← Authors

Hanjun Dai

59 papers · 2014–2025 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (6) πŸƒ Academic Marathon (11) 🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (15) 🏠 Conference Loyalist (20) 🀝 Dynamic Duo (27) πŸ‘‘ Triple Crown πŸ’Ž Century Club (59) πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ”₯ Unstoppable (12) ❓ The Questioner πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (209)

Conferences

NIPS (20) ICML (16) ICLR (12) AISTATS (5) EMNLP (4) ACL (1) COLING (1)

Top co-authors

Bo Dai (27) Dale Schuurmans (21) Le Song (19) Haoran Sun (7) Niao He (5) Ruoxi Sun (5) Charles Sutton (5) Tomas Pfister (4) Rishabh Singh (4) Azade Nova (4)

Keywords

graph neural network (8) variational inference (4) markov chain monte carlo (4) large language model (4) combinatorial optimization (4) reinforcement learning (4) energy-based model (3) neural network (3) in-context learning (3) transfer learning (3) entity retrieval (2) knowledge graph (2) graphical model (2) discrete sampling (2) latent variable model (2) energy based model (2) deep learning (2) bayesian inference (2) zero-shot learning (2) multimodal learning (2)

Papers

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF ICLR 2025
Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM Alignment AISTATS 2025
UQE: A Query Engine for Unstructured Databases NIPS 2024
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models ICML 2024
Gradient-Free Structured Pruning with Unlabeled Data ICML 2023
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets NIPS 2023
Video Timeline Modeling For News Story Understanding NIPS 2023
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas NIPS 2023
DISCS: A Benchmark for Discrete Sampling NIPS 2023
Better Zero-Shot Reasoning with Self-Adaptive Prompting ACL 2023
Any-scale Balanced Samplers for Discrete Space ICLR 2023
Score-based Continuous-time Discrete Diffusion Models ICLR 2023
Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023
Learning to Optimize with Stochastic Dominance Constraints AISTATS 2023
Universal Self-Adaptive Prompting EMNLP 2023
DocumentNet: Bridging the Data Gap in Document Pre-training EMNLP 2023
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data EMNLP 2023
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval EMNLP 2023
Revisiting Sampling for Combinatorial Optimization ICML 2023
Learning Universal Policies via Text-Guided Video Generation NIPS 2023
Does GNN Pretraining Help Molecular Representation? NIPS 2022
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces NIPS 2022
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation ICLR 2022
Neural Stochastic Dual Dynamic Programming ICLR 2022
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization ICML 2022
Path Auxiliary Proposal for MCMC in Discrete Space ICLR 2022
CrossBeam: Learning to Search in Bottom-Up Program Synthesis ICLR 2022
Combiner: Full Attention Transformer with Sparse Computation Cost NIPS 2021
SpreadsheetCoder: Formula Prediction from Semi-structured Context ICML 2021
Towards understanding retrosynthesis by energy-based models NIPS 2021
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs ICML 2021
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration ICLR 2021
Molecule Optimization by Explainable Evolution ICLR 2021
Scalable Deep Generative Modeling for Sparse Graphs ICML 2020
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration NIPS 2020
Differentiable Top-k with Optimal Transport NIPS 2020
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS ICLR 2020
Learning To Stop While Learning To Predict ICML 2020
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search ICML 2020
Energy-Based Processes for Exchangeable Data ICML 2020
Particle Flow Bayes’ Rule ICML 2019
CompILE: Compositional Imitation Learning and Execution ICML 2019
Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019
Retrosynthesis Prediction with Conditional Graph Logic Network NIPS 2019
Exponential Family Estimation via Adversarial Dynamics Embedding NIPS 2019
Learning a Meta-Solver for Syntax-Guided Program Synthesis ICLR 2019
Learning Transferable Graph Exploration NIPS 2019
Learning Loop Invariants for Program Verification NIPS 2018
Learning Steady-States of Iterative Algorithms over Graphs ICML 2018
Adversarial Attack on Graph Structured Data ICML 2018
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification NIPS 2018
Coupled Variational Bayes via Optimization Embedding NIPS 2018
Syntax-Directed Variational Autoencoder for Structured Data ICLR 2018
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs ICML 2017
Learning Combinatorial Optimization Algorithms over Graphs NIPS 2017
Discriminative Embeddings of Latent Variable Models for Structured Data ICML 2016
Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016
M-Statistic for Kernel Change-Point Detection NIPS 2015
A Probabilistic Model for Learning Multi-Prototype Word Embeddings COLING 2014