Changyou Chen

ALPHA: Action-Based Learning for Pluralistic Human Alignment in Large Language Models AAAI 2026 Small Agents, Big Gains: Journey-Aware and Critic-Guided Simulation for Long-Horizon Shopping Dialogues ACL 2026 Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models CVPR 2026 A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation COLING 2025 Cross-Modal Feature Alignment and MMD Improve Robustness of Prompt Tuning WACV 2025 Long-Term Ad Memorability: Understanding & Generating Memorable Ads WACV 2025 Measuring And Improving Engagement of Text-to-Image Generation Models ICLR 2025 Multimodal LLMs as Customized Reward Models for Text-to-Image Generation ICCV 2025 Multi-Modal Multi-Task Unified Embedding Model (M3T-UEM): A Task-Adaptive Representation Learning Framework ICCV 2025 SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding ICLR 2025 Teaching Human Behavior Improves Content Understanding Abilities Of VLMs ICLR 2025 A probability contrastive learning framework for 3D molecular representation learning NeurIPS 2024 AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning ICLR 2024 Diffusion Models for Multi-Task Generative Modeling ICLR 2024 Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior ICLR 2024 TextLap: Customizing Language Models for Text-to-Layout Planning EMNLP 2024 Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints ICLR 2024 TRINS: Towards Multimodal Language Models that Can Read CVPR 2024 ALCAP: Alignment-Augmented Music Captioner EMNLP 2023 AUC Maximization for Low-Resource Named Entity Recognition AAAI 2023 A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot EMNLP 2023 Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction ICML 2023 Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels NeurIPS 2023 Learning Unnormalized Statistical Models via Compositional Optimization ICML 2023 Persuasion Strategies in Advertisements AAAI 2023 ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models ACL 2023 Shifted Diffusion for Text-to-Image Generation CVPR 2023 Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning CVPR 2023 Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios EMNLP 2022 MINIMAL: Mining Models for Universal Adversarial Triggers AAAI 2022 Rethinking Deep Face Restoration CVPR 2022 TiGAN: Text-Based Interactive Image Generation and Manipulation AAAI 2022 Towards Language-Free Training for Text-to-Image Generation CVPR 2022 Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees AAAI 2022 Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective NeurIPS 2022 Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval ACL 2021 Meta-Learning with Neural Tangent Kernels ICLR 2021 MixKD: Towards Efficient Distillation of Large-scale Language Models ICLR 2021 Rethinking Sentiment Style Transfer EMNLP 2021 Unpaired Image-to-Image Translation via Latent Energy Transport CVPR 2021 Unsupervised Hashing with Contrastive Information Bottleneck IJCAI 2021 Bayesian Meta Sampling for Fast Uncertainty Adaptation ICLR 2020 Bayesian Multi-type Mean Field Multi-agent Imitation Learning NeurIPS 2020 Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning ICLR 2020 Feature Quantization Improves GAN Training ICML 2020 Generative Semantic Hashing Enhanced via Boltzmann Machines ACL 2020 Improving Adversarial Text Generation by Modeling the Distant Future ACL 2020 Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions AAAI 2020 Learning Manifold Implicitly via Explicit Heat-Kernel Learning NeurIPS 2020 Nested-Wasserstein Self-Imitation Learning for Sequence Generation AISTATS 2020 Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference EMNLP 2020 Semantic Matching for Sequence-to-Sequence Learning EMNLP 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Structure-Aware Human-Action Generation ECCV 2020 Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints ACL 2020 Variance Reduction in Stochastic Particle-Optimization Sampling ICML 2020 Variational Adversarial Kernel Learned Imitation Learning AAAI 2020 Adversarial Learning of a Sampler Based on an Unnormalized Distribution AISTATS 2019 Bayesian Uncertainty Matching for Unsupervised Domain Adaptation IJCAI 2019 Certified Adversarial Robustness with Additive Noise NeurIPS 2019 Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019 Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm IJCAI 2019 Differentially Private Empirical Risk Minimization with Non-convex Loss Functions ICML 2019 Distributionally Adversarial Attack AAAI 2019 Document Hashing with Mixture-Prior Generative Models EMNLP 2019 Implicit Deep Latent Variable Models for Text Generation EMNLP 2019 Improving Sequence-to-Sequence Learning via Optimal Transport ICLR 2019 On Connecting Stochastic Gradient MCMC and Differential Privacy AISTATS 2019 PointCloud Saliency Maps ICCV 2019 Scalable Thompson Sampling via Optimal Transport AISTATS 2019 Self-Adversarially Learned Bayesian Sampling AAAI 2019 Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning NeurIPS 2019 Topic-Guided Variational Auto-Encoder for Text Generation NAACL 2019 Continuous-Time Flows for Efficient Inference and Density Estimation ICML 2018 Learning Structural Weight Uncertainty for Sequential Decision-Making AISTATS 2018 Policy Optimization as Wasserstein Gradient Flows ICML 2018 Symmetric Variational Autoencoder and Connections to Adversarial Learning AISTATS 2018 ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching NeurIPS 2017 Learning Structured Weight Uncertainty in Bayesian Neural Networks AISTATS 2017 Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling ACL 2017 Stochastic Gradient Monomial Gamma Sampler ICML 2017 Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization AISTATS 2016 Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016 Nonlinear Statistical Learning with Truncated Gaussian Graphical Models ICML 2016 Stochastic Gradient MCMC with Stale Gradients NeurIPS 2016 Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NeurIPS 2016 On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators NeurIPS 2015 Scalable Deep Poisson Factor Analysis for Topic Modeling ICML 2015 Bayesian Sampling Using Stochastic Gradient Thermostats NeurIPS 2014 Robust Bayesian Max-Margin Clustering NeurIPS 2014 Dependent Normalized Random Measures ICML 2013