Improved bounds for ℓp sensitivity sampling reducing the sample complexity for structured matrices.
problem Improving the sample complexity for structured matrices using ℓp sensitivity sampling. method Developed new bounds for ℓp sensitivity sampling, achieving a bound of roughly S2−2/p for 2<p<∞. result Achieved improved bounds for ℓp sensitivity sampling, reducing the sample complexity for structured matrices. Improved subsampling bounds for ℓp sensitivity sampling using ℓ2 augmentation.
problem Efficiently approximating large data sets by small representative proxies.
method Optimized sampling based on ℓp and ℓ2 sensitivities. result Optimal linear ildeO(ε−2(S+d)) sampling complexity for all p∈[1,2]. Given a loss function F:X→R+ that can be written as the sum of losses over a large set of inputs a1,…,an, it is often desirable to approximate F by subsampling the input points. Strong theoretical guarantees require taking into account the importance of each point, measured by how …
Method differentiates diffusion model training to predict sample sensitivity.
problem Predict how diffusion model samples change with small perturbations.
method Closed-form procedure for computing directional derivatives of the map.
result Estimates sensitivity of diffusion model samples to additive perturbations.
Active sampling improves design space exploration for analog circuits.
problem Efficiently exploring the space of design features in analog circuits with many parameters.
method Combining drastic dimension reduction with sensitivity analysis and Bayesian surrogate modeling for active sampling.
result The proposed active sampling flow outperforms traditional Monte-Carlo sampling.
The study sets lower bounds on MMSE for inferring sensitive features from noisy data.
problem Estimating sensitive features from noisy observations of correlated features.
method Adversarial evaluation framework based on MMSE estimation with theoretical lower bounds.
result Derives closed-form bounds for linear models, showing optimality in noise variance.
Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense me…
Enhances sensitivity analysis for correlated inputs.
problem Estimating sensitivity indices in models with correlated inputs.
method Proposes an extension of Sobol' estimator using a linear correlation model.
result Improves accuracy in variance-based sensitivity analysis.
Framework for sensitivity analysis in biomanufacturing processes.
problem High complexity and uncertainty in biomanufacturing processes.
method Shapley value estimation for linear and nonlinear pKG models, using quasi-Monte Carlo and antithetic sampling.
result Improved efficiency and accuracy in sensitivity analysis for biomanufacturing processes.
Recent reports have described that learning Bayesian networks are highly sensitive to the chosen equivalent sample size (ESS) in the Bayesian Dirichlet equivalence uniform (BDeu). This sensitivity often engenders some unstable or undesirable results. This paper describes some asymptotic analyses of BDeu to explain the …
Diffusion models accurately recover mixture weights from generated samples despite score function insensitivity.
problem Score-based generative models often fail to learn correct relative mode amplitudes (mixture weights) from generated samples.
method Relate diffusion score matching (DSM) loss to mixture weight estimation error, define diffusion score sensitivity index (DSSI), and prove its governing role in mixture weight recovery.
result Generated samples can accurately recover mixture weights from the DSM loss, even when the target score is insensitive to mixture weights.
Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private function. Bounding such sensitivity is often a prohibitively complex analytic calculation. As an alternative, we propose a straightforward sampl…
Two formulae estimate sensitivity of random vectors to distributional parameters.
problem Estimating sensitivity of random vectors to distributional parameters.
method Two analytical formulae and four numerical algorithms.
result Validated numerical algorithms and demonstrated effectiveness.
This paper presents efficient sampling methods for Gaussian processes.
problem High cost of global sensitivity analysis and optimization due to limited high-quality observations.
method Two sampling methods: random Fourier features and pathwise conditioning.
result Efficient generation of posterior samples from Gaussian processes at reduced computational cost.
Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.
problem Asymmetric binary classification problems with unequal error severities.
method Develops TUBE-CS algorithm to bridge cost-sensitive and Neyman-Pearson paradigms.
result High-probability control of population type I error.
New method speeds up causal sensitivity analysis.
problem Bounding causal effects in unobserved confounding.
method Amortized approach using prior-data fitted networks.
result Orders of magnitude faster computation.
This work provides efficient algorithms for approximating ℓ_p sensitivities and related statistics.
problem Estimating the importance of datapoints in high-dimensional datasets.
method Efficient algorithms for computing α-approximation of ℓ_1 sensitivities and total sensitivity using importance sampling and sensitivity computations.
result Real-world datasets have significantly lower intrinsic effective dimensionality than theoretical predictions.
