166 citations · 625 across the 30 of their papers we have counts for
11 papers · 1 filter
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
Guojun Xiong, Gang Yan, Shiqiang Wang +1
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular…
Generative Table Pre-training Empowers Models for Tabular Prediction
Tianping Zhang, Shaowen Wang, Shuicheng Yan +2
Recently, the topic of table pre-training has attracted considerable research interest. However, how to employ table pre-training to boost the performance of tabular prediction rem…
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search
Clemens JS Schaefer, Elfie Guo, Caitlin Stanton +7
Serving large-scale machine learning (ML) models efficiently and with low latency has become challenging owing to increasing model size and complexity. Quantizing models can simult…
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models
Qiong Wu, Jian Li, Zhenming Liu +2
This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or int…
Knowledge-Guided Exploration in Deep Reinforcement Learning
Sahisnu Mazumder, Bing Liu, Shuai Wang +4
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP). Tw…
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization
Zhize Li, Jian Li
We propose and analyze several stochastic gradient algorithms for finding stationary points or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and onlin…
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability
Zhouzi Li, Zixuan Wang, Jian Li
Recent findings (e.g., arXiv:2103.00065) demonstrate that modern neural networks trained by full-batch gradient descent typically enter a regime called Edge of Stability (EOS). In…
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu, Jing Lu, Xiwei Zhao +8
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest…
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
Robert David, Jared Duke, Advait Jain +10
Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges befo…
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Chuheng Zhang, Yuanqi Li, Xi Chen +3
Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superi…
Loosely Coupled Federated Learning Over Generative Models
Shaoming Song, Yunfeng Shao, Jian Li
Federated learning (FL) was proposed to achieve collaborative machine learning among various clients without uploading private data. However, due to model aggregation strategies, e…