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.
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.
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.
A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid mode…
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.
The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co~event. The theory of experience and chance is the theory of c…
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.
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.
We propose a stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint of chance-constrained programs that seeks solutions on the efficient frontier of optimal objective value versus risk of constraint violation. …
Chances of a gambler are always lower than chances of a casino in the case of an ideal, mathematically perfect roulette, if the capital of the gambler is limited and the minimum and maximum allowed bets are limited by the casino. However, a realistic roulette is not ideal: the probabilities of realisation of different …
In this paper, a new approach to computing the generalisation performance is presented that assumes the distribution of risks, ρ(r), for a learning scenario is known. From this, the expected error of a learning machine using empirical risk minimisation is computed for both classification and regression problems. A cr…
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.
Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence…
CADR estimator improves inference for contextual bandit data.
problem Valid inference on contextual bandit data.
method CADR estimator for policy value, addressing adaptive data collection challenges.
result CADR provides correct coverage of confidence intervals.
The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimizat…
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.
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.
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.
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 …
This paper provides a non-robust interpretation of the distributionally robust optimization (DRO) problem by relating the distributional uncertainties to the chance probabilities. Our analysis allows a decision-maker to interpret the size of the ambiguity set, which is often lack of business meaning, through the chance…
Over the last decade there has been increasing concern about the biases embodied in traditional evaluation methods for Natural Language Processing/Learning, particularly methods borrowed from Information Retrieval. Without knowledge of the Bias and Prevalence of the contingency being tested, or equivalently the expecta…
Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during learning of new, disjoint knowledge. Here, we propose a new approach to sequential learning which leverages the recent discovery of adversari…
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.
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.
Language models fail to execute simple steps, showing gating and binding errors.
problem Procedural hallucinations in language models, failing to execute simple steps.
method Analyzed long-context binding tasks, identifying gating and binding errors.
result Procedural errors are due to gating and binding failures, with recency bias contributing to the latter.
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.
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.
In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or t…
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.
Improves logistic regression performance with nonconvex programming.
problem Stochastic generalized linear regression with chance constraints.
method Nonconvex programming techniques, clustering, quantile estimation.
result Over 1 to 2 percent improvement in model performance.
A statistical toolbox for analyzing model performance in medical imaging.
problem Analyzing model performance by patient and recording properties, especially in medical imaging.
method Selection of appropriate performance metrics, correction of multiple comparisons, and finding interesting subgroups.
result Enables rigorous assessment of model performance for potential subgroup disparities.
Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently-discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology…
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance. Further ideas for their regularization towards better decoding accuracies are discussed.
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.
Transformer models are compared to curved spacetime in General Relativity.
problem Understanding the geometric structure of Transformer models.
method Geometric analogy to General Relativity, focusing on attention and curvature effects.
result Transformer token embeddings exhibit curvature, affecting their evolution over layers.
Paper uses Gaussian processes to solve AC-OPF with renewable uncertainty.
problem Optimizing power grids with fluctuating renewable sources.
method Data-driven approach using Gaussian processes.
result Efficiently solves chance-constrained AC-OPF with uncertainty.
A fast bootstrap method estimates cross-validation standard error.
problem Uncertainty quantification in cross-validation estimates.
method Random-effects model to estimate variance component.
result Valid confidence intervals for model performance.
Investment challenge study finds luck and strategy equally important.
problem The role of luck and strategic considerations in M6 investment challenge performance.
method Introduced a stylized model to derive and analyze a portfolio strategy.
result Improving chances of winning without attaining abnormal returns possible.
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.