A new method codes nominal data as complex numbers for better classification.
problem Losing information in nominal data coding for effective classification.
method Assigning a rank as a complex number to nominal data for classification.
result Classification with coded nominal data is more effective than with only numerical data.
New schemes improve vertex nomination in stochastic block models.
problem Ordering vertices with unknown labels in a network.
method Canonical sampling and extended spectral nomination schemes.
result Improved precision and scalability of vertex nomination schemes.
The paper introduces subgraph nomination for finding similar subgraphs in networks.
problem Finding similar subgraphs in networks using example subgraphs.
method Formalizes subgraph nomination framework with user-supervised retrieval.
result User-supervised retrieval improves performance in subgraph nomination.
Suppose that a graph is realized from a stochastic block model where one of the blocks is of interest, but many or all of the vertices' block labels are unobserved. The task is to order the vertices with unobserved block labels into a ``nomination list'' such that, with high probability, vertices from the interesting b…
Constructs rational models for pricing and managing inflation-linked derivatives.
problem Pricing and risk management of inflation-linked derivatives.
method Rational models constructed in a multiplicative manner, isolating inflation convexity-adjustment.
result Closed-form pricing of various inflation products and exotic swaps.
Develops FSC for maxima nominated samples, improving classification in rare-event data.
problem Combining labeled and unlabeled data in rare-event scenarios.
method Introduces a latent representation to account for maxima nomination sampling.
result Improves classification performance in rare-event contamination mixtures.
The paper extends vertex nomination schemes to general graph models and explores consistency.
problem Finding corresponding vertices in a network when given a vertex of interest.
method Extended statistical model of graphs, definitions of Bayes optimality and consistency, derivation of Bayes optimal scheme, proof of no universally consistent schemes.
result No universally consistent vertex nomination schemes exist.
Paper proves ML-based vertex nomination is consistent and scalable.
problem Ordering non-interesting vertices to highlight interesting ones in graphs.
method Maximum likelihood estimation and vertex nomination scheme.
result ML-based scheme asymptotically matches Bayes optimal scheme performance.
Effective nonparametric anomaly detection for high-dimensional data.
problem Timely detection of abrupt anomalies in high-dimensional data.
method Nonparametric algorithms using univariate summary statistics and submanifold learning.
result Asymptotic guarantees for accurate anomaly detection.
Two-stage recommender systems show better performance when components interact rather than operate independently.
problem Two-stage recommender systems are often treated as sums of their parts, ignoring interactions between components.
method Used synthetic and real-world data to demonstrate interactions between ranker and nominators. Derived a generalization lower bound and proposed a Mixture-of-Experts approach to learn optimal item pools.
result Independent nominator training can lead to performance on par with random recommendations, highlighting the importance of interactions.
Financial networks' dynamics linked to economic fundamentals across countries.
problem Understanding financial sector dynamics and their relation to economic fundamentals.
method Constructed return correlation networks from daily data, analyzed centrality and clustering, and used market metrics to identify sector importance.
result Sector-level financial dynamics are anchored to economic size, influencing portfolio optimization.
The paper tackles VN with multiple vertices of interest and adversarial contamination.
problem Finding corresponding vertices in a graph when some vertices are contaminated.
method Bayes optimality, maximal consistency classes, adversarial contamination model, network regularization.
result VN schemes perform well in uncontaminated settings but are adversely impacted by adversarial contamination.
The paper explores how to find relevant vertices in one graph using another graph's attributes and structure.
problem Finding relevant vertices in one graph using another graph's attributes and structure.
method Theoretical and practical exploration of vertex nomination schemes that leverage both content (edge and vertex attributes) and context (network topology).
result Necessary and sufficient conditions for schemes that use both content and context to outperform those using only one.
Optimistic likelihoods improve classification accuracy by considering nearby distributions.
problem Evaluating likelihoods of nominal distributions estimated from data, which can be inaccurate.
method Use ambiguity sets and geodesic/standard convex optimization to compute optimistic likelihoods.
result Optimistic likelihoods lead to better classification performance.
A method for ranking items using distance-based learning from positive and unlabeled data.
problem Learning to rank items without an analytic description of what constitutes a good ranking.
method Combining representations using an integer linear program for ranking items based on nominations.
result The method is effective in simulation and real data examples, especially when supervision is light.
