7 papers · 1 filter
Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
Gustavo Sutter, Hao Wang, Luis Ricardez-Sandoval +2
Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available. Multi-fid…
Deep Time-series Forecasting Needs Kernelized Moment Balancing
Licheng Pan, Hao Wang, Haocheng Yang +7
Deep time-series forecasting can be formulated as a distribution balancing problem aimed at aligning the distribution of the forecasts and ground truths. According to Imbens' crite…
DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting
Yuqi Li, Kuiye Ding, Chuanguang Yang +5
Time-series forecasting is fundamental across many domains, yet training accurate models often requires large-scale datasets and substantial computational resources. Dataset distil…
QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Models
Hao Wang, Baojun Ma
In real-world time series forecasting tasks, category information plays a pivotal role in capturing inherent data patterns. This paper introduces QKCV (Query-Key-Category-Value) at…
Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
Gustavo Sutter Pessurno de Carvalho, Mohammed Abdulrahman, Hao Wang +7
The optimization of expensive black-box functions is ubiquitous in science and engineering. A common solution to this problem is Bayesian optimization (BO), which is generally comp…
FTS: A Framework to Find a Faithful TimeSieve
Songning Lai, Ninghui Feng, Haochen Sui +5
The field of time series forecasting has garnered significant attention in recent years, prompting the development of advanced models like TimeSieve, which demonstrates impressive…