This paper optimizes performative risk by focusing on convex properties and developing efficient algorithms.
problem Performative risk, the loss experienced by decision makers, is not optimized by stable models.
method Identifying convex properties of loss function and model-induced distribution shift, developing algorithms for optimization.
result Optimization of performative risk with better sample efficiency than generic methods.
Plug-in method improves performative prediction accuracy.
problem Learning under performative feedback with slow convergence rates.
method Plug-in performative optimization using models.
result Plug-in method can be superior to model-agnostic strategies.
We present a new methodology of computing incremental contribution for performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling ratios. Using Euler's homogeneous function theorem, we are able to decompose these performance ratios as a linear combination of individual modified performance ratios. This a…
The computation of convolution layers in deep neural networks typically rely on high performance routines that trade space for time by using additional memory (either for packing purposes or required as part of the algorithm) to improve performance. The problems with such an approach are two-fold. First, these routines…
New causal models perform poorly when evaluated on biased training sets.
problem Sample selection bias affects the evaluation of causal models' prediction performance.
method Re-evaluated prediction performance of causal models on a genetic perturbation data set, proposing a less-biased evaluation set.
result Causal models have similar or worse performance when evaluated on a less-biased set compared to standard association-based estimators.
A new framework for performative prediction robust to distributional misspecification.
problem Performative prediction models can be influenced by their own predictions, leading to suboptimal outcomes.
method Introduces distributionally robust performative prediction (DRPO) to approximate the true performative optimum (PO) robustly.
result DRPO provides provable guarantees as a robust approximation to the true PO when the nominal distribution map is misspecified.
Study compares Islamic banks' accounting and market performance.
problem Assessing the relationship between Islamic banks' accounting and market performance.
method Selected six Islamic banks, collected data from 2009-2013, used random-effect models.
result Superior accounting performance does not correlate with superior market performance.
The study evaluates AI model performance measures for medical use.
problem Selecting appropriate performance measures for AI models in medical practice.
method Assessed 32 performance measures across five domains for binary outcomes.
result 17 measures are both proper and reflect decision-analytic performance.
SHIFT framework identifies subgroups with large ML model performance decay.
problem Large model performance decay in subgroups when deployed.
method Subgroup-scanning Hierarchical Inference Framework (SHIFT) for performance drift.
result SHIFT identifies interpretable subgroups with large performance decay and suggests targeted actions to mitigate it.
When predictions support decisions they may influence the outcome they aim to predict. We call such predictions performative; the prediction influences the target. Performativity is a well-studied phenomenon in policy-making that has so far been neglected in supervised learning. When ignored, performativity surfaces as…
The paper explores how machine learning models can be learnable despite label shifts.
problem Learnability of binary classification models in the presence of label shifts.
method Developed a performative empirical risk function that is an unbiased estimate of the true risk on the shifted distribution.
result PAC-learnable hypothesis spaces remain PAC-learnable for performative scenarios.
New approach tackles decision-making under predictions that shape outcomes.
problem Challenges in learning optimal decision rules when predictions influence outcomes.
method Introduces performative omniprediction, a predictor that encodes optimal decision rules for multiple objectives.
result Efficient performative omnipredictors exist under a natural restriction of outcome performativity.
This paper extends performative prediction to nonlinear cases.
problem Performative prediction's effectiveness is limited by linear assumptions in real-world applications.
method Formulated a maximum margin approach loss function and extended it to nonlinear spaces using kernel methods.
result Derived conditions for performative stability in both linear and nonlinear cases.
Learn2Evaluate uses learning curves to estimate high-dimensional prediction performance.
problem Estimating test performance in high-dimensional data settings is challenging.
method Learn2Evaluate uses learning curves to estimate test performance at the total sample size.
result Learn2Evaluate provides a lower confidence bound for performance estimation.
