A new kernel risk-sensitive loss improves adaptive filtering robustness and speed.
problem Improving adaptive filtering performance in non-Gaussian environments.
method Introducing kernel risk-sensitive loss (KRSL) and developing MKRSL algorithm.
result MKRSL achieves faster convergence and higher accuracy with robustness to outliers.
The paper studies risk-sensitive learning schemes and provides learning bounds for empirical OCE minimizers.
problem Risk-sensitive learning aims to minimize risk-averse measures of loss.
method Proposes learning bounds for empirical OCE minimizers based on Rademacher average and variance.
result Provides two learning bounds on the performance of empirical OCE minimizers.
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
A risk-sensitive approach to inverse reinforcement learning.
problem Risk-sensitive reinforcement learning in Markov decision processes.
method Gradient-based algorithm minimizing a loss function defined on observed behavior.
result Demonstrated performance on Grid World and ride-sharing examples.
Study quantifies model risk in cyber insurance, affecting premium pricing.
problem Model risk and risk sensitivity in cyber insurance pricing.
method Robust estimators for model parameters and dependence analysis.
result Robust estimation improves tail index and joint loss model accuracy.
New algorithms optimize risk in reinforcement learning with exponential utility.
problem Optimizing rewards under risk in reinforcement learning with unknown transition kernels.
method Two model-free algorithms: Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ).
result Proved near-optimal regret bounds for RSVI and RSQ.
The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.
problem Modeling human-like decision-making in multi-agent systems with risk-seeking and loss-aversion behaviors.
method Forward policy design and inverse reward learning with iterative reasoning and cumulative prospect theory.
result The proposed algorithms demonstrate both risk-averse and risk-seeking behaviors in multi-agent systems.
Boosted CVaR Classification improves tail performance in classification tasks.
problem Maximizing tail performance in classification tasks.
method Proposed Boosted CVaR Classification framework using randomized classifiers and LPBoost algorithm.
result Minimizing CVaR loss over randomized classifiers leads to better tail performance.
Paper introduces RCaI, a risk-sensitive control method using Rényi divergence.
problem Risk-sensitive control in reinforcement learning.
method RCaI extends CaI using Rényi divergence variational inference.
result Risk-sensitive optimal policy can be obtained by solving a soft Bellman equation.
Paper introduces a new method for risk-sensitive investment management using RL.
problem Risk-sensitive portfolio management with unknown model parameters.
method Combines RL and risk-sensitive stochastic control with Gaussian perturbations for exploration.
result Endogenous relative-entropy regularization and optimal investment strategy derived.
Study risk-sensitive RL in offline settings, improving efficiency and accuracy.
problem Efficiently derive near-optimal policies for risk-sensitive RL using offline data.
method Introduced two provably sample-efficient algorithms for risk-sensitive offline RL in linear MDPs.
result First provably efficient risk-sensitive offline RL algorithms.
Paper introduces risk-sensitive bandits with optimal arm mixtures.
problem Designing algorithms for risk-sensitive multi-armed bandits.
method Formalizes risk-sensitive bandits framework, identifies optimal arm mixtures, designs regret-efficient algorithms.
result Regret-efficient algorithms track optimal arm mixtures or solitary arms.
Study risk-sensitive imitation learning using GAIL and Wasserstein distance.
problem Improve imitation learning performance by considering risk profiles.
method Formulate risk-sensitive imitation learning, derive optimization problems for JS divergence and Wasserstein distance, develop algorithms.
result RS-GAIL algorithms outperform GAIL and RAIL in MuJoCo and OpenAI tasks.
This paper introduces a method to incorporate risk sensitivity in RL using quadratic variation penalties.
problem Risk-sensitive reinforcement learning under entropy regularization.
method Equivalent martingale property and quadratic variation penalty for value process.
result The proposed method improves finite-sample performance in linear-quadratic control problems.
Improves SA-CCR model to be more consistent and risk-sensitive.
problem Inconsistent and risk-insensitive SA-CCR model.
method Cashflow decomposition in a 3-Factor Gaussian Market Model.
result Makes SA-CCR self-consistent and risk-sensitive.
Improved risk-sensitive RL with exponential Bellman equation and better regret bounds.
problem Exponential gap between upper and lower bounds in risk-sensitive RL.
method Identified and addressed deficiencies in existing algorithms and analysis; developed novel analysis and exploration mechanism.
result Improved regret upper bounds over existing ones.
Study risk-sensitive reinforcement learning with Lipschitz dynamic risk measures, establishing regret bounds.
problem Risk-sensitive reinforcement learning in Markov decision processes.
method Two model-based algorithms for Lipschitz dynamic risk measures, focusing on regret bounds.
result Upper bounds demonstrate optimal dependencies on actions and episodes, reflecting risk sensitivity vs. sample complexity trade-off.
