Study shows comonotonicity depends on eligible assets, not just risk measures.
problem Characterizing comonotonicity in capital adequacy using risk measures.
method Examined comonotonicity in terms of acceptance sets and eligible assets.
result Comonotonicity is compatible only with risk-free eligible assets.
We study capital requirements for bounded financial positions defined as the minimum amount of capital to invest in a chosen eligible asset targeting a pre-specified acceptability test. We allow for general acceptance sets and general eligible assets, including defaultable bonds. Since the payoff of these assets is not…
We discuss risk measures representing the minimum amount of capital a financial institution needs to raise and invest in a pre-specified eligible asset to ensure it is adequately capitalized. Most of the literature has focused on cash-additive risk measures, for which the eligible asset is a risk-free bond, on the grou…
The risk of financial positions is measured by the minimum amount of capital to raise and invest in eligible portfolios of traded assets in order to meet a prescribed acceptability constraint. We investigate nondegeneracy, finiteness and continuity properties of these risk measures with respect to multiple eligible ass…
Study optimal portfolios of eligible assets for capital adequacy tests.
problem Finding minimal capital for passing a financial test.
method Existence and uniqueness of optimal portfolios, stability properties.
result Lower semicontinuity of optimal portfolios set-valued map, stability under polyhedral risk measures.
New risk measures use internal resources to make positions acceptable.
problem Monetary risk measures can lead to infinite values and lack flexibility.
method Intrinsic risk measures use internal resources and a free choice of eligible assets.
result Intrinsic risk measures avoid infinite values and preserve key properties.
Set-valued risk measures on Ldp with 0≤p≤∞ for conical market models are defined, primal and dual representation results are given. The collection of initial endowments which allow to super-hedge a multivariate claim are shown to form the values of a set-valued sublinear (coherent) risk measure. Sc…
Extends return risk measures to multiple assets, proving properties and comparing different risk models.
problem Evaluating risk in financial markets with multiple assets.
method Develops multi-asset return risk measures (MARRMs), analyzes their properties, and compares them with other risk models.
result Proves that a positively homogeneous MARRM is quasi-convex if and only if it is convex, and provides conditions to avoid inconsistent risk evaluations.
Estimates treatment effects in bipartite systems with partial eligibility and interference.
problem Randomized experiments in bipartite systems with partial treatment eligibility and interference.
method Formalizes eligibility-constrained bipartite experiments, defines PTTE and STTE, identifies conditions, develops ensemble estimators, introduces projection.
result Proposed estimators recover PTTE and STTE with low bias and variance, corrects interference bias in field experiments.
New algorithm LSTD(λ)-RP uses random projections and eligibility traces for efficient reinforcement learning.
problem Policy evaluation in high-dimensional feature spaces with linear function approximation.
method Proposes LSTD(λ)-RP algorithm combining random projections and eligibility traces. result Demonstrates improved performance and better error bounds compared to prior methods.
Paper proposes fairgroup construction to improve fairness in Medicaid eligibility decisions.
problem Improper decisions in Medicaid eligibility allocation due to ML/DL model limitations.
method Fairgroup construction based on legal doctrine of disparate impact.
result Demonstrates improved fairness in regressive classifiers for Medicaid eligibility decisions.
Model predicts eligibility of cancer patients for clinical trials.
problem Clinical trials exclude many patients based on comorbidities and age.
method Deep neural networks trained on clinical trial protocols and free-texts.
result Model accurately predicts eligibility of clinical trial statements.
Introduces expected eligibility traces for more efficient credit assignment in reinforcement learning.
problem Efficiently assigning credit to states and actions in reinforcement learning.
method Introduces expected eligibility traces, allowing updates to counterfactual sequences.
result Substantial improvements in temporal-difference learning can be achieved with expected traces.
Unified algorithm Q(σ,λ) combines eligibility traces to improve memory and computation efficiency.
problem Memory and computation inefficiency in multi-step temporal-difference learning.
method Introduced Q(σ) algorithm, combined with eligibility traces to propose Q(σ,λ). result Proposed algorithm Q(σ,λ) converges to optimal value function exponentially. Meta-learning adjusts TD learning's eligibility trace parameter for more efficient reinforcement learning.
problem Efficiently tuning the eligibility trace parameter for temporal difference learning.
method Meta-learning method to adjust eligibility trace parameter state-dependently.
result Improves overall quality of update targets, minimizing target error.
