FastAMI efficiently approximates AMI and SMI for large datasets.
problem Computational difficulty in comparing clusterings with an adjustment for chance.
method Monte Carlo-based approach to approximate AMI and SMI.
result FastAMI provides accurate results for large datasets.
The paper proposes a framework to adjust dependency measure estimates for chance.
problem Challenges in interpreting and ranking dependency measures on finite samples.
method Simple adjustments to improve interpretability and accuracy of dependency measures.
result Improves interpretability and accuracy of dependency measures, demonstrated on MIC and random forests.
This paper proposes a method to safely adjust exploration in RL to satisfy constraints.
problem Unsafe exploration in reinforcement learning violates constraints on controlled object states.
method Automatic adjustment of exploration inputs and variance-covariance matrix for safety.
result The method guarantees satisfaction of joint chance constraints with specified probability.
Paper proposes adjustments for ARI and AMI measures.
problem Lack of clear guidelines for using ARI and AMI.
method Developed generalized IT measures and solved their statistical properties.
result Proposed guidelines for using ARI and AMI.
This note explores the mathematical theory to solve modern gamblers ruin problems. We establish a ruin framework and solve for the probability of bankruptcy. We also show how this relates to the expected time to bankruptcy and review the risk neutral probabilities associated an adjustment to asymmetrical views.
A new RL method handles uncertainty and constraints in real-time optimization.
problem Real-time optimization under process uncertainty and constraints.
method Chance-constrained reinforcement learning to handle probabilistic state constraints.
result Satisfies process constraints with high probability in real-time.
Proposes a new method to rank risky investments based on Omega measure.
problem Evaluating and ranking risky investment projects.
method Introduces an investment certainty equivalence approach and uses the Omega measure.
result Proposed method ranks projects differently from conventional risk-adjusted discount rate (RADR) approach.
Two-stage TMLE reduces bias and improves efficiency in CRTs.
problem Differential outcome measurement and imbalance in baseline predictors in CRTs.
method Two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates.
result Our approach nearly eliminates bias due to differential outcome measurement.
LFD method improves text classification by making features clearer and less label-leaking.
problem Creating interpretable text representations that are both predictive and understandable.
method LFD method: proposes lexical and semantic features from contrastive text pairs, screens candidates using κ, and selects features by residual gain. result LFD features achieve higher human-human and human-LLM agreement than baseline concepts and are less label-leaking.
A new model of learning corrects for chance to improve learning outcomes.
problem The importance of chance-corrected measures in learning.
method Developed two models: Informatron and AdaBook, based on empirical psychological results.
result Chance correction facilitates learning, as shown by computational results.
New algorithms improve boosting by optimizing chance-corrected measures.
problem Improving boosting algorithms to use chance-corrected measures effectively.
method Developed new algorithms (AdaBook and Multibook) that optimize chance-corrected measures.
result AdaBook and Multibook outperform standard Multiboost or AdaBoost in multiclass situations.
Chance-constrained ActInf allows for small violations of constraints to drive goal-directed behavior.
problem Goal-directed behavior constrained by prior beliefs.
method Introducing chance constraints to ActInf, allowing for small violations of constraints.
result Chance-constrained ActInf allows for a trade-off between robust control and chance constraint violation.
SALR improves deep learning generalization by dynamically adjusting learning rates.
problem Improving generalization in deep learning models.
method Sharpness-aware learning rate scheduling based on local loss function sharpness.
result SALR drives solutions to flatter regions, improving generalization and convergence.
New theory of co~events resolves debates between Bayesianists and frequentists.
problem Fierce debates between Bayesianists and frequentists over Bayesian scheme.
method Developed new co~event axiomatics and theory to study experience and chance as a single co~event.
result Demonstrated effectiveness of new theory in resolving debates over Bayesian scheme.
This work proposes an online learning approach to tighten constraints in stochastic control problems.
problem Solving chance-constrained stochastic optimal control problems is computationally challenging.
method Reformulate chance constraints as a binary regression problem and use a GP model to learn constraint-tightening parameters online.
result The approach tightens constraints more effectively, leading to lower costs in numerical experiments.
CPP solves chance constrained optimization problems with a framework that combines samples and quantile lemma.
problem Chance constrained optimization problems with constraints on random variables.
method CPP framework using samples and quantile lemma to transform into deterministic problem.
result CPP provides a posteriori guarantees on constraint satisfaction and can handle different types of chance constraints.
