Lawrence Carin

Graph Transformers Dream of Electric Flow ICLR 2025 On Understanding Attention-Based In-Context Learning for Categorical Data ICML 2025 LangMark: A Multilingual Dataset for Automatic Post-Editing ACL 2025 Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning WACV 2024 Pushing the Efficiency Limit Using Structured Sparse Convolutions WACV 2023 Open World Classification with Adaptive Negative Samples EMNLP 2022 What Makes Good In-Context Examples for GPT-3? ACL 2022 Gradient Importance Learning for Incomplete Observations ICLR 2022 Learning to Weight Filter Groups for Robust Classification WACV 2022 Capturing actionable dynamics with structured latent ordinary differential equations UAI 2022 Zero-Shot Recognition via Optimal Transport WACV 2021 Counterfactual Representation Learning with Balancing Weights AISTATS 2021 FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders ICLR 2021 Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning ICLR 2021 GO Hessian for Expectation-Based Objectives AAAI 2021 Learning Graphons via Structured Gromov-Wasserstein Barycenters AAAI 2021 MixKD: Towards Efficient Distillation of Large-scale Language Models ICLR 2021 Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer NIPS 2021 APo-VAE: Text Generation in Hyperbolic Space NAACL 2021 Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors AISTATS 2021 Learning Task Sampling Policy for Multitask Learning EMNLP 2021 SpanPredict: Extraction of Predictive Document Spans with Neural Attention NAACL 2021 CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks NIPS 2021 Wasserstein Contrastive Representation Distillation CVPR 2021 Efficient Feature Transformations for Discriminative and Generative Continual Learning CVPR 2021 Learning Autoencoders with Relational Regularization ICML 2020 CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information ICML 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Nested-Wasserstein Self-Imitation Learning for Sequence Generation AISTATS 2020 RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering ICLR 2020 Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning EMNLP 2020 Semantic Matching for Sequence-to-Sequence Learning EMNLP 2020 Improving Text Generation with Student-Forcing Optimal Transport EMNLP 2020 Methods for Numeracy-Preserving Word Embeddings EMNLP 2020 An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets EMNLP 2020 Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training CVPR 2020 Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction CVPR 2020 Students Need More Attention: BERT-based Attention Model for Small Data with Application to Automatic Patient Message Triage MLHC 2020 AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning NIPS 2020 Calibrating CNNs for Lifelong Learning NIPS 2020 GAN Memory with No Forgetting NIPS 2020 Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability NIPS 2020 Reconsidering Generative Objectives For Counterfactual Reasoning NIPS 2020 Graph-Driven Generative Models for Heterogeneous Multi-Task Learning AAAI 2020 Bridging Maximum Likelihood and Adversarial Learning via α-Divergence AAAI 2020 Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning AAAI 2020 Dynamic Embedding on Textual Networks via a Gaussian Process AAAI 2020 Complementary Auxiliary Classifiers for Label-Conditional Text Generation AAAI 2020 Graph Optimal Transport for Cross-Domain Alignment ICML 2020 Transferable Perturbations of Deep Feature Distributions ICLR 2020 Improving Adversarial Text Generation by Modeling the Distant Future ACL 2020 Improving Disentangled Text Representation Learning with Information-Theoretic Guidance ACL 2020 On Leveraging Pretrained GANs for Generation with Limited Data ICML 2020 An End-to-End Generative Architecture for Paraphrase Generation EMNLP 2019 Improving Sequence-to-Sequence Learning via Optimal Transport ICLR 2019 Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019 Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing NAACL 2019 Learning Compressed Sentence Representations for On-Device Text Processing ACL 2019 Syntax-Infused Variational Autoencoder for Text Generation ACL 2019 Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models ACL 2019 StoryGAN: A Sequential Conditional GAN for Story Visualization CVPR 2019 Revisiting the Softmax Bellman Operator: New Benefits and New Perspective ICML 2019 Topic-Guided Variational Auto-Encoder for Text Generation NAACL 2019 Improving Textual Network Embedding with Global Attention via Optimal Transport ACL 2019 Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images MLHC 2019 Ouroboros: On Accelerating Training of Transformer-Based Language Models NIPS 2019 Certified Adversarial Robustness with Additive Noise NIPS 2019 Improving Textual Network Learning with Variational Homophilic Embeddings NIPS 2019 On Fenchel Mini-Max Learning NIPS 2019 Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching NIPS 2019 Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods NIPS 2019 An End-to-End Generative Architecture for Paraphrase Generation IJCNLP 2019 GO Gradient for Expectation-Based Objectives ICLR 2019 Adversarial Learning of a Sampler Based on an Unnormalized Distribution AISTATS 2019 Scalable Thompson Sampling via Optimal Transport AISTATS 2019 On Connecting Stochastic Gradient MCMC and Differential Privacy AISTATS 2019 Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms ACL 2018 Policy Optimization as Wasserstein Gradient Flows ICML 2018 Learning Registered Point Processes from Idiosyncratic Observations ICML 2018 Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model MLHC 2018 Multi-Label Learning from Medical Plain Text with Convolutional Residual Models MLHC 2018 Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes IJCAI 2018 Adversarial Text Generation via Feature-Mover's Distance NIPS 2018 Diffusion Maps for Textual Network Embedding NIPS 2018 Distilled Wasserstein