Bayesian method corrects timing misalignment in recurrent event studies.
problem Estimating differences in event rates under two treatments with timing misalignment.
method g-computation procedure with joint semiparametric Bayesian model.
result Correctly estimates average causal effects under right-censoring.
This paper extends Q(σ) to Q(σ, λ) and Double Q(σ), improving reinforcement learning control.
problem Improving reinforcement learning control methods.
method Extends Q(σ) to Q(σ, λ) using eligibility traces and introduces Double Q(σ).
result The new Q(σ, λ) algorithm outperforms classical TD control methods.
The paper defines and analyzes scalar risk measures in markets with transaction costs.
problem Defining and analyzing scalar risk measures in markets with transaction costs.
method Dual representation of scalar risk measures, time consistency, backward recursion.
result A weaker notion of time consistency for scalar risk measures in markets with frictions is defined and proven equivalent to a backward recursion.
In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects,…
Continuous selections for optimal portfolios under convex risk measures fail in finite-dimensional settings.
problem Finding optimal financial positions with continuous selections under convex risk measures.
method Analyzing set-valued maps in finite-dimensional settings with convex risk measures.
result Continuous selections do not always exist for optimal portfolios under convex risk measures.
Meta-learning technique speeds RL control learning and handles non-stationarity.
problem Hyperparameter tuning and non-stationarity in RL control.
method Meta-gradient descent for online step-size tuning with eligibility traces.
result Meta-step-size parameter easy to set, speeds learning, and handles non-stationarity.
Kernel balancing weights are generalized as KRRR, providing better confidence intervals for treatment effects.
problem Lack of generalization error, correct feature specification, and limited to average effects.
method Interpreting kernel balancing weights as KRRR, relaxing feature specification, and extending Gaussian approximation.
result KRRR provides strong generalization properties and justifies confidence sets for causal functions.
The paper explores time consistency for scalar multivariate risk measures in markets with transaction costs.
problem Time consistency of scalar multivariate risk measures in markets with transaction costs.
method Presented dual representations and derived an equivalent recursive formulation for multivariate scalar risk measures.
result Developed a direct notion of a 'moving scalarization' for scalar time consistency.
New methods improve deep reinforcement learning by accelerating credit assignment.
problem Challenges in achieving fast and stable off-policy learning in deep reinforcement learning.
method Extends the generalized PBE objective to support multistep credit assignment and derives three gradient-based methods.
result Proposed methods outperform PPO and StreamQ in MuJoCo and MinAtar environments.
New methods for fairness in regression using probabilistic classification.
problem Estimating fairness in continuous regression problems.
method Tractable approximations of fairness criteria using conditional probabilities from distinct classifiers.
result Model agnostic, tractable approximations of fairness criteria.
This paper investigates robust and efficient DR/RDR estimators for WATEs.
problem Lack of systematic investigation into robustness and efficiency conditions for WATE estimation.
method Proposes three RDR estimators using semiparametric efficient influence function and double/debiased machine learning.
result Demonstrates the practical relevance of the methods in medical and social sciences.
A fully decentralized multi-agent algorithm converges linearly with minimal memory.
problem Efficiently evaluating policies in multi-agent settings with limited exploration.
method Fully decentralized, combining off-policy learning, eligibility traces, and linear function approximation.
result Achieves linear convergence with minimal memory requirements.
New algorithms for collaborative reinforcement learning with limited communication.
problem Efficiently learning value functions in multi-agent systems with strict information constraints.
method Distributed gradient-based temporal difference algorithms with consensus schemes.
result Parameter estimates converge to ODEs with defined invariant sets under general assumptions.
Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.
problem Lack of a risk score to guide ECMO planning for critically ill patients.
method Leverages machine learning to develop the PEER score.
result PEER score predicts mortality and decompensation in patients eligible for ECMO.
New complete panel dataset for LMICs helps analyze innovation and development.
problem Lack of complete data for empirical analyses in LMICs.
method Predictive Mean Matching multiple imputation technique.
result Created a large dataset of 47 variables for 82 LMICs from 2005-2019.
Asymmetry PRISM outperforms CPU and GPU solvers for institutional rebalancing.
problem Institutional rebalancing with deadline constraints
method Asymmetry PRISM
result Asymmetry PRISM-CPU is 4.5x to 24.1x faster than the fastest completed reference row in the same lane.
