New risk measures adjust for tail risk inadequacies.
problem Tail risk inadequacy in classical risk measures.
method Developed a family of adjusted risk measures using target risk profiles.
result Analyzed and derived properties of adjusted risk measures.
New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.
problem Lack of accurate treatment effect estimates for machine learning-based targeting in social domains.
method Empirical assessment of targeting strategies using data from 5 real-world RCTs in various domains.
result Treatment effect-based targeting outperforms risk-based targeting, even with biased estimates.
Paper uses neural networks to compress large portfolios of options, reducing risk and capital requirements.
problem Managing risk and capital requirements for large portfolios of financial options.
method Artificial neural network framework for portfolio compression, static hedging, and risk management.
result The compressed portfolio's risk profiles align closely with the target portfolio's, reducing capital requirements.
Target Date Funds may not adapt well, leading to poor performance.
problem Target Date Funds may not adapt to investors' needs effectively.
method Modeling with historical returns and bootstrap resampling to compare performance.
result Adaptive investment strategies significantly outperform Target Date Funds.
The paper provides guarantees for statistical learning with a nuisance parameter.
problem Statistical learning with an unknown nuisance parameter.
method Two-stage sample splitting meta-algorithm for target and nuisance parameters.
result Nuisance estimation error impacts excess risk bound of second order under Neyman orthogonality.
New method targets relative risk heterogeneity in clinical trials.
problem Identifying treatment effects across subgroups with absolute risk differences.
method Modified causal forests using a novel node-splitting procedure based on relative risk.
result Relative risk causal forests can capture heterogeneity not detected by absolute risk methods.
Proposes a new investment strategy to optimize portfolio value within a target range.
problem Maximizing portfolio value within a specified range of returns.
method Two-stage least squares Monte Carlo method to handle complex payoffs.
result STRS strategy effectively contains portfolio value within the targeted range, improving risk-return trade-off.
This paper calculates worst-case target semi-variances for uncertain losses.
problem Managing risk when loss distribution is uncertain and only partial information is known.
method Derives worst-case target semi-variances for symmetric or non-negative losses under uncertainty sets representing investor's undesirable scenarios.
result Closed-form expressions for worst-case target semi-variances are derived.
Framework improves target domain prediction using quantile matching.
problem Improving prediction accuracy in data-scarce target domains.
method Conditional quantile matching for distributional alignment.
result Empirical risk minimizer achieves tighter excess risk bound.
New bounds show polyhedral surrogates are optimal for generalization.
problem Proving generalization rates for polyhedral loss functions.
method Developed two general results for polyhedral surrogates.
result Polyhedral surrogates provide linear surrogate regret bounds, translating directly to target rates.
A declining CVaR glidepath framework for TDF design with Chilean pension system application
problem Designing Target-Date Funds around an explicit return objective while controlling risk
method Propose a framework for designing TDFs with a declining CVaR constraint
result Key feature: conservative evaluation of each glidepath
An algorithm learns from multiple models to match an oracle's risk.
problem Learning from multiple noisy models to estimate a target parameter.
method Elimination rounds algorithm for adaptive learning.
result Risk of weak-oracle learner matches that of an oracle in multiple source case.
Paper proposes methods for transfer learning with random coefficient ridge regression.
problem Estimation and prediction in high-dimensional settings with related models.
method Two estimators using weighted sums of ridge estimates from target and source models.
result Explicit expression of estimation and prediction risks derived using random matrix theory.
New algorithm uses conditionally invariant components to improve domain adaptation performance.
problem Improving domain adaptation performance when source and target data distributions differ.
method Conditionally invariant components (CICs) and importance-weighted conditional invariant penalty (IW-CIP) algorithm.
result New algorithm provides target risk guarantees and addresses label-flipping features.
The paper analyzes the performance of empirical risk minimization for p-norm linear regression.
problem Empirical risk minimization on p-norm linear regression. method Analyzes performance under various conditions and moment assumptions.
result High probability excess risk bounds for empirical risk minimizer, matching asymptotic rates.
Estimates model performance under distribution shift using domain-invariant predictors.
problem Poor performance of models on test distributions different from training distributions.
method Uses domain-invariant predictors as a proxy for unknown target labels.
result Shows that the complexity of latent representations influences target risk.