New method improves consistency of reinforcement learning performance evaluations.
problem Inconsistent performance results in reinforcement learning due to flawed evaluation metrics.
method Proposes a new comprehensive evaluation methodology for reinforcement learning algorithms.
result Demonstrates improved reliability of performance measurements for reinforcement learning algorithms.
Machine learning predicts ship performance changes over time.
problem Estimating ship hydrodynamic performance over time.
method Machine learning methods (NL-PCR, NL-PLSR, probabilistic ANN) calibrated with in-service data.
result Probabilistic ANN model performs best in predicting ship performance changes.
Survey of performative prediction, a machine learning setup causing distribution shifts.
problem Machine learning models causing shifts in the environment they predict.
method Classification of performative prediction settings based on distribution map information.
result Introduction of new solution concepts and theoretical analyses.
Proposes a Siamese NN for algorithm selection focusing on alike performing instances.
problem Lack of effective meta-features for algorithm selection via meta-learning.
method Siamese Neural Network architecture with 'Algorithm-Performance Personas' concept.
result Proposed metric outperforms standard performance metrics in training sample selection.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
The paper explores conditions for predicting optimization performance.
problem Lack of formal theoretical guarantees linking prediction and optimization performance.
method Exploring conditions for asymptotic convergence and exact quantification of optimization performance.
result Explicit theoretical relationship between prediction and optimization performance.
Paper proposes GP-NAS-ensemble for fast neural architecture performance prediction.
problem Estimating neural network performance without training time-consuming evaluations.
method GP-NAS-ensemble framework using ensemble learning improvements.
result Ranked second in a NAS performance prediction challenge.
Proposes a new cross-validation method to estimate model performance.
problem The standard cross-validation method does not accurately estimate the performance of the recommended model.
method Develops a new random-effects model framework to improve naive cross-validation estimators.
result Proposed estimators outperform conventional and naive methods in estimating model performance.
Partially performative prediction studies how predictive models influence future data.
problem Distribution shift in predictive models due to endogenous and exogenous factors.
method Generalizing performative prediction to capture both endogenous and exogenous sources of distribution shift.
result Developed online analogues of performative stability and optimality for partially performative environments.
Paper analyzes impact of PRM on binary random variables and distribution shifts.
problem Impact of performative risk minimization on binary random variables and distribution shifts.
method Formulated two measures of impact, derived explicit formulas for full information, and provided estimators for partial information.
result PRM can have amplified side effects compared to methods that do not model data shift.
Proposes a method to select fair performance metrics through metric elicitation.
problem Choosing fair performance metrics in multiclass classification with multiple sensitive groups.
method Metric elicitation strategy that requires only relative preference feedback and is robust to noise.
result Elicits group-fair performance metrics for multiclass classification problems.
Paper establishes statistical inference for performative predictions.
problem Dynamic influence of predictions on their targets.
method End-to-end framework for estimation and inference under performativity.
result Established central limit theorem for performative settings.
Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enabling high user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models (particularly co…
The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial …
Paper introduces a new performance metric for class imbalance datasets.
problem Challenges in selecting and comparing models for imbalanced datasets.
method Proposes a new performance measure based on the harmonic mean of Recall and Selectivity normalized in class labels.
result The proposed measure is less sensitive to changes in the majority class and more sensitive to changes in the minority class.
New framework models algorithmic decisions affecting data distribution, enabling efficient learning.
problem Algorithmic decisions can alter data distribution, affecting model performance.
method Model performative effects as push-forward measures, estimating gradients under shift operators.
result Prove convexity of performative risk, allowing more accurate models to be harder to classify.
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datas…
Deep learning models predict mutual funds' performance better than traditional methods.
problem Predicting mutual funds' performance accurately.
method Deep learning models (LSTM, GRUs) trained with Bayesian optimization and ensemble methods.
result Ensemble method of LSTM and GRUs achieves the highest accuracy in forecasting mutual funds' Sharpe ratios.
Develops a new class of forward performance processes for investment pools.
problem Investment performance in market models with continuous semimartingale stock prices.
method Constructs a broad class of forward performance processes with power mixture initial conditions.
result Characterizes and derives properties of two-power mixture forward performance processes.
