47 citations · 194 across the 36 of their papers we have counts for
14 papers · 1 filter
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search
Yifei Zhang, Xu Yang, Xiao Yang +8
LLM-based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient-free optimization that uses scalar validation scores to rank candidate…
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
Panqi Chen, Yifan Sun, Lei Cheng +6
Modeling and reconstructing multidimensional physical dynamics from sparse and off-grid observations presents a fundamental challenge in scientific research. Recently, diffusion-ba…
Functional Complexity-adaptive Temporal Tensor Decomposition
Panqi Chen, Lei Cheng, Jianlong Li +4
Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning low-rank factors to represent high-order interactions. While recent works on temporal te…
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
Jianming Pan, Zeqi Ye, Xiao Yang +4
Data-driven decision-making processes increasingly utilize end-to-end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in ce…
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
Xinyao Fan, Yueying Wu, Chang Xu +3
Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However,…
Learning Differential Operators for Interpretable Time Series Modeling
Yingtao Luo, Chang Xu, Yang Liu +3
Modeling sequential patterns from data is at the core of various time series forecasting tasks. Deep learning models have greatly outperformed many traditional models, but these bl…
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Zhengyu Yang, Kan Ren, Xufang Luo +5
It is challenging for reinforcement learning (RL) algorithms to succeed in real-world applications like financial trading and logistic system due to the noisy observation and envir…
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
Wendi Li, Xiao Yang, Weiqing Liu +2
In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data di…
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
Wentao Xu, Weiqing Liu, Jiang Bian +2
The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weathe…
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
Hengxu Lin, Dong Zhou, Weiqing Liu +1
Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capa…
Model Complexity of Deep Learning: A Survey
Xia Hu, Lingyang Chu, Jian Pei +2
Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complex…
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
Hongshun Tang, Lijun Wu, Weiqing Liu +1
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field. Though deep learning methods have achieved promising resu…
Learning to Reweight with Deep Interactions
Yang Fan, Yingce Xia, Lijun Wu +5
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real t…
Measuring Model Complexity of Neural Networks with Curve Activation Functions
Xia Hu, Weiqing Liu, Jiang Bian +1
It is fundamental to measure model complexity of deep neural networks. The existing literature on model complexity mainly focuses on neural networks with piecewise linear activatio…