TBQ(σ) improves trace utilization in off-policy reinforcement learning.
problem Efficiency of trace utilization under greedy target policies.
method Introduces TBQ(σ) that unifies tree-backup and Naive Q(λ) with a new parameter σ.
result TBQ(σ) improves efficiency in trace utilization, accelerating learning and performance.
Optimizes trading portfolios considering risk and profit.
problem Balancing risk and profit in trading portfolios.
method Risk-Aware Trading Swarm (RATS) algorithm.
result RATS improves portfolio performance and risk management.
Stochastic optimization problems often involve the expectation in its objective. When risk is incorporated in the problem description as well, then risk measures have to be involved in addition to quantify the acceptable risk, often in the objective. For this purpose it is important to have an adjusted, adapted and eff…
99% of papers use real-world data, but only 3% provide formal comparisons.
problem Lack of complete argumentative chains in demonstrating algorithmic effectiveness in machine learning papers.
method Systematic review of NeurIPS papers from 2017, assessing completeness of argumentative steps.
result Only 3% of papers provide formal comparisons, indicating incomplete argumentative chains.
Donors who defer their donations volunteer less in the future.
problem Volunteer labor can be less beneficial to charities than its costs.
method Regression discontinuity design with a procedure to handle manipulation.
result Donor manipulation invalidates standard regression discontinuity design, but a new method provides partial identification bounds.
Proposes a new method for off-policy learning in reinforcement learning.
problem Improving off-policy learning in reinforcement learning.
method Integrates current Q-function corrections with rewards instead of transition probabilities.
result Proves off-policy convergence for policy evaluation and control under certain conditions.
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
problem Evaluating the impact of algorithmic policy decisions.
method Developed a treatment-effect estimator using algorithmic decisions as instruments.
result Funding from an algorithmic relief rule had little effect on COVID-19-related hospital activities.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
Machine learning identifies key defensive factors for playoff and championship teams.
problem Characterize NBA playoff and championship teams using machine learning.
method Used classification trees, random forests, and neural networks to analyze 17 seasons of NBA data.
result Defensive factors, particularly made three-point shots and perimeter defense, are crucial for playoff and championship success.
Random Forests automatically prune a latent 'true' tree, explaining their overfitting without tuning.
problem Difficulty in building bad Random Forests and overfitting without apparent consequences.
method Bootstrap aggregation and model perturbation in Random Forests.
result Randomized ensembles implicitly perform optimal early stopping out-of-sample, explaining overfitting.
Paper explores asset pricing dynamics in Bachelier model.
problem Understanding risky asset price dynamics in Bachelier model.
method Analyzes Bachelier market model to represent risky asset price dynamics.
result Defines riskless assets within the Bachelier model.
Unified algorithm for reinforcement learning with function approximation.
problem Limited scalability of Q(σ,λ) for large-scale learning.
method Proposes GQ(σ,λ) with linear function approximation to extend tabular Q(σ,λ).
result Empirical results show GQ(σ,λ) outperforms full-sampling and pure-expectation methods.
We explain a persistent cost-of-carry spread in EUA market and suggest ECB policy change.
problem Persistent cost-of-carry spread in EUA market.
method Cointegration analysis of EUA spread with credit spread and risk-free rate.
result Cointegration found between EUA spread, credit spread, and risk-free rate.
Enhances portfolio construction with tailored regime forecasts for individual assets.
problem Traditional portfolio construction methods fail to account for asset-specific market conditions.
method Hybrid framework combining unsupervised and supervised learning for regime identification and forecasting.
result Outperforms traditional portfolio models across various asset classes.
This paper develops source traces for faster TD learning.
problem Improving temporal difference learning speed and generalization.
method Introduces source traces as a backward view of successor representations, enabling TD errors to be propagated to potential causal states.
result Demonstrates faster generalization and improved performance of source traces compared to previous methods.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
IDA makes DFMM's asset tradeable, enhancing cross-chain finance efficiency.
problem Making DFMM's asset tradeable to improve cross-chain finance efficiency.
method Introducing IDA as a tradeable asset, leveraging DFMM's robust liquidity and dynamic AMM.
result IDA enhances cross-chain finance efficiency through tradeable asset and dynamic AMM.