When response variables are nominal and populations are cross-classified with respect to multiple polytomies, questions often arise about the degree of association of the responses with explanatory variables. When populations are known, we introduce a nominal association vector and matrix to evaluate the dependence of …
Exact distribution of split conformal prediction coverage found.
problem Determining the reliability of prediction sets in batch mode.
method Analysis of exchangeable data to find universal distribution of empirical coverage.
result Exact distribution of empirical coverage is universal and determined by nominal miscoverage level and calibration sample size.
New model detects communities in network data from edge nominations.
problem Noise and bias in network data from edge nominations.
method General model for network sampling, spectral clustering, method of moments.
result Community detection improved for network data collected via edge nominations.
In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal- Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal…
New method trims network data to resist adversarial contamination.
problem Adversarial contamination in network data affects statistical and algorithmic performance.
method Proposes a new trimming method operating in model space to address both block and white noise contamination.
result Demonstrates superior performance in simulations compared to direct trimming.
Proposes a new framework for uncertainty evaluation in ML classification models.
problem Uncertainty evaluation for ML classification models not addressed by existing metrological guidelines.
method Develops a metrological framework based on probability mass functions and summary statistics.
result Extends the GUM to uncertainty for nominal properties, applicable to ML classification models.
Model ranks abstract anaphors based on their relation to antecedents.
problem Resolving abstract anaphora in text understanding.
method LSTM-Siamese Net learns mention-ranking through artificial data.
result Model outperforms state-of-the-art on shell noun resolution.
System identifies power grid location from media recordings.
problem Identifying the origin of power distribution grid from media recordings.
method Cascaded SVM and pole-matching classifiers for grid identification.
result Cascaded system improves accuracy by 15.57%.
New privacy-preserving method for conformal prediction without splitting data.
problem Privacy and uncertainty quantification in data-driven decision making.
method Proposes a full-data privacy-preserving conformal prediction framework using differential privacy.
result Demonstrates improved prediction sets compared to split-based private baselines.
Investigates optimal life insurance and annuity decisions in inflationary economies.
problem Optimal consumption and investment decisions in an inflationary economy with money illusion.
method Formulated as a random horizon utility maximization problem, derived optimal strategy.
result Money illusion increases life insurance demand for young adults and reduces annuity demand for retirees.
This study uses RNNs to diagnose faults in underwater thrusters.
problem Fault detection and diagnosis of underwater thrusters in harsh marine environments.
method Data-driven fault detection using Recurrent Neural Networks (RNNs) with empirical data.
result RNNs outperform residual-based feature extraction for fault classification.
Responds to critiques on tests for causal parameter confidence intervals.
problem Testing nominal confidence interval coverage for causal parameters estimated by machine learning.
method Rejoinder to critiques on nearly assumption-free tests.
result Clarifies and supports the original research's approach.
New RL algorithm learns robust policies without knowing nominal model.
problem Designing robust policies for RL with unknown nominal model.
method Model-based RL algorithm with three uncertainty set forms.
result Precise sample complexity for each uncertainty set.
Two-stage recommender systems struggle with exploration, leading to linear regret.
problem Linear regret in two-stage recommender systems due to exploration issues.
method Proposed a method to synchronize exploration strategies between the ranker and nominators using LinUCB.
result Demonstrated the effectiveness of the proposed algorithm experimentally.
Proposes using Wasserstein barycenters for robust optimization with multiple data sources.
problem Distributionally robust optimization with multiple heterogeneous data sources.
method Construct nominal distribution through Wasserstein barycenter of multiple data samples, reformulates as a finite convex program.
result Proposed scheme outperforms other estimators in sparse inverse covariance matrix estimation.
Enhances deep learning models for anomaly detection in time series data.
problem Anomalies in time series data corrupt performance of models.
method Monte Carlo EM for inferring anomaly indicators during training.
result Improves model performance on nominal data and anomalous points.
Selective inference framework for CART trees to control error rates and coverage.
problem Inference on CART trees does not control Type 1 error rates and coverage.
method Selective inference framework conditioning on tree estimation, efficient algorithms.
result Proposes tests and intervals for CART trees with selective error control.
Generalizes causal inference to high-dimensional outcomes.
problem Limited causal inference methods for multivariate outcomes.
method Formulates causal discrepancy tests for nominal variables, uses conditional independence tests.
result Causal CDcorr method improves finite sample validity and power.
