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Found 15 papers, 2 papers with code
Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge
Legal Judgement Prediction has attracted more and more attention in recent years.
Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning
“Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms.
Fundamental Analysis based Neural Network for Stock Movement Prediction
In practice, news or prices of a stock in one day are normally impacted by different days with different weights, and they can influence each other.
Global Algorithms for Mean-Variance Optimization in Markov Decision Processes
In this paper, we propose a new approach to find the globally optimal policy for combined metrics of steady-state mean and variance in an infinite-horizon undiscounted MDP.
Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network
In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system.
Risk-Sensitive Markov Decision Processes with Long-Run CVaR Criterion
CVaR (Conditional Value at Risk) is a risk metric widely used in finance.
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation
Among the reasons hindering reinforcement learning (RL) applications to real-world problems, two factors are critical: limited data and the mismatch between the testing environment (real environment in which the policy is deployed) and the training environment (e. g., a simulator).
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning
Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc.
A unified algorithm framework for mean-variance optimization in discounted Markov decision processes
To deal with this unorthodox problem, we introduce a pseudo mean to transform the untreatable MDP to a standard one with a redefined reward function in standard form and derive a discounted mean-variance performance difference formula.
Average-Reward Reinforcement Learning with Trust Region Methods
Our work provides a unified framework of the trust region approach including both the discounted and average criteria, which may complement the framework of reinforcement learning beyond the discounted objectives.
Risk-Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance
This paper investigates the optimization problem of an infinite stage discrete time Markov decision process (MDP) with a long-run average metric considering both mean and variance of rewards together.
SOAC: The Soft Option Actor-Critic Architecture
In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.
Embedding-based Retrieval in Facebook Search
In this paper, we discuss the techniques for applying EBR to a Facebook Search system.
Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration
The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks.
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning
In this paper, we present a new reinforcement learning (RL) algorithm called Distributional Soft Actor Critic (DSAC), which exploits the distributional information of accumulated rewards to achieve better performance.