L-ARC improves model fairness by localizing risk guarantees.
problem Improving model fairness in tasks like image segmentation and wireless networks.
method Localized Adaptive Risk Control (L-ARC) updates a threshold function in RKHS to target localized statistical risk guarantees.
result L-ARC produces prediction sets with improved fairness across different data subpopulations.
Paper tackles efficient risk estimation under dataset shift conditions.
problem Limited data from target population; auxiliary data available.
method Semiparametric efficiency theory; efficient and multiply robust estimators.
result Developed estimators for various dataset shift conditions.
Develops a new method for building data-driven portfolios with a target risk-return.
problem Building a portfolio with a specific risk-return level.
method Applies LSTM to select the best predictor for portfolio construction and uses predictive threshold-based portfolios (TBPs) to target specific risk-return levels.
result Thresholds play a dominant role in characterizing risk, return, and prediction accuracy of the subset.
A new method trains deep neural networks for open set domain adaptation without negative open set difference.
problem Training deep neural networks for open set domain adaptation without negative open set difference.
method Proposes a new upper bound of target-domain risk, including source-domain risk, ε-open set difference (Δε), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and Δε, and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound. result Shows state-of-the-art performance on benchmark datasets.
New PAC-Bayesian approach for domain adaptation.
problem Learning a model from a source domain for a target domain with distributional shift.
method PAC-Bayesian analysis, deriving an upper-bound on target risk.
result Upper-bound on target risk with distribution divergence controlling trade-off.
Paper proposes a method to learn linear regression models using multiple pre-trained models.
problem Learning a linear regression model with limited target data.
method Representation transfer learning method using multiple pre-trained models.
result The method achieves better sample complexity compared to baseline methods.
Synthetic tabular data synthesis models balance utility and risk.
problem Generating synthetic tabular data for regulated domains.
method Latent flow models with various learning targets, paths, and sampling methods.
result Velocity and posterior matching objectives yield higher utility, while score and noise matching achieve lower risk.
Develops estimators for near-optimal linear regression under distribution shift.
problem Linear regression under distribution shift with scarce target domain data.
method Minimax linear risk estimators covering various transfer learning settings.
result Achieves near-optimal risk for linear regression problems under distribution shift.
Develops a robust classifier for domain adaptation without relying on restrictive assumptions.
problem Robustness of domain-adaptive classifiers in general domain adaptation settings.
method Formulates a conservative parameter estimator that guarantees lower risk for all possible target labelings.
result Classical least-squares and discriminant analysis cases perform on par with state-of-the-art classifiers in sample selection bias settings and outperform them in general domain adaptation settings.
Emputation learns imputation models guided by missingness assumptions.
problem Learning imputation models for missing data given observed data.
method Guided by specific missingness assumptions, Emputation trains a deep generative model to learn the extrapolation distribution of missing variables.
result The population minimizer of the emputation risk recovers the target extrapolation distribution under various identification assumptions.
When we implement a portfolio selection methodology under a mean-risk formulation, it is essential to correctly model investors' risk aversion which may be time-dependent, or even state-dependent during the investment procedure. In this paper, we propose a behavior risk aversion model, which is a piecewise linear funct…
The paper analyzes recalibration methods for binary classifiers under distribution shift.
problem Recalibrating binary classifiers to match a target prior probability.
method Analysis of distribution shift assumptions and proposal of new recalibration methods.
result QMM methods provide conservative results for risk weights functions.
Optimal reinsurance strategy found to minimize financial risk.
problem Minimizing financial risk in insurance companies through optimal reinsurance.
method Solving the problem via neural networks and a Cramér-Lundberg model.
result Optimal reinsurance strategy found to control terminal wealth and ruin probability.
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.
DRDA robustly adapts models across domains with mismatched distributions.
problem Vulnerability of DA methods to noise and inability to generalize to unseen samples.
method DRDA uses distributionally robust optimization (DRO) with MMD metric to learn robust decision functions.
result DRDA outperforms existing robust learning approaches in experiments.