Kernel dependence measures yield accurate estimates of nonlinear relations between random variables, and they are also endorsed with solid theoretical properties and convergence rates. Besides, the empirical estimates are easy to compute in closed form just involving linear algebra operations. However, they are hampere…
Study risk-sensitive reinforcement learning with Lipschitz dynamic risk measures, establishing regret bounds.
problem Risk-sensitive reinforcement learning in Markov decision processes.
method Two model-based algorithms for Lipschitz dynamic risk measures, focusing on regret bounds.
result Upper bounds demonstrate optimal dependencies on actions and episodes, reflecting risk sensitivity vs. sample complexity trade-off.
Improves generative models for cost-sensitive decisions.
problem Generative models lack awareness of decision costs.
method Integrates a decision loss into the training objective.
result Improves cost-sensitive forecast accuracy.
A new family of nonparametric statistics, the r-statistics, is introduced. It consists of counting the number of records of the cumulative sum of the sample. The single-sample r-statistic is almost as powerful as Student's t-statistic for Gaussian and uniformly distributed variables, and more powerful than the sign and…
New algorithm for risk-sensitive reinforcement learning with natural policy gradients.
problem Risk-sensitive reinforcement learning with downside risk constraints.
method Introduce a new Bellman equation to estimate the lower partial moment of returns, use natural policy gradients, and extend Reward Constrained Policy Optimization.
result Sample-efficient estimation of partial moments and effective risk-sensitive control.
Sobol method applied to probabilistic networks for sensitivity analysis.
problem Measuring influence of probabilistic network nodes on a quantity of interest.
method Transforms global sensitivity analysis into marginalization inference exploiting network structure.
result Efficient computation of sensitivity indices for complex networks.
This paper proposes CSADA to make DNNs cost-sensitive.
problem Over-parameterization challenges cost-sensitive classification in DNNs.
method CSADA framework using adversarial data augmentation.
result CSADA effectively minimizes overall cost and reduces critical errors.
Novel framework for risk-sensitive reinforcement learning with robustness against uncertainty.
problem Risk-sensitive reinforcement learning with uncertainty in transition dynamics.
method Developed a risk-sensitive robust Markov decision process (RSRMDP), derived its Bellman equation, and proposed a Bayesian Dynamic Programming (Bayesian DP) algorithm.
result Demonstrated convergence to near-optimal policies and analyzed sample and computational complexities.
Unified framework for causal inference under sample selection.
problem Causal inference under sample selection with treatment and outcome non-randomness.
method ForestRiesz estimator, Riesz representation framework.
result ForestRiesz estimator yields more stable treatment effect estimates than conventional double machine learning approaches.
Fairness is becoming a rising concern w.r.t. machine learning model performance. Especially for sensitive fields such as criminal justice and loan decision, eliminating the prediction discrimination towards a certain group of population (characterized by sensitive features like race and gender) is important for enhanci…
Improved sample complexity for identifying best policies in risk-sensitive reinforcement learning.
problem Identifying approximately optimal policies in risk-sensitive reinforcement learning with exponential horizon dependence.
method Forward-model based algorithm with KL-based exploration bonuses adapted for entropic criterion, leveraging smoothness properties of exponential utility and a new stopping rule.
result Achieved sample complexity matching the lower bound, closing the gap between upper and lower bounds.
New algorithms optimize risk in reinforcement learning with exponential utility.
problem Optimizing rewards under risk in reinforcement learning with unknown transition kernels.
method Two model-free algorithms: Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ).
result Proved near-optimal regret bounds for RSVI and RSQ.
Study risk-sensitive RL in offline settings, improving efficiency and accuracy.
problem Efficiently derive near-optimal policies for risk-sensitive RL using offline data.
method Introduced two provably sample-efficient algorithms for risk-sensitive offline RL in linear MDPs.
result First provably efficient risk-sensitive offline RL algorithms.
Paper introduces risk-sensitive bandits with optimal arm mixtures.
problem Designing algorithms for risk-sensitive multi-armed bandits.
method Formalizes risk-sensitive bandits framework, identifies optimal arm mixtures, designs regret-efficient algorithms.
result Regret-efficient algorithms track optimal arm mixtures or solitary arms.