The paper optimizes stock portfolios with constraints based on performance attribution.
problem Optimizing stock portfolios with performance attribution constraints.
method Minimizes expected tail loss, constrains asset allocation and selection effect, tests on Dow Jones stocks.
result Imposing constraints on asset allocation and selection effect improves portfolio performance.
Study compares three performance metrics of Bangladeshi banks.
problem Comparing different performance metrics of commercial banks.
method Empirical framework using MPI, ROA, TSR over 2011-2015.
result Productivity growth was recorded, but profitability and stock performance were negative.
Method diagnoses model performance under distribution shifts.
problem Understanding and improving model performance under distribution shifts.
method DIstribution Shift DEcomposition (DISDE) method to attribute performance drop to distribution shifts.
result Shows how model performance can be improved across different distribution shifts.
This work introduces a method to attribute model performance drops to distribution shifts.
problem Attributing performance drops of machine learning models to distribution shifts.
method Formulated as a cooperative game, value of a set of distributions is defined as the change in model performance when only that set of distributions changes. Importance weighting method for computing the value of an arbitrary set of distributions is derived. Quantifying the contribution of each distribution as its Shapley value.
result Demonstrated the effectiveness of the method on various case studies.
Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems. Using global constraints to perform rescoring is complementary to state of th…
New algorithm improves group fairness in social classification problems by exploiting performativity.
problem Inequities in social classification problems due to performativity.
method Develops algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees.
result Achieves stronger group fairness guarantees compared to non-performative settings.
Paper tackles performative prediction without convexity assumptions.
problem Performative prediction where data distribution changes with model deployment.
method Reparameterization framework to transform non-convex objective into convex one.
result Provably sublinear regret guarantees for learnable model.
Identifies the best-performing algorithm from a set of candidates.
problem Finding the best-performing machine learning algorithm from benchmark performance.
method Subset selection for multinomial distributions, providing asymptotic and finite-sample schemes.
result Significantly improved methods for identifying the best-performing algorithm.
Investment managers face harder choices in green stocks due to reduced performance variability.
problem Difficulty in deploying talent in green stocks due to reduced performance variability.
method Analysis of S&P 500 firms' greenhouse gas emission levels and peer performance ratios.
result Performance variability has decreased in green stocks, making it harder for managers to choose.
Unified framework for comparing classification metrics across different imbalance rates.
problem Differences in scale and sensitivity to class imbalance rates in classification metrics.
method Introduces outperformance standardization (OPS) function to map metrics to a common scale.
result Unified o-value metric provides clear comparison across different imbalance rates.
The book explores alternatives to worst-case analysis for algorithm performance.
problem Providing strong worst-case guarantees for many algorithms is impossible.
method Surveying and detailing various nuanced analysis approaches.
result More nuanced analysis approaches are needed for fundamental problems.
New method explains ML performance gaps without causal knowledge.
problem Understanding why ML algorithms perform differently across domains.
method Nonparametric hierarchical decomposition framework.
result Detailed variable-level explanations for performance gaps.
XPER methodology decomposes credit scoring model performance.
problem Monitoring and understanding the key drivers of credit scoring model performance.
method XPER methodology based on Shapley values, decomposing performance metrics into feature contributions.
result A small number of features explain a large part of model performance.
The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.
problem Analyzing performance of competing fund managers in Ito-diffusion markets.
method Developed forward relative performance criteria and forward Nash equilibrium for passive and competitive cases.
result Extended performance criteria for investment problems in Ito-diffusion markets.
This paper studies how relative performance concerns affect stock prices in a tree-like market model.
problem The impact of relative performance concerns on stock prices in a tree-like market model.
method Mean-field equilibrium analysis in a binomial tree framework with exponential utility.
result Existence and uniqueness of market-clearing mean-field equilibrium in both single- and multi-population settings.