A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper tackles optimal policy learning with observational data in multi-action scenarios.
Neural Index Policy for multi-action bandits with heterogeneous budgets.
Lower bounds for PI on multi-action MDPs are established, showing complexity grows with action count.
In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action. Examples include selecting offers, prices, advertisements, or emails to send to consumers, as well as the problem of determining which medication to prescribe to a patient. Whil…
Optimal policy for multi-armed multi-action bandits with unknown parameters.
Study proves duality in exotic option pricing under uncertain model and delayed information.
In this work we describe a novel deep reinforcement learning architecture that allows multiple actions to be selected at every time-step in an efficient manner. Multi-action policies allow complex behaviours to be learnt that would otherwise be hard to achieve when using single action selection techniques. We use both …
Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned …
ARL and Hawkes processes improve market-making strategies with variable volatility.
Paper derives policy rules from observational data for hepatitis C treatment.
Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speakers, called a specific scenario. While some works included experiments for different scenarios, research towards combining data of different…
Two new methods score stress test scenarios for risk managers.
Extracts representative scenarios from large data panels.
Methodology measures financial impacts using existing credit loss infrastructure.
Proposes a new framework for environmental CVA with robust wrong-way risk.
This paper formed part of a preliminary research report for a risk consultancy and academic research. Stochastic Programming models provide a powerful paradigm for decision making under uncertainty. In these models the uncertainties are represented by a discrete scenario tree and the quality of the solutions obtained i…
Statistical depth metrics help identify risky power grid scenarios.
Two autoencoding models learn latent traffic scene representations.
A new method using energy distance for ensemble and scenario reduction.
Develops a method for reverse stress testing in multivariate scenarios.
Method generates plausible financial stress scenarios using large deviations.
Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e., inputs of the simulation model, where the system is unstable. Subgroup discovery methods are commonly used for scenario discovery. They find…
Generates multimodal safety-critical scenarios for robustness evaluation of decision-making algorithms.
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
We treat the so-called scenario approach, a popular probabilistic approximation method for robust minmax optimization problems via independent and indentically distributed (i.i.d) sampling from the uncertainty set, from various perspectives. The scenario approach is well-studied in the important case of convex robust o…
Algorithm reduces historical expected shortfall computation by focusing on worst-case scenarios.
Risk measures such as Expected Shortfall (ES) and Value-at-Risk (VaR) have been prominent in banking regulation and financial risk management. Motivated by practical considerations in the assessment and management of risks, including tractability, scenario relevance and robustness, we consider theoretical properties of…
Paper proposes a copula method to generate unfavorable VaR scenarios.
Study optimal timing to divest from assets with uncertain future scenarios.
Researchers validate ML scenario generators by checking dependencies and detecting memorization effects.
Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent years, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly c…
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss…
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…
We define scenarios, propose different methods of aggregating them, discuss their properties and benchmark them against quadrant requirements.
In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problemati…
The paper proposes an efficient nested simulation design using likelihood ratio method.
MARCD uses generative scenarios to improve portfolio decisions during regime shifts.
We study the problem of determination of asset prices in an incomplete market proposing three different but related scenarios. One scenario uses a market game approach whereas the other two are based on risk sharing or regret minimizing considerations. Dynamical schemes modeling the convergence of the buyer's and of th…
Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reason…
Detects causal scenarios with inequality constraints among classical correlations.
Generative Adversarial Network (GAN) simulates realistic multi-asset scenarios for tail risk.
Consider an agent who enters a financial market on day t = 0 with an initial capital amount x. He invests this amount on stocks and the money market, and by day t = T, has generated a wealth W . He is given a convex class of probability measures (called scenarios) and a real-valued function (or floors) corresponding to…
Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.
We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks. By using a Bayesian formulation, generators can be construc…
There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific scenario, and may underperform for scenarios th…
We propose a hybrid algorithmic strategy for complex stochastic optimization problems, which combines the use of scenario trees from multistage stochastic programming with machine learning techniques for learning a policy in the form of a statistical model, in the context of constrained vector-valued decisions. Such a …
New method selects critical DER scenarios for distribution grid investment planning.
Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.