Method approximates efficient frontier of chance-constrained programs.
problem Approximating the efficient frontier of chance-constrained nonlinear programs.
method Stochastic approximation method based on bi-objective viewpoint.
result Converges to stationary solutions of a smooth approximation of the original problem.
The paper tackles voltage control in distribution systems with uncertainties using chance constraints.
problem Voltage control in distribution systems with high uncertainties from distributed energy resources.
method Chance constraint approach accounting for arbitrary correlations, solved via stochastic quasi gradient method.
result The method is more robust and computationally tractable compared to conventional approaches.
A scalable method for deep metric learning using chance constraints.
problem Improving deep metric learning by addressing feasibility issues.
method Relating DML to chance constraints, reformulating as a feasibility problem, and iteratively training proxies.
result The method effectively improves deep metric learning performance across multiple benchmarks.
Develops a new method for optimizing with uncertain data.
problem Uncertainty in real-world optimization problems.
method Combines chance constraints and constraint learning for mixed-integer linear optimization.
result Data-driven solution for setting probabilistic bounds on learned constraints.
The paper tackles online resource allocation with uncertain coefficients and chance constraints.
problem Online stochastic resource allocation problem with chance constraints.
method Linearization and primal-dual algorithms with heuristic corrections.
result Optimality gap and constraint violation are on the order of √n.
Bunching in non-ideal roulette increases gambler's chances, leading to positive expected return.
problem Chances of a gambler are lower than a casino in ideal roulette, but deviations make it possible to win.
method Described deviations by a statistical distribution, found critical δ to equalize chances, and estimated critical capital.
result Bunching of numbers in non-ideal roulette can lead to positive expected return for the gambler.
The paper develops embeddings to estimate causal effects from text data.
problem Estimating causal effects from text data with confounding features.
method Causally sufficient embeddings combining supervised dimensionality reduction and efficient language modeling.
result Causally sufficient embeddings improve causal estimation over related methods.
Survey of DRO, a robust optimization framework.
problem Risk-aversion and chance-constrained optimization challenges.
method Distributionally robust optimization (DRO) framework.
result DRO's growing importance in operations research and statistics.
A new measure normalizes clustering accuracy to evaluate algorithms better.
problem Evaluation of clustering algorithms is challenging due to limitations of existing measures.
method Proposes a new, normalised clustering accuracy measure.
result The new measure identifies worst-case scenarios and is more interpretable.
The paper interprets DRO problems through chance constraints, offering a clearer business interpretation.
problem Understanding distributional ambiguity in optimization problems.
method Relating DRO problems to mean-deviation problems and chance-constrained optimization.
result A DRO problem can be transformed into a chance-constrained optimization problem, providing a clearer interpretation.
We discuss the role of integrated chance constraints (ICC) as quantitative risk constraints in asset and liability management (ALM) for pension funds. We define two types of ICC: the one period integrated chance constraint (OICC) and the multiperiod integrated chance constraint (MICC). As their names suggest, the OICC …
A new method uses GANs for robust optimization under uncertain data.
problem Optimizing supply chains under demand uncertainty with ambiguous distributions.
method Generative adversarial networks (GANs) for data-driven distributionally robust chance constrained programming.
result The approach effectively handles uncertain data distributions and improves supply chain optimization.
Deep invertible networks decode EEG signals better than chance.
problem Decoding brain signals from EEG data.
method Deep invertible networks for generating and classifying brain signals.
result Deep invertible networks generate realistic EEG signals and classify novel signals above chance.
We consider large-scale studies in which it is of interest to test a very large number of hypotheses, and then to estimate the effect sizes corresponding to the rejected hypotheses. For instance, this setting arises in the analysis of gene expression or DNA sequencing data. However, naive estimates of the effect sizes …
Study scaling of optimal solutions for reliability constraints in resource provisioning.
problem Achieving high reliability in resource provisioning under stringent requirements.
method Chance-constrained optimization, distributionally robust optimization, f-divergence balls, line search.
result Correct scaling properties of optimal decisions are preserved by using appropriate f-divergence balls, leading to conservative yet near-optimal solutions.
Modified SVM improves classification accuracy for imbalanced classes.
problem Improper SVM handling of class variances leads to misclassification.
method Adjust SVM margins to reflect class variances, proportional to standard deviation.
result Improved predictive performance for imbalanced classes.
USS fund risk assessment shows low default chance but high overfunding.
problem Risk assessment of Universities Superannuation Scheme (USS) fund.
method Estimates risk of default and overfunding using a cautious model.
result Fund has less than 7% chance of defaulting but overfunding by at least £100bn.