Evaluating prediction models under covariate shift and selective labels
problem Model performance evaluation under distribution shift and selection bias
method Double machine learning
result Accurate estimation of target risk
Develops a model for optimal trading with uncertain volume targets.
problem Optimal trading strategy under uncertain volume targets.
method Model incorporating risk term related to volume uncertainty.
result Delayed trades can be optimal for risk-averse traders.
Quantitative Structuring reveals risk sources and performance measures.
problem Model risk in financial products.
method Investment structuring approach to analyze risk.
result Reveals precise risk sources and performance measures.
A Python approach minimizes risk in decentralized exchanges.
problem Minimizing risk in decentralized exchanges.
method Three-step approach: Kernel Ridge Regression, function minimization, and algorithmic trick.
result Reduced computational load and increased solution accuracy.
MetaPred uses meta-learning to improve clinical risk prediction from limited EHR data.
problem Clinical risk prediction from sparse patient EHR data.
method Meta-learning approach to train a meta-learner from related tasks, then fine-tune for target risk prediction.
result MetaPred achieves better performance for target risk prediction with limited data.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
Deep learning predicts risky behavior in retail investors for financial risk management.
problem Predicting profitable trading behavior in retail investors.
method Developed a deep learning model to predict trader profitability.
result Deep learning outperforms conventional machine learning methods in predicting trader behavior.
New estimator reduces risk in slate bandits by leveraging Bayes risk criterion.
problem Evaluating slate policies using logged data when policies factorize over slots.
method Developed a new estimator using a control variate approach, showing risk improvement over existing methods.
result The new estimator has lower risk than the pseudoinverse estimator in slate bandit problems.
Rotation invariant algorithms fail with hard labels sampled from sparse targets.
problem Rotation invariant algorithms fail to learn from hard labels sampled from sparse targets.
method Proving the excess risk of rotation invariant algorithms and proposing a simple non-rotation invariant algorithm.
result Rotation invariant algorithms incur an excess risk of $Ω\left(\frac{d-1}{n}
ight)$, while non-rotation invariant algorithms have an excess risk of $O\left(\frac{s\log d}{n}
ight).
A new meta-learning framework that assigns weights to source tasks based on target samples.
problem Learning initialization for target tasks with limited labeled examples.
method A general framework that assigns weights to the loss of different source tasks, which can depend on the target samples. Provides upper bounds and develops a learning algorithm based on minimizing the error bound with respect to an empirical IPM.
result Empirically, the weighted meta-learning algorithm finds better initializations than uniformly-weighted meta-learning algorithms.
The study evaluates forecast risk-adjusted performance using various metrics.
problem Evaluating forecast reliability beyond accuracy.
method Risk-adjusted performance measures (Sharpe, Sortino, Omega ratios) and Edge Ratio.
result Machine learning models often offer attractive risk profiles but not necessarily higher reliability.
Risk-only investment strategies have been growing in popularity as traditional in- vestment strategies have fallen short of return targets over the last decade. However, risk-based investors should be aware of four things. First, theoretical considerations and empirical studies show that apparently dictinct risk-based …
ToolChain-CRC addresses the risk-control problem for retrieval-augmented and tool-using agents under drift.
problem Risk-control problem for retrieval-augmented and tool-using agents under drift.
method ToolChain-CRC uses conformal risk-control under exchangeable calibration runs.
result Trajectory-level risk control keeps accepted-trajectory risk below the target.
Unified framework for HTL with faster convergence rates.
problem Incorporating hypotheses from source to target domains.
method Transformation function framework for HTL.
result HTL achieves faster convergence rates than non-transfer learning.
Federated learning method improves covariate shift adaptation for missing target values.
problem Missing target values in federated learning.
method Federated covariate shift adaptation algorithm for missing target output values.
result Asymptotically unbiased and efficient algorithm for federated learning.
This paper was presented and written for two seminars: a national UK University Risk Conference and a Risk Management industry workshop. The target audience is therefore a cross section of Academics and industry professionals. The current ongoing global credit crunch has highlighted the importance of risk measurement i…
Study shows pretraining and finetuning can effectively tackle covariate shift in linear regression.
problem Linear regression under covariate shift where source and target distributions differ but conditional distribution remains similar.
method Pretraining on source data and finetuning on target data using online SGD.
result Transfer learning with O(N2) source data is as effective as supervised learning with N target data.