Develops an actor-critic algorithm for risk-sensitive Markov decision processes.
problem Risk-sensitive cost criterion in Markov decision processes.
method Actor-critic algorithm with function approximation.
result Asymptotic convergence of the actor-critic algorithm.
Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.
problem Optimizing decision-making under risk in Markov processes.
method Analyzes risk-sensitive criteria using Optimized Certainty Equivalent, including entropic risk and Conditional Value-at-Risk.
result Conditions for the existence of optimal policies and solution procedures are provided.
Framework for risk-sensitive cooperative games between humans and machines.
problem Asymmetric information and risk sensitivity in human-machine interactions.
method Risk-sensitive dynamic games, performance measures based on regret.
result Framework improves joint optimization of human and machine objectives.
Paper tackles risk-sensitive impulse control for continuous-time processes.
problem Risk-sensitive impulse control for continuous-time Feller-Markov processes.
method Probabilistic approach to solve Bellman equation and construct optimal strategy.
result Optimal strategy approximated by dyadic impulse strategies.
Separates estimation and control in risk-sensitive investment problems with partial observation.
problem Risk-sensitive investment problems with incomplete observation.
method Investigates separability of a general class of risk-sensitive investment management problems using a finite-dimensional filter.
result The separated problem is strictly equivalent to the original control problem.
Paper tackles risk-sensitive decision-making under uncertainty.
problem Risk-sensitive decision-making problem under uncertainty.
method Formulated as a stochastic control problem, delineated necessary optimality conditions.
result Illustrative examples from optimal betting and inventory management support the theory.
We establish a stochastic maximum principle (SMP) for control problems of partially observed diffusions of mean-field type with risk-sensitive performance functionals.
Dynamic reinsurance minimizes insurer's cost of capital over time.
problem Minimizing insurer's cost of capital in a dynamic reinsurance setting.
method Dynamic extension of the static optimal reinsurance problem, viewed as a risk-sensitive Markov Decision Process.
result Existence of a stationary Markovian optimal reinsurance policy under an infinite planning horizon.
This paper analyzes risk-sensitive reinforcement learning with Conditional Value-at-Risk (CVaR) for robust Markov Decision Processes.
problem Risk-sensitive reinforcement learning for robust Markov Decision Processes (RMDPs) with state-action-dependent ambiguity sets.
method The paper establishes a connection between robustness and risk sensitivity, defining a new risk measure NCVaR and proposing value iteration algorithms.
result The proposed approach using NCVaR optimization and value iteration algorithms can solve problems with state-action-dependent ambiguity sets.
Solves risk-sensitive investment via duality, entropic regularization, and RL.
problem Risk-sensitive portfolio management in a factor-based setting.
method Free energy-entropy duality, Kuroda-Nagai change-of-measure, RL algorithm.
result Direct analytical solution, explicit controls, two interpretations of optimal allocation.
Study gap-dependent regret bounds for risk-sensitive RL.
problem Risk-sensitive reinforcement learning with entropic risk measure.
method Propose cascaded gaps to adapt to problem structures, derive regret bounds.
result Exponential improvement over existing bounds in appropriate settings.
Novel framework for risk-sensitive reinforcement learning using martingale decomposition.
problem Risk sensitivity in sequential decision-making with uncertain rewards.
method Martingale decomposition and chaotic variation for reward uncertainty, integrated into model-free reinforcement learning algorithms.
result Demonstrated relevance of risk-sensitive reinforcement learning in grid world and portfolio optimization problems.
Novel framework for risk-sensitive reinforcement learning with robustness against uncertainty.
problem Risk-sensitive reinforcement learning with uncertainty in transition dynamics.
method Developed a risk-sensitive robust Markov decision process (RSRMDP), derived its Bellman equation, and proposed a Bayesian Dynamic Programming (Bayesian DP) algorithm.
result Demonstrated convergence to near-optimal policies and analyzed sample and computational complexities.
Flexible framework for bounding high-loss predictions using quantiles.
problem Need for rigorous guarantees in risk-sensitive applications.
method Order statistics of loss values, flexible quantile-based metrics.
result Ability to rigorously control loss quantiles on real-world datasets.
Bayesian neural networks decompose uncertainty into epistemic and aleatoric components for efficient and risk-sensitive learning.
problem Uncertainty in Bayesian neural networks estimation of weights and complex noise patterns in data.
method Decomposition of uncertainty into epistemic and aleatoric components, and definition of a risk-sensitive criterion for reinforcement learning.
result Identification of informative points for active learning and policies balancing expected cost, model-bias, and noise aversion.
