Le Song

Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs ACL 2025 Size-Generalizable RNA Structure Evaluation by Exploring Hierarchical Geometries ICLR 2025 MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training NeurIPS 2024 Optimistic Bayesian Optimization with Unknown Constraints ICLR 2024 Training Compute-Optimal Protein Language Models NeurIPS 2024 Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization NeurIPS 2023 XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images ICCV 2023 xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data NeurIPS 2023 Explaining Point Processes by Learning Interpretable Temporal Logic Rules ICLR 2022 GNN is a Counter? Revisiting GNN for Question Answering ICLR 2022 PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning ACL 2022 Provable Learning-based Algorithm For Sparse Recovery ICLR 2022 ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select EMNLP 2022 Spanning Tree-based Graph Generation for Molecules ICLR 2022 Uncovering the Structural Fairness in Graph Contrastive Learning NeurIPS 2022 A Biased Graph Neural Network Sampler with Near-Optimal Regret NeurIPS 2021 BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition ACL 2021 Locality Sensitive Teaching NeurIPS 2021 Molecule Optimization by Explainable Evolution ICLR 2021 Multi-task Learning of Order-Consistent Causal Graphs NeurIPS 2021 Orthogonal Over-Parameterized Training CVPR 2021 RoMA: Robust Model Adaptation for Offline Model-based Optimization NeurIPS 2021 Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning NeurIPS 2021 Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction AAAI 2020 Bandit Samplers for Training Graph Neural Networks NeurIPS 2020 Cost-Effective Incentive Allocation via Structured Counterfactual Inference AAAI 2020 Double Neural Counterfactual Regret Minimization ICLR 2020 Efficient Probabilistic Logic Reasoning with Graph Neural Networks ICLR 2020 GLAD: Learning Sparse Graph Recovery ICLR 2020 HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS ICLR 2020 Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees ICLR 2020 Learning To Stop While Learning To Predict ICML 2020 Learn to Explain Efficiently via Neural Logic Inductive Learning ICLR 2020 Question Directed Graph Attention Network for Numerical Reasoning over Text EMNLP 2020 Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020 Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search ICML 2020 RNA Secondary Structure Prediction By Learning Unrolled Algorithms ICLR 2020 Temporal Logic Point Processes ICML 2020 The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models NeurIPS 2020 Understanding Deep Architecture with Reasoning Layer NeurIPS 2020 Exponential Family Estimation via Adversarial Dynamics Embedding NeurIPS 2019 Generative Adversarial User Model for Reinforcement Learning Based Recommendation System ICML 2019 GeniePath: Graph Neural Networks with Adaptive Receptive Paths AAAI 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019 Language Modeling with Shared Grammar ACL 2019 Large Scale Evolving Graphs with Burst Detection IJCAI 2019 Latent Dirichlet Allocation for Internet Price War AAAI 2019 Learning a Meta-Solver for Syntax-Guided Program Synthesis ICLR 2019 L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data ICLR 2019 Meta Architecture Search NeurIPS 2019 Neural Similarity Learning NeurIPS 2019 Particle Flow Bayes’ Rule ICML 2019 Retrosynthesis Prediction with Conditional Graph Logic Network NeurIPS 2019 Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning NeurIPS 2019 Adversarial Attack on Graph Structured Data ICML 2018 A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop AISTATS 2018 Boosting the Actor with Dual Critic ICLR 2018 Coupled Variational Bayes via Optimization Embedding NeurIPS 2018 Decoupled Networks CVPR 2018 Iterative Learning With Open-Set Noisy Labels CVPR 2018 Learning Loop Invariants for Program Verification NeurIPS 2018 Learning Steady-States of Iterative Algorithms over Graphs ICML 2018 Learning Temporal Point Processes via Reinforcement Learning NeurIPS 2018 Learning to Explain: An Information-Theoretic Perspective on Model Interpretation ICML 2018 Learning towards Minimum Hyperspherical Energy NeurIPS 2018 Multi-scale Nystrom