Learning for Word Embedding and Topic Modeling NIPS 2018 NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing ACL 2018 Joint Embedding of Words and Labels for Text Classification ACL 2018 Topic Compositional Neural Language Model AISTATS 2018 Benefits from Superposed Hawkes Processes AISTATS 2018 Symmetric Variational Autoencoder and Connections to Adversarial Learning AISTATS 2018 Learning Structural Weight Uncertainty for Sequential Decision-Making AISTATS 2018 Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration CVPR 2018 Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment EMNLP 2018 Learning Context-Sensitive Convolutional Filters for Text Processing EMNLP 2018 Adversarial Feature Matching for Text Generation ICML 2017 Adversarial Symmetric Variational Autoencoder NIPS 2017 Deconvolutional Paragraph Representation Learning NIPS 2017 VAE Learning via Stein Variational Gradient Descent NIPS 2017 A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks NIPS 2017 Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling ACL 2017 Semantic Compositional Networks for Visual Captioning CVPR 2017 An inner-loop free solution to inverse problems using deep neural networks NIPS 2017 Triangle Generative Adversarial Networks NIPS 2017 Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis AISTATS 2017 Learning Structured Weight Uncertainty in Bayesian Neural Networks AISTATS 2017 ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching NIPS 2017 Scalable Model Selection for Belief Networks NIPS 2017 Deep Generative Models for Relational Data with Side Information ICML 2017 Targeting EEG/LFP Synchrony with Neural Nets NIPS 2017 Cross-Spectral Factor Analysis NIPS 2017 Learning Generic Sentence Representations Using Convolutional Neural Networks EMNLP 2017 Stochastic Gradient Monomial Gamma Sampler ICML 2017 Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization AISTATS 2016 Topic-Based Embeddings for Learning from Large Knowledge Graphs AISTATS 2016 Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information AISTATS 2016 Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization AISTATS 2016 Variational Gaussian Copula Inference AISTATS 2016 A Deep Generative Deconvolutional Image Model AISTATS 2016 Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016 Factored Temporal Sigmoid Belief Networks for Sequence Learning ICML 2016 Variational Autoencoder for Deep Learning of Images, Labels and Captions NIPS 2016 Stochastic Gradient MCMC with Stale Gradients NIPS 2016 Linear Feature Encoding for Reinforcement Learning NIPS 2016 Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NIPS 2016 Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks IJCAI 2016 Electronic Health Record Analysis via Deep Poisson Factor Models JMLR 2016 Nonlinear Statistical Learning with Truncated Gaussian Graphical Models ICML 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016 Scalable Probabilistic Tensor Factorization for Binary and Count Data IJCAI 2015 GP Kernels for Cross-Spectrum Analysis NIPS 2015 Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings NIPS 2015 Deep Temporal Sigmoid Belief Networks for Sequence Modeling NIPS 2015 On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators NIPS 2015 Deep Poisson Factor Modeling NIPS 2015 Preconditioned Spectral Descent for Deep Learning NIPS 2015 Stochastic Spectral Descent for Restricted Boltzmann Machines AISTATS 2015 Learning Deep Sigmoid Belief Networks with Data Augmentation AISTATS 2015 Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood ICML 2015 Scalable Deep Poisson Factor Analysis for Topic Modeling ICML 2015 A Multitask Point Process Predictive Model ICML 2015 Stick-Breaking Policy Learning in Dec-POMDPs IJCAI 2015 Nonlinear Information-Theoretic Compressive Measurement Design ICML 2014 Modeling Correlated Arrival Events with Latent Semi-Markov Processes ICML 2014 Low-Cost Compressive Sensing for Color Video and Depth CVPR 2014 Multi-Shot Imaging: Joint Alignment, Deblurring and Resolution-Enhancement CVPR 2014 Latent Gaussian Models for Topic Modeling AISTATS 2014 Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling NIPS 2014 On the relations of LFPs & Neural Spike Trains NIPS 2014 Analysis of Brain States from Multi-Region LFP Time-Series NIPS 2014 Compressive Sensing of Signals from a GMM with Sparse Precision Matrices NIPS 2014 Dynamic Rank Factor Model for Text Streams NIPS 2014 Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors ICML 2014 Online Expectation Maximization for Reinforcement Learning in POMDPs IJCAI 2013 Real-Time Inference for a Gamma Process Model of Neural Spiking NIPS 2013 Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture NIPS 2013 Integrated Non-Factorized Variational Inference NIPS 2013 Exploring the Mind: Integrating Questionnaires and fMRI ICML 2013 Designed Measurements for Vector Count Data NIPS 2013 Joint Modeling of a Matrix with Associated Text via Latent Binary Features NIPS 2012 Beta-Negative Binomial Process and Poisson Factor Analysis AISTATS 2012 Augment-and-Conquer Negative Binomial Processes NIPS 2012 The Kernel Beta Process NIPS 2011 Dependent Hierarchical Beta Process for Image Interpolation and Denoising AISTATS 2011 On the Analysis of Multi-Channel Neural Spike Data NIPS 2011 Logistic Stick-Breaking Process JMLR 2011 Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices NIPS 2011 Classification with Incomplete Data Using Dirichlet Process Priors JMLR 2010 Joint Analysis of Time-Evolving Binary Matrices and Associated Documents NIPS 2010 Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations NIPS 2009 A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation NIPS 2009 Multi-task Reinforcement Learning in Partially Observable Stochastic Environments JMLR 2009 Learning to Explore and Exploit in POMDPs NIPS 2009 Multi-Task Learning for Classification with Dirichlet Process Priors JMLR 2007 Semi-Supervised Multitask Learning NIPS 2007