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05/25/2023
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Ensembling is among the most popular tools in machine learning (ML) due ...
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03/14/2023
Testing Causality for High Dimensional Data
Determining causal relationship between high dimensional observations ar...
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02/23/2023
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
The world is not static: This causes real-world time series to change ov...
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12/15/2022
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Transformer-based models have gained large popularity and demonstrated p...
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07/19/2022
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
As deep learning models have gradually become the main workhorse of time...
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02/24/2022
Robust Probabilistic Time Series Forecasting
Probabilistic time series forecasting has played critical role in decisi...
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02/23/2022
Multivariate Quantile Function Forecaster
We propose Multivariate Quantile Function Forecaster (MQF^2), a global p...
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11/12/2021
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Quantile regression is an effective technique to quantify uncertainty, f...
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02/13/2021
Attention-based Domain Adaptation for Time Series Forecasting
Recent years have witnessed deep neural networks gaining increasing popu...
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07/13/2020
Structured Policy Iteration for Linear Quadratic Regulator
Linear quadratic regulator (LQR) is one of the most popular frameworks t...
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10/15/2019