New method calculates sensitivity of system failure probability.
problem Difficulty in computing sensitivity of failure probability.
method Monte Carlo strategy using response gradient and kernel smoothing.
result Single Monte Carlo run for sensitivity estimates.
Study risk-sensitive reinforcement learning with entropic risk measures and generative models.
problem Risk-sensitive reinforcement learning in discounted MDPs with recursive entropic risk measures.
method Introduced Model-Based ERM Q-Value Iteration (MB-RS-QVI) and derived PAC bounds on sample complexity for value and policy learning. result PAC bounds show exponential dependence on ∣β∣/(1−γ), with tight bounds in S and A. An ε-coreset for Least-Mean-Squares (LMS) of a matrix A∈Rn×d is a small weighted subset of its rows that approximates the sum of squared distances from its rows to every affine k-dimensional subspace of Rd, up to a factor of 1±ε. Such coresets are useful…
Improved sample efficiency for private learning of Gaussian mixtures.
problem Learning mixtures of Gaussians with differential privacy.
method Inverse sensitivity mechanism, sample compression, sumset volume bounds.
result Proved optimal sample complexity for private learning of mixtures of Gaussians.
Develops scalable methods to assess sensitivity and uncertainty in continuous treatment effects.
problem Estimating effects of continuous-valued interventions from observational data, especially when ignorability and positivity assumptions are violated.
method Continuous treatment-effect marginal sensitivity model (CMSM), scalable algorithm, uncertainty-aware deep models.
result Derives bounds that agree with observed data and a defined level of hidden confounding.
Adjoint SA speeds up bioprocess parameter learning.
problem Challenges in digital twin development for biomanufacturing.
method Adjoint sensitivity analysis on multi-scale enzymatic reaction networks.
result Resilient sensitivities reveal bioprocess regulatory mechanisms.
New Monte Carlo method outperforms existing strategy for estimating Sobol' indices.
problem Estimating first-and total-orders Sobol' indices accurately.
method Comparing two Monte Carlo estimators for Sobol' indices.
result New method outperforms current approach in accuracy.
Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.
problem Optimizing portfolios with complex asset dynamics and common drivers.
method Model asset dynamics with PDEs, approximate sensitivities with neural networks, and use hierarchical clustering on sensitivity matrix for optimization.
result Achieves over-performance in portfolio optimization across various markets and datasets.
In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that 0-1 Bayes classifiers are not (uniform) noise robust in cost sensitive setting. To circumvent this impossibility result, we present two schemes; un…
Enhances GNNs by capturing node relationships, outperforming 2-WL test.
problem Inability of conventional GNNs to fully capture node relationships due to permutation invariance.
method Develops permutation-sensitive aggregation mechanism using permutation groups.
result Proves superior expressivity compared to 2-WL test and not less than 3-WL test.
This paper introduces a method to incorporate risk sensitivity in RL using quadratic variation penalties.
problem Risk-sensitive reinforcement learning under entropy regularization.
method Equivalent martingale property and quadratic variation penalty for value process.
result The proposed method improves finite-sample performance in linear-quadratic control problems.
BDeu marginal likelihood score is a popular model selection criterion for selecting a Bayesian network structure based on sample data. This non-informative scoring criterion assigns same score for network structures that encode same independence statements. However, before applying the BDeu score, one must determine a …
Method quantifies sensitivity of reliability analysis to uncertainty sources.
problem Computational expense in reliability analysis of complex models.
method Gaussian process surrogate model, active learning, sensitivity analysis.
result Reduces main source of error in estimating rare event probabilities.
Efficiently identifies key input variables for expensive functions using active learning.
problem Efficiently identify key input variables for expensive, black-box functions.
method Proposes novel active learning acquisition functions targeting derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models.
result Active learning substantially enhances sample efficiency of DGSM estimation, especially with limited evaluation budgets.
Paper proposes a new FRL algorithm for continuous sensitive attributes using EIPM.
problem Existing FRL algorithms cannot handle continuous sensitive attributes.
method Introduces EIPM to assess fairness in representation space for continuous attributes and proposes FREM algorithm.
result FREM outperforms other methods in fairness evaluation for continuous sensitive attributes.
Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. We introduce a novel perturbation manifold …
Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates under the assumption that the sensitive feature available in one's training sample is perfectly reliable. This assumption may be violated in…