← Authors

Lawrence Carin

174 papers · 2007–2025 · 15 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (37) 🐝 Cross-Pollinator (11) πŸƒ Academic Marathon (18) 🌍 Conference Polyglot (15) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (9) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (13) 🏠 Conference Loyalist (55) πŸ† Keyword Champion (3) πŸ”¬ Deep Specialist (13) 🀝 Dynamic Duo (41) πŸ† Grand Slam πŸ‘‘ Triple Crown 🌱 Topic Pioneer ❓ The Questioner ⚑ Prolific Year (28) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (182) πŸš€ Conference Pioneer πŸ’Ž Century Club (174) πŸ”₯ Unstoppable (17)

Conferences

NIPS (55) AISTATS (25) ICML (20) ACL (12) EMNLP (11) CVPR (10) ICLR (9) AAAI (8) IJCAI (5) JMLR (5) MLHC (4) NAACL (4) WACV (4) IJCNLP (1) UAI (1)

Top co-authors

Ricardo Henao (41) Zhe Gan (32) Changyou Chen (26) Chunyuan Li (24) Dinghan Shen (23) Guoyin Wang (21) Yizhe Zhang (18) Wenlin Wang (18) Liqun Chen (17) Xuejun Liao (14)

Research topics

Education (1)

Keywords

variational inference (21) bayesian inference (20) generative model (16) markov chain monte carlo (13) optimal transport (11) convolutional neural network (11) variational autoencoder (11) gibbs sampling (10) topic modeling (10) text generation (9) gaussian process (8) dirichlet process (8) nonparametric bayesian (7) generative adversarial network (7) topic model (7) adversarial learning (6) continual learning (6) bayesian nonparametrics (6) compressive sensing (6) reinforcement learning (6)

Papers

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 Ξ±-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