Search Results for author:
Found 12 papers, 4 papers with code
End-to-end Conditional Robust Optimization
The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty.
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty.
Robust Data-driven Prescriptiveness Optimization
The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information to provide more anticipative decisions.
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
However, we show that these popular decompositions for Conditional-Value-at-Risk (CVaR) and Entropic-Value-at-Risk (EVaR) are inherently suboptimal regardless of the discretization level.
Risk-Aware Bid Optimization for Online Display Advertisement
This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget.
Deep Reinforcement Learning for Equal Risk Option Pricing and Hedging under Dynamic Expectile Risk Measures
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider coherent risk measures.
WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management
In this paper, we present a new portfolio policy network architecture for deep reinforcement learning (DRL) that can exploit more effectively cross-asset dependency information and achieve better performance than state-of-the-art architectures.
WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management
The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be made over time with the consideration of many factors such as investors preferences, trading environments, and market conditions.
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures.
Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering
Optimal stopping is the problem of deciding the right time at which to take a particular action in a stochastic system, in order to maximize an expected reward.
Robustifying Conditional Portfolio Decisions via Optimal Transport
Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem.
Distributionally Robust Local Non-parametric Conditional Estimation
Conditional estimation given specific covariate values (i. e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social and natural sciences.