GP CC-OPF solves uncertain power grid optimization with Gaussian Process.
problem Uncertainty in power grid operations due to high renewables integration.
method Data-driven Gaussian Process regression for solving non-convex CC-OPF problem.
result Effective economic dispatch optimization in uncertain power grids.
Paper proposes a fast data-driven AC-OPF method using sparse hybrid Gaussian processes.
problem Optimizing electricity generation and delivery under generation uncertainty in modern power grids.
method Data-driven approach using sparse hybrid Gaussian processes to model power flow equations.
result Shows up to two times faster and more accurate solutions compared to state-of-the-art methods.
Bayesian method optimizes uncertain constraints in black-box function optimization.
problem Optimizing black-box functions with uncertain environmental variables.
method Distributionally robust chance-constrained Bayesian optimization.
result The method can find accurate solutions with high probability in a finite number of trials.
The paper critiques and expands on common evaluation metrics in machine learning.
problem The common evaluation metrics like Precision, Recall, F-Measure, and Rand Accuracy are biased and misleading.
method The paper introduces new measures like Informedness, Markedness, and Correlation to better reflect the quality of predictions.
result A system that performs worse in terms of Informedness can appear better using common measures like Precision and Recall.
Bayesian method approximates intractable stochastic programs with chance constraints.
problem Designing systems with stochastic constraints and chance constraints.
method Variational Bayesian approach to approximate posterior predictive integral.
result The solution set converges to the true solution set as the number of observations increases.
The paper uses machine learning to optimize rework policies in semiconductor manufacturing.
problem Optimizing rework steps to increase yield without increasing costs.
method Applied double/debiased machine learning (DML) to estimate treatment effects.
result Derived optimal rework policies and estimated their value empirically.
We use ellipsoids to solve power system voltage regulation problems.
problem Voltage regulation in power systems under uncertainty.
method Tractable ellipsoidal approximation for chance constrained optimizations.
result Efficiently trained machine learning model approximates uncertainty region.
Optimizes financial decisions with illiquid assets using Kelly criterion.
problem Determining optimal betting strategies in games with external capital constraints.
method Dynamic programming and WKB approximation for multi-round games; Kelly criterion for single-round games.
result Rational players adjust their risk-taking based on the proportion of their capital locked away.
Paper develops robust OPF method using contextual information.
problem Optimal Power Flow problem under incomplete uncertainty knowledge.
method Distributionally robust chance-constrained formulation with probability trimmings and optimal transport.
result Distributional robustness improves expected cost and system reliability.
G-computation improves clinical trial power with machine learning.
problem Balancing prognostic factors in randomized trials to prevent near-confounders.
method G-computation with penalized models (Lasso, Elasticnet) and algorithm-based methods (neural network, SVM, super learner).
result G-computation with Elasticnet and splines reduces variance and increases power in RCTs.
New study shows limits to classifying brain activity from randomized EEG trials.
problem Classifying human brain activity from image stimuli using EEG is challenging.
method Used randomized trials on a larger dataset (20x) to avoid stimulus-time confound.
result Classification accuracy is marginally above chance and statistically significant.
Develops a machine learning approach for solving AC-OPF problems.
problem Nonlinear and computationally demanding AC chance-constrained OPF problem.
method Uses Gaussian process regression to approximate AC power flow equations.
result Demonstrates competitive and promising results compared to state-of-the-art approaches.
The lottery ticket hypothesis finds multiple winning sub-networks in neural networks.
problem Finding a single winning sub-network in neural networks.
method Analyzing neural networks trained in isolation and on different tasks.
result Neural networks contain multiple sub-networks that match the accuracy of the original network, not just one.
Optimizes power systems with energy storage under uncertainty using scenario-based method.
problem Optimizing power systems with energy storage, intermittent renewable generation, and uncontrollable loads under uncertainty.
method Developed a novel solution method based on scenario optimization and strategic sampling to solve the chance-constrained optimal power system operation problem.
result The strategic sampling method significantly improves computational efficiency and data-driven convex approximation of power flow.
Generalizes information theory for hierarchical partitions.
problem Understanding hierarchical decomposition of complex systems.
method Introducing a generalization of information theory for hierarchical partitions, revisiting Hierarchical Mutual Information (HMI), and proving its bounds and transformations.
result Derives hierarchical generalizations of information-theoretic quantities, including a non-metric variation of information.