research
∙
04/17/2023
Long-term Forecasting with TiDE: Time-series Dense Encoder
Recent work has shown that simple linear models can outperform several T...
share
research
∙
06/09/2022
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
We study the problem of learning generalized linear models under adversa...
research
∙
04/21/2022
A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting
Hierarchical forecasting is a key problem in many practical multivariate...
research
∙
06/18/2021
On the benefits of maximum likelihood estimation for Regression and Forecasting
We advocate for a practical Maximum Likelihood Estimation (MLE) approach...
research
∙
06/14/2021
Hierarchically Regularized Deep Forecasting
Hierarchical forecasting is a key problem in many practical multivariate...
research
∙
03/29/2021
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Can deep learning solve multiple tasks simultaneously, even when they ar...
research
∙
12/24/2020
Upper Confidence Bounds for Combining Stochastic Bandits
We provide a simple method to combine stochastic bandit algorithms. Our ...
research
∙
05/15/2020
Learning the gravitational force law and other analytic functions
Large neural network models have been successful in learning functions o...
research
∙
04/08/2019
On the Learnability of Deep Random Networks
In this paper we study the learnability of deep random networks from bot...
research
∙
02/08/2018
Minimizing Latency in Online Ride and Delivery Services
Motivated by the popularity of online ride and delivery services, we stu...
research
∙
02/19/2011