arXiv research

Framework for worst-case generation using Wasserstein space optimization.

In this paper we prove the probabilistic continuous complexity conjecture. In continuous complexity theory, this states that the complexity of solving a continuous problem with probability approaching 1 converges (in this limit) to the complexity of solving the same problem in its worst case. We prove the conjecture ho…

Proposes a new uncertain volatility model with worst-case scenario analysis.

Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert inf…

The study assesses external validity by evaluating worst-case treatment effects across subpopulations.

New framework improves worst-case generalization bounds for stochastic optimization.

Options are generally learned by using an inaccurate environment model (or simulator), which contains uncertain model parameters. While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case…

Study of linear classifiers in infinite imbalance scenarios.

Paper introduces DOO models to outperform SAA out-of-sample.

New approach limits regret in non-stationary bandits.

The paper analyzes extreme risk measures with limited distributional information.

Worst-Case Sensitivity measures model sensitivity to uncertainty set size.

Worst-case risk measures refer to the calculation of the largest value for risk measures when only partial information of the underlying distribution is available. For the popular risk measures such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), it is now known that their worst-case counterparts can be ev…

The book explores alternatives to worst-case analysis for algorithm performance.

Paper improves worst-case regret bounds for RLSVI in reinforcement learning.

Optimizes bond portfolios to avoid worst-case losses.

New framework assesses extreme errors in machine learning models.

This paper calculates worst-case target semi-variances for uncertain losses.

Bayesian optimization for function-valued responses, addressing worst case deviations.

Proposes a new framework for balancing average- and worst-case performance in machine learning.

This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness). Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial tra…

New framework identifies worst-case shifts for predictive resource allocation models.

Study approximates worst-case stock trading under uncertainty, quantifying sensitivity.

We consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common underlying dictionary. In particular, we derive lower bounds on the minimum achievable worst case mean squared error (MSE), regardless of computational complexity…

Study optimizes identifying the best arm with fixed rounds and Gaussian outcomes.

With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important area of research. However, most of the research literature merely focuses on the \textit{worst-case} setting where the input of the neural …

New algorithm reduces regret for kernelized bandits by adapting to specific problem instances.

Develops wcPCA for better low-rank approximations in heterogeneous domains.

A new algorithm avoids worst-case outcomes in risky contexts.

The paper analyzes worst-case distortion risk metrics and weighted entropy under partial information.

Improves neural network performance by enriching training dataset.

The study assesses how financial networks resist simultaneous price shocks and calculates the worst-case loss.

Paper derives best- and worst-case GlueVaR measures with incomplete data.

The paper shows how policy regularization acts like an adversary to improve robustness.

New method optimises worst-case risk under model uncertainty.

We study high-dimensional asymptotic performance limits of binary supervised classification problems where the class conditional densities are Gaussian with unknown means and covariances and the number of signal dimensions scales faster than the number of labeled training samples. We show that the Bayes error, namely t…

We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well…

A framework identifies worst-case decision points in safety-critical scenarios, improving risk assessment by 10 hours.

ISMCTS-BR learns best responses in large games, approximating worst-case performance.

Robust hypothesis testing designs a test for worst-case distributions using kernel methods.

Proposes a method to ensure low losses across all subpopulations in large datasets.

We analyze MDL for binary classification, quantifying overfitting and underfitting.

Overparameterized neural networks can be highly accurate on average on an i.i.d. test set yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups). Distributionally robust optimization (DRO) allows us to learn models that instead minimize…

By treating the financial market as a thermodynamic system, we establish a one-to-one correspondence between thermodynamic variables and economic quantities. Measured by the expected loss under the worst-case scenario, financial risk caused by model uncertainty is regarded as a result of the interaction between financi…

In three-dimensional computational topology, the theory of normal surfaces is a tool of great theoretical and practical significance. Although this theory typically leads to exponential time algorithms, very little is known about how these algorithms perform in "typical" scenarios, or how far the best known theoretical…

AI models solved the Kaczmarz algorithm's worst-case complexity.

In this paper we consider the worst-case model risk approach described in Glasserman and Xu (2014). Portfolio selection with model risk can be a challenging operational research problem. In particular, it presents an additional optimisation compared to the classical one. We find the analytical solution for the optimal …

Proposes a sensitivity framework to handle limited overlap in causal inference.