Search Results for author: Yichen Zhu

Found 13 papers, 1 papers with code

Optimal market completion through financial derivatives with applications to volatility risk

no code implementations16 Feb 2022 Matt Davison, Marcos Escobar-Anel, Yichen Zhu

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models.

Derivatives-based portfolio decisions. An expected utility insight

no code implementations11 Jan 2022 Marcos Escobar-Anel, Matt Davison, Yichen Zhu

This paper challenges the use of stocks in portfolio construction, instead we demonstrate that Asian derivatives, straddles, or baskets could be more convenient substitutes.

Make A Long Image Short: Adaptive Token Length for Vision Transformers

no code implementations3 Dec 2021 Yichen Zhu, Yuqin Zhu, Jie Du, Yi Wang, Zhicai Ou, Feifei Feng, Jian Tang

The TLA enables the ReViT to process the image with the minimum sufficient number of tokens during inference.

Action Recognition Image Classification

Training BatchNorm Only in Neural Architecture Search and Beyond

no code implementations1 Dec 2021 Yichen Zhu, Jie Du, Yuqin Zhu, Yi Wang, Zhicai Ou, Feifei Feng, Jian Tang

Critically, there is no effort to understand 1) why training BatchNorm only can find the perform-well architectures with the reduced supernet-training time, and 2) what is the difference between the train-BN-only supernet and the standard-train supernet.

Fairness Neural Architecture Search

Networked Time Series Prediction with Incomplete Data

no code implementations5 Oct 2021 Yichen Zhu, Mengtian Zhang, Bo Jiang, Haiming Jin, Jianqiang Huang, Xinbing Wang

We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future.

Time Series Time Series Prediction

Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher

no code implementations ICCV 2021 Yichen Zhu, Yi Wang

We formulate the knowledge distillation as a multi-task learning problem so that the teacher transfers knowledge to the student only if the student can benefit from learning such knowledge.

Knowledge Distillation Multi-Task Learning +2

Classification Trees for Imbalanced and Sparse Data: Surface-to-Volume Regularization

no code implementations26 Apr 2020 Yichen Zhu, Cheng Li, David B. Dunson

When data are limited in one or more of the classes, the estimated decision boundaries are often irregularly shaped due to the limited sample size, leading to poor generalization error.

General Classification

VAENAS: Sampling Matters in Neural Architecture Search

no code implementations25 Sep 2019 Shizheng Qin, Yichen Zhu, Pengfei Hou, Xiangyu Zhang, Wenqiang Zhang, Jian Sun

In this paper, we propose a learnable sampling module based on variational auto-encoder (VAE) for neural architecture search (NAS), named as VAENAS, which can be easily embedded into existing weight sharing NAS framework, e. g., one-shot approach and gradient-based approach, and significantly improve the performance of searching results.

Neural Architecture Search

Resizable Neural Networks

no code implementations25 Sep 2019 Yichen Zhu, Xiangyu Zhang, Tong Yang, Jian Sun

We introduce the adaptive resizable networks as dynamic networks, which further improve the performance with less computational cost via data-dependent inference.

Data Augmentation Neural Architecture Search

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

1 code implementation13 May 2019 Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Wei-Nan Zhang, Yong Yu, Haiming Jin, Zhenhui Li

The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.

Multi-agent Reinforcement Learning reinforcement-learning

Nonoverlap-Promoting Variable Selection

no code implementations ICML 2018 Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing

Variable selection is a classic problem in machine learning (ML), widely used to find important explanatory factors, and improve generalization performance and interpretability of ML models.

Variable Selection

Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis

no code implementations ICML 2018 Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing

In this paper, we address these three issues by (1) seeking convex relaxations of the original nonconvex problems so that the global optimal is guaranteed to be achievable; (2) providing a formal analysis on OPR's capability of promoting balancedness; (3) providing a theoretical analysis that directly reveals the relationship between OPR and generalization performance.

Metric Learning

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