPS 2015
Time-Sensitive Recommendation From Recurrent User Activities NIPS 2015
Influence Function Learning in Information Diffusion Networks ICML 2014
Active Learning and Best-Response Dynamics NIPS 2014
Shaping Social Activity by Incentivizing Users NIPS 2014
Scalable Kernel Methods via Doubly Stochastic Gradients NIPS 2014
Learning Time-Varying Coverage Functions NIPS 2014
Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem COLT 2014
Least Squares Revisited: Scalable Approaches for Multi-class Prediction ICML 2014
Nonparametric Estimation of Multi-View Latent Variable Models ICML 2014
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm ICML 2014
Hierarchical Tensor Decomposition of Latent Tree Graphical Models ICML 2013
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes AISTATS 2013
Uncover Topic-Sensitive Information Diffusion Networks AISTATS 2013
Unfolding Latent Tree Structures using 4th Order Tensors ICML 2013
Learning Triggering Kernels for Multi-dimensional Hawkes Processes ICML 2013
Scalable Influence Estimation in Continuous-Time Diffusion Networks NIPS 2013
Robust Low Rank Kernel Embeddings of Multivariate Distributions NIPS 2013
Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels JMLR 2013
Feature Selection via Dependence Maximization JMLR 2012
Learning Networks of Heterogeneous Influence NIPS 2012
Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks AISTATS 2011
Kernel Belief Propagation AISTATS 2011
Spectral Methods for Learning Multivariate Latent Tree Structure NIPS 2011
Kernel Embeddings of Latent Tree Graphical Models NIPS 2011
Kernel Bayes' Rule NIPS 2011
Multiscale Community Blockmodel for Network Exploration AISTATS 2011
Nonparametric Tree Graphical Models AISTATS 2010
Learning Nonlinear Dynamic Models from Non-sequenced Data AISTATS 2010
Time-Varying Dynamic Bayesian Networks NIPS 2009
Sparsistent Learning of Varying-coefficient Models with Structural Changes NIPS 2009
Kernel Measures of Independence for non-iid Data NIPS 2008
Kernelized Sorting NIPS 2008
Colored Maximum Variance Unfolding NIPS 2007
A Kernel Statistical Test of Independence NIPS 2007