Risk-sensitive RL aims to optimize performance while considering risk constraints or measures.
problem Optimizing expected value alone may not be sufficient; incorporating risk measures is necessary.
method Policy gradient search is used to solve risk-sensitive RL problems, considering various risk measures.
result Policy gradient methods can be adapted for risk-sensitive RL, addressing challenges and future directions.
Risk-averse trading policies learned from simulated market interactions.
problem Minimizing execution cost in limit order book markets with market impact.
method Risk-sensitive Q-learning applied to Markov Decision Process in a market simulator.
result Derived decision-tree-based execution policies that minimize cost variance.
Formulates Markov property for risk-sensitive dynamic optimisation.
problem Risk-sensitive dynamic optimisation problems in discrete time.
method Formulates probabilistic Markov property under dynamic risk framework.
result Property holds for standard risk measures and has multiple equivalent versions.
New algorithm for risk-sensitive reinforcement learning with natural policy gradients.
problem Risk-sensitive reinforcement learning with downside risk constraints.
method Introduce a new Bellman equation to estimate the lower partial moment of returns, use natural policy gradients, and extend Reward Constrained Policy Optimization.
result Sample-efficient estimation of partial moments and effective risk-sensitive control.
In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle (SMP) for optimal control of stochastic differential equations (SDEs) of mean-field type, in which the drift and the diffusion coefficients as well as the performance function…
Develops variational framework for LQG risk-sensitive MFGs with major-minor interactions.
problem Risk-sensitive optimal control in LQG systems with major-minor interactions.
method Variational approach, nonlinear necessary and sufficient condition of optimality, equivalent risk-neutral measure, Markovian closed-loop best-response strategies.
result Derives optimal control strategies for LQG risk-sensitive MFGs with major-minor interactions, establishing Nash and ε-Nash equilibria. A new method for risk-sensitive reinforcement learning using Spectral Risk Measures.
problem Incorporating risk sensitivity into reinforcement learning algorithms.
method Proposes a novel framework for optimizing Spectral Risk Measures in both online and offline RL algorithms.
result Demonstrates consistent outperformance over existing risk-sensitive methods in various domains.
Optimizes portfolios with costs, showing existence of optimal strategies.
problem Risk-sensitive portfolio optimization with transaction costs.
method Log-return i.i.d. framework, Bellman equation analysis.
result Existence of optimal strategies for risk-averse and risk-seeking cases.
Proposes a method to solve deep neural networks' local minimum problem.
problem Local minimum problem in deep neural networks training.
method Transforms cross-entropy loss into risk-averse error criterion, adjusts RSI, and uses convexity region.
result Trained deep learning machine is expected to be inside a global minimum's attraction basin.
Efficient RL in partially observable risk-sensitive environments with hindsight observations.
problem Risk-sensitive reinforcement learning in partially observable environments.
method Integrates hindsight observations into POMDP framework, develops novel RL algorithm.
result Achieves polynomial regret with provable efficiency, outperforming existing methods.
We discuss a class of risk-sensitive portfolio optimization problems. We consider the portfolio optimization model investigated by Nagai in 2003. The model by its nature can include fixed income securities as well in the portfolio. Under fairly general conditions, we prove the existence of optimal portfolio in both fin…
We study a risk sensitive control version of the lifetime ruin probability problem. We consider a sequence of investments problems in Black-Scholes market that includes a risky asset and a riskless asset. We present a differential game that governs the limit behavior. We solve it explicitly and use it in order to find …
This paper deals with discrete-time Markov control processes on a general state space. A long-run risk-sensitive average cost criterion is used as a performance measure. The one-step cost function is nonnegative and possibly unbounded. Using the vanishing discount factor approach, the optimality inequality and an optim…
Paper analyzes learning rates for SVM with Gaussian kernels.
problem Optimizing learning rates for binary classification.
method Refined error analysis for SVM with Gaussian kernel and convex loss.
result SVM with Gaussian kernel can achieve optimal learning rates under certain conditions.
A new method for reinforcement learning using quantile regression.
problem Improving reinforcement learning algorithms for better performance.
method Using quantile regression to approximate the full quantile function for state-action return distributions.
result Improved performance on Atari games, demonstrating the effectiveness of the method.
Reinforcement learning for continuous-time risk-sensitive asset allocation
problem Continuous-time risk-sensitive asset allocation
method Free energy-entropy duality reformulation and q-learning actor-critic method result Optimal policy learning with high accuracy