This work aims to study off-policy evaluation (OPE) under scenarios wher...
Service discovery is a crucial component in today's massively distribute...
Off-Policy evaluation (OPE) is concerned with evaluating a new target po...
Motivated by the human-machine interaction such as training chatbots for...
Transformer-based sequential recommenders are very powerful for capturin...
In many important applications of precision medicine, the outcome of int...
Data often has many semantic attributes that are causally associated wit...
The vast majority of literature on evaluating the significance of a trea...
The Mandarin Chinese language is known to be strongly influenced by a ri...
Dysarthric speech recognition is a challenging task due to acoustic
vari...
Today's cyber-world is vastly multivariate. Metrics collected at extreme...
The U.S. electrical grid has undergone substantial transformation with
i...
ℓ_1-penalized quantile regression is widely used for analyzing
high-dime...
Adversarial representation learning aims to learn data representations f...
Modeling inter-dependencies between time-series is the key to achieve hi...
This paper develops new tools to quantify uncertainty in optimal decisio...
This paper theoretically investigates the following empirical phenomenon...
In recent years, compressed sensing (CS) based image coding has become a...
The electric power grid is a critical societal resource connecting multi...
A key task for speech recognition systems is to reduce the mismatch betw...
Training a code-switching end-to-end automatic speech recognition (ASR) ...
Recently, there has been growing interest in estimating optimal treatmen...
Penalized (or regularized) regression, as represented by Lasso and its
v...
We consider a heteroscedastic regression model in which some of the
regr...
Data of different modalities generally convey complimentary but heteroge...
Detecting complex events in a large video collection crawled from video
...