Adversarial robustness improved by abstaining from decisions.
problem Improving classification accuracy in the presence of adversarial perturbations.
method Introducing an abstain option in binary classification problems, using metrics to quantify performance and robustness.
result There is a tradeoff between nominal performance and adversarial robustness.
In this paper we introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous- time information processes that describe the flow of information about market factors in a monetary economy. The nominal pricing kernel is at any given time assumed to be given by …
Combines robust optimization and bootstrap to create prescriptive analytics.
problem Optimal decision-making in uncertain environments with noisy data.
method Combines robust optimization and bootstrap methods.
result Robust prescriptive methods reduce overfitting and generalize better.
This paper compares VaR estimation methods under tail misspecification, finding importance sampling underestimates VaR.
problem Tail misspecification in VaR estimation.
method Importance sampling and moment-based VaR bracketing.
result Importance sampling underestimates VaR under heavy-tailed returns, while moment-based methods are robust.
A method for matching vertices in large networks using seeds.
problem Matching vertices in large, overlapping networks.
method Identify seeds in local neighborhoods, match induced subgraphs, rank matches.
result Principled approach for large networks, demonstrated through simulations and real data.
Generative models help make decisions under changing data distributions.
problem Making decisions based on historical data when the actual data distribution changes.
method Flow- and score-based generative models to represent and transform distributions.
result Generative models can learn nominal uncertainty, create stressed distributions, and produce conditional distributions.
The paper reviews and extends calibration concepts for classification and regression.
problem Formalizing compatibility between probabilistic predictions and outcomes.
method Review and extension of existing calibration concepts, introduction of new concepts.
result Hierarchical relations between calibration concepts for various data types.
Novel variational autoencoder for generative and classification tasks.
problem Developing a robust generative model for various tasks.
method A novel variational autoencoder with specific latent variables and ordinality enforcement.
result Comparable performance in generative and classification tasks compared to baselines.
The paper uses EVT to improve tail risk measures under ambiguity sets.
problem Misspecification of tail risk measures leads to inflated risk estimates.
method Applies Extreme Value Theory to derive worst-case tail risk under ambiguity sets.
result Proposes a tail-calibrated ambiguity design that preserves nominal tail asymptotic scaling.
This work proposes robust reinforcement learning methods using both offline and online data.
problem Designing robust policies against parameter uncertainties in high-dimensional systems.
method Proposes RPQ for model-free learning with historical data and HyTQ for hybrid learning with both historical and online data.
result Unified analysis and theoretical guarantees for robust optimal policies in high-dimensional systems.
PANDA augments data to regularize GLM estimation and inference.
problem Regularizing estimation and inference in GLMs with noisy data.
method Iteratively optimizes augmented noise data to converge to regularized model estimates.
result Established convergence and asymptotic distributions for regularized parameters.
Proposes robust assortment optimization from observational data.
problem Real-world scenarios often violate assumptions of stable customer preferences and correct choice models.
method Develops a robust framework that accounts for potential distributional shifts in customer choice behavior.
result Uncovered the notion of ``robust item-wise coverage'' as the minimal data requirement for sample-efficient robust assortment learning.
Generative Distributionally Robust Optimization (GDRO) improves model compatibility and adversarial structure in DRO.
problem Trade-off between model compatibility and adversarial structure in existing DRO methods.
method GDRO accepts any sampleable conditional generator and restricts worst-case laws to a chosen family, using sampler-Sinkhorn pairing.
result Reduces inventory regret by 60% and navigation collisions by 50% relative to nominal decisions.
The paper analyzes a five-factor capital market model and facilitates exact simulation.
problem Analyzing and simulating a five-factor capital market model.
method Using a Vasicek interest rate model, mean-reverting excess return, and realized inflation with expectation, the paper derives the necessary distributional results and describes practical methods to overcome rank deficiency.
result Exact simulation from the model can be achieved by sampling from a seven-dimensional normal distribution.
New robust control method for uncertain systems using bootstrapped noise.
problem Designing controllers robust to model uncertainties in finite data.
method Least-squares model estimator, bootstrap resampling, multiplicative noise LQR.
result Significantly outperforms certainty equivalent controllers in numerical tests.