Search Results for author:
Found 13 papers, 1 papers with code
Optimal market completion through financial derivatives with applications to volatility risk
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
This paper challenges the use of stocks in portfolio construction, instead we demonstrate that Asian derivatives, straddles, or baskets could be more convenient substitutes.
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
Make A Long Image Short: Adaptive Token Length for Vision Transformers
The TLA enables the ReViT to process the image with the minimum sufficient number of tokens during inference.
Training BatchNorm Only in Neural Architecture Search and Beyond
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.
Networked Time Series Prediction with Incomplete Data
We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future.
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher
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.
Classification Trees for Imbalanced and Sparse Data: Surface-to-Volume Regularization
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.
VAENAS: Sampling Matters in Neural Architecture Search
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.
Resizable Neural Networks
We introduce the adaptive resizable networks as dynamic networks, which further improve the performance with less computational cost via data-dependent inference.
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
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.
Nonoverlap-Promoting Variable Selection
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.
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
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.