Method AISTATS 2018 SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation ICML 2018 Stochastic Training of Graph Convolutional Networks with Variance Reduction ICML 2018 Syntax-Directed Variational Autoencoder for Structured Data ICLR 2018 Towards Black-box Iterative Machine Teaching ICML 2018 COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution JMLR 2017 Deep Hyperspherical Learning NeurIPS 2017 Diverse Neural Network Learns True Target Functions AISTATS 2017 Fake News Mitigation via Point Process Based Intervention ICML 2017 Iterative Machine Teaching ICML 2017 Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs ICML 2017 Learning Combinatorial Optimization Algorithms over Graphs NeurIPS 2017 Learning from Conditional Distributions via Dual Embeddings AISTATS 2017 Linking Micro Event History to Macro Prediction in Point Process Models AISTATS 2017 On the Complexity of Learning Neural Networks NeurIPS 2017 Predicting User Activity Level In Point Processes With Mass Transport Equation NeurIPS 2017 Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks JMLR 2017 SphereFace: Deep Hypersphere Embedding for Face Recognition CVPR 2017 Stochastic Generative Hashing ICML 2017 Variational Policy for Guiding Point Processes ICML 2017 Wasserstein Learning of Deep Generative Point Process Models NeurIPS 2017 Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions NeurIPS 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 Isotonic Hawkes Processes ICML 2016 Multistage Campaigning in Social Networks NeurIPS 2016 Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016 The Nonparametric Kernel Bayes Smoother AISTATS 2016 A la Carte – Learning Fast Kernels AISTATS 2015 Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades AISTATS 2015 COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution NeurIPS 2015 Deep Fried Convnets ICCV 2015 Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression NeurIPS 2015 M-Statistic for Kernel Change-Point Detection NeurIPS 2015 Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients NeurIPS 2015 Time-Sensitive Recommendation From Recurrent User Activities NeurIPS 2015 Active Learning and Best-Response Dynamics NeurIPS 2014 Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm ICML 2014 Influence Function Learning in Information Diffusion Networks ICML 2014 Learning Time-Varying Coverage Functions NeurIPS 2014 Least Squares Revisited: Scalable Approaches for Multi-class Prediction ICML 2014 Nonparametric Estimation of Multi-View Latent Variable Models ICML 2014 Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem COLT 2014 Scalable Kernel Methods via Doubly Stochastic Gradients NeurIPS 2014 Shaping Social Activity by Incentivizing Users NeurIPS 2014 Hierarchical Tensor Decomposition of Latent Tree Graphical Models ICML 2013 Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels JMLR 2013 Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes AISTATS 2013 Learning Triggering Kernels for Multi-dimensional Hawkes Processes ICML 2013 Robust Low Rank Kernel Embeddings of Multivariate Distributions NeurIPS 2013 Scalable Influence Estimation in Continuous-Time Diffusion Networks NeurIPS 2013 Uncover Topic-Sensitive Information Diffusion Networks AISTATS 2013 Unfolding Latent Tree Structures using 4th Order Tensors ICML 2013 Feature Selection via Dependence Maximization JMLR 2012 Learning Networks of Heterogeneous Influence NeurIPS 2012 Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks AISTATS 2011 Kernel Bayes' Rule NeurIPS 2011 Kernel Belief Propagation AISTATS 2011 Kernel Embeddings of Latent Tree Graphical Models NeurIPS 2011 Multiscale Community Blockmodel for Network Exploration AISTATS 2011 Spectral Methods for Learning Multivariate Latent Tree Structure NeurIPS 2011 Learning Nonlinear Dynamic Models from Non-sequenced Data AISTATS 2010 Nonparametric Tree Graphical Models AISTATS 2010 Sparsistent Learning of Varying-coefficient Models with Structural Changes NeurIPS 2009 Time-Varying Dynamic Bayesian Networks NeurIPS 2009 Kernelized Sorting NeurIPS 2008 Kernel Measures of Independence for non-iid Data NeurIPS 2008 A Kernel Statistical Test of Independence NeurIPS 2007 Colored Maximum Variance Unfolding NeurIPS 2007 Relative Novelty Detection AISTATS 2005