73 citations · 208 across the 22 of their papers we have counts for
23 papers
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
Sidi Lu, Wenbo Zhao, Chenyang Tao +4
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models. It is designed to avoid catastrophic forgetting while achieving gua…
Unsupervised Melody-Guided Lyrics Generation
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby +7
Automatic song writing is a topic of significant practical interest. However, its research is largely hindered by the lack of training data due to copyright concerns and challenged…
Unsupervised Melody-to-Lyric Generation
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby +8
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than uncon…
PLACES: Prompting Language Models for Social Conversation Synthesis
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising directio…
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier,…
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping
Junya Chen, Sijia Wang, Lawrence Carin +1
Distributed learning has become an integral tool for scaling up machine learning and addressing the growing need for data privacy. Although more robust to the network topology, dec…
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
Junya Chen, Zhe Gan, Xuan Li +10
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years. However, these contrastive schemes are notoriously resource de…
Gradient Importance Learning for Incomplete Observations
Qitong Gao, Dong Wang, Joshua D. Amason +6
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assump…
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Dong Wang, Yuewei Yang, Chenyang Tao +5
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts. However, ad-hoc visual explanations of…
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Zidi Xiu, Junya Chen, Ricardo Henao +3
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of pr…
Double Robust Representation Learning for Counterfactual Prediction
Shuxi Zeng, Serge Assaad, Chenyang Tao +3
Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators with high-dimensional data in…
Improving Text Generation with Student-Forcing Optimal Transport
Guoyin Wang, Chunyuan Li, Jianqiao Li +10
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, how…
Counterfactual Representation Learning with Balancing Weights
Serge Assaad, Shuxi Zeng, Chenyang Tao +5
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation lea…
Variational Disentanglement for Rare Event Modeling
Zidi Xiu, Chenyang Tao, Michael Gao +3
Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical d…
Weakly supervised cross-domain alignment with optimal transport
Siyang Yuan, Ke Bai, Liqun Chen +6
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural langu…
APo-VAE: Text Generation in Hyperbolic Space
Shuyang Dai, Zhe Gan, Yu Cheng +3
Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings…
Variational Learning of Individual Survival Distributions
Zidi Xiu, Chenyang Tao, Benjamin A. Goldstein +1
The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making. P…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Survival Function Matching for Calibrated Time-to-Event Predictions
Paidamoyo Chapfuwa, Chenyang Tao, Lawrence Carin +1
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused prim…
Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen, Yizhe Zhang, Ruiyi Zhang +7
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word…
Adversarial Text Generation via Feature-Mover's Distance
Liqun Chen, Shuyang Dai, Chenyang Tao +5
Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text…
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li +4
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support…