371 citations · 1.3k across the 68 of their papers we have counts for
14 papers · 1 filter
Learning Survival Distributions with the Asymmetric Laplace Distribution
Deming Sheng, Ricardo Henao
Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have p…
An Effective Meaningful Way to Evaluate Survival Models
Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh +5
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by…
Neural Insights for Digital Marketing Content Design
Fanjie Kong, Yuan Li, Houssam Nassif +3
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement. However, creating successful marketing content is a manual and…
Flexible Triggering Kernels for Hawkes Process Modeling
Yamac Alican Isik, Connor Davis, Paidamoyo Chapfuwa +1
Recently proposed encoder-decoder structures for modeling Hawkes processes use transformer-inspired architectures, which encode the history of events via embeddings and self-attent…
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…
Wasserstein Contrastive Representation Distillation
Liqun Chen, Dong Wang, Zhe Gan +3
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compa…
Learning Autoencoders with Relational Regularization
Hongteng Xu, Dixin Luo, Ricardo Henao +2
A new algorithmic framework is proposed for learning autoencoders of data distributions. We minimize the discrepancy between the model and target distributions, with a \emph{relati…
Straight-Through Estimator as Projected Wasserstein Gradient Flow
Pengyu Cheng, Chang Liu, Chunyuan Li +3
The Straight-Through (ST) estimator is a widely used technique for back-propagating gradients through discrete random variables. However, this effective method lacks theoretical ju…
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…
Discriminative Clustering for Robust Unsupervised Domain Adaptation
Rui Wang, Guoyin Wang, Ricardo Henao
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source datase…
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Yunchen Pu, Shuyang Dai, Zhe Gan +5
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed m…
Adversarial Symmetric Variational Autoencoder
Yunchen Pu, Weiyao Wang, Ricardo Henao +4
A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: () from observed data fed thr…
VAE Learning via Stein Variational Gradient Descent
Yunchen Pu, Zhe Gan, Ricardo Henao +3
A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make para…
Semi-Supervised Kernel PCA
Christian Walder, Ricardo Henao, Morten Mørup +1
We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA,…