The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.
problem Modeling human-like decision-making in multi-agent systems with risk-seeking and loss-aversion behaviors.
method Forward policy design and inverse reward learning with iterative reasoning and cumulative prospect theory.
result The proposed algorithms demonstrate both risk-averse and risk-seeking behaviors in multi-agent systems.
Reduces risk of model inversion by reducing sensitive feature influence.
problem Model inversion attacks reveal sensitive individual data from trained models.
method Privacy-guided training to reduce sensitive feature influence in tree-based models.
result Training models to reduce sensitive feature influence reduces the risk of inference attacks.
FairICP addresses equalized odds fairness for multiple sensitive attributes.
problem Equalized odds fairness for multiple sensitive attributes.
method Adversarial learning with inverse conditional permutation.
result Promotes equalized odds under complex, multi-dimensional sensitive attributes.
A new method uses variational autoencoders to speed up greenhouse gas sensitivity calculations.
problem Computational inefficiency in generating LPDM sensitivities from gas mole fraction observations.
method Developed a convolutional variational autoencoder (CVAE) to emulate LPDM sensitivities in a low-dimensional space.
result The CVAE-based emulator outperforms traditional methods and can be applied to various LPDMs.
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, an…
New framework improves differential privacy for asymmetric datasets.
problem Improving differential privacy for asymmetric datasets.
method Adapts inverse sensitivity mechanism with sparse vector technique.
result Efficiently estimates general functions with improved privacy.
Optimizes insurance pricing by accounting for policyholders' price sensitivity.
problem Traditional insurance pricing does not consider policyholders' price sensitivity.
method Formulates insurance pricing as a decision-making problem and uses off-policy evaluation and stochastic control.
result Neural networks outperform existing techniques for policy optimization.
New diagnostic method detects misspecified models in inverse PDE problems.
problem Misleading residual-norm diagnostics in inverse PDE problems.
method Structure-sensitive sequential diagnostic using e-processes.
result Rejects fitted models that produce biased predictions.
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.
Iteratively reweighted least squares (IRLS) is a widely-used method in machine learning to estimate the parameters in the generalised linear models. In particular, IRLS for L1 minimisation under the linear model provides a closed-form solution in each step, which is a simple multiplication between the inverse of the we…
Develops new instance-optimality concepts in differential privacy.
problem Improving privacy guarantees in statistical estimation.
method Introduces local minimax risk and unbiased mechanisms, and develops inverse sensitivity mechanisms.
result Inverse sensitivity mechanisms are nearly instance optimal for a wide range of functions.
It is an open question whether there are Vassiliev invariants that can distinguish an oriented knot from its inverse, i.e., the knot with the opposite orientation. In this article, an example is given for a first order Vassiliev invariant that takes different values on a virtual knot and its inverse. The Vassiliev inva…
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assum…
In this paper we propose a transform method to compute the prices and greeks of barrier options driven by a class of Levy processes. We derive analytical expressions for the Laplace transforms in time of the prices and sensitivities of single barrier options in an exponential Levy model with hyper-exponential jumps. In…
A novel double-space tensor-product RKHS framework for hybrid uncertainty sensitivity analysis.
problem Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses.
method A novel double-space tensor-product RKHS framework for sensitivity analysis under hybrid uncertainty.
result Concurrent double Möbius inversion orthogonally decomposes global dependence measure into pure aleatory effects, pure epistemic effects, and their interaction contributions.
Novel PCA method for high-dimensional inverse problems.
problem Optimizing large-scale random fields with gradient information.
method Gradient-Sensitive Principal Component Analysis (Gradient-SPCA) that modifies PCA using objective function gradients.
result Improvements in encoding quality for objective function minimization and field distribution.
New method optimizes hyperparameters for randomized algorithms like random feature regression.
problem Optimizing hyperparameters in randomized algorithms is challenging due to their stochastic nature.
method Introduced a random objective function and used ensemble Kalman inversion (EKI) for gradient-free optimization.
result Demonstrated successful optimization of hyperparameters in various randomized algorithms.
New attack recovers user-level information from large batch images.
problem Recovering private information from user-level gradients in distributed learning.
method Proposes a gradient inversion attack using a denoising diffusion model as a prior.
result Demonstrates recovery of realistic facial images and private attributes.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often involve sensitive information, thus far there has been no work on privacy-preserving IPW methods. We add…
FSIR extends SIR for federated learning with privacy and efficiency.
problem Privacy-preserving dimension reduction in federated learning.
method FSIR employs sliced inverse regression with differential privacy and collaborative variable screening.
result FSIR achieves effective dimension reduction and privacy protection in federated learning.
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.
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.
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
This work enhances collaborative inference privacy by minimizing conditional entropy and boosting robustness against model inversion attacks.
problem Privacy leakage in collaborative inference systems via model inversion attacks.
method Theoretical proof and derivation of a differentiable measure for bounding conditional entropy, followed by a CEM algorithm to maximize it.
result Theoretical proof and experimental validation show that CEM consistently boosts inversion robustness without compromising feature utility or efficiency.
Recent works have demonstrated that machine learning models are vulnerable to model inversion attacks, which lead to the exposure of sensitive information contained in their training dataset. While some model inversion attacks have been developed in the past in the black-box attack setting, in which the adversary does …
DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.
problem Recovering optimal stopping region from expert trajectories with unknown gain functions.
method Dynamics-Aware Offline Inverse Q-Learning incorporating temporal information and confidence-based oversampling.
result Demonstrated performance on real and artificial data, including optimal intervention for critical events.
Study uses neural fields to improve geophysical inversions by reducing artifacts.
problem Improving geophysical inversions by reducing artifacts and improving model recovery.
method Employing neural fields for test-time learning in geophysical inversions.
result Test-time learning with neural fields eliminates unwanted artifacts in recovered models.
This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data. Since its first introduction, such attacks have raised serious concerns given that training data usually contain privacy-sensitive information. Thus far, successful model-inversion attacks …
Improved method for private quantile estimation in datasets with atoms.
problem Private estimation of quantiles in datasets with atoms.
method Application of Inverse Sensitivity mechanism and Heuristically Smoothed JointExp.
result Improved performance on datasets with atoms compared to existing methods.
A new PGA algorithm ensures stable, robust, and noise-immune solutions for non-negative inverse problems.
problem Stable convergence and suboptimal solutions in inverse problems due to negative values and high sensitivity to hyperparameters.
method A novel multiplicative update proximal gradient algorithm (SSO-PGA) that enforces non-negativity and boundedness through a learnable sigmoid-based operator.
result Significantly surpasses traditional PGA and other state-of-the-art algorithms in performance and stability.
Differentially private method for estimating individualized treatment rules.
problem Estimating individualized treatment rules while preserving privacy.
method Differentially private two-stage empirical risk minimization (DP-2ERM).
result Improved privacy-utility trade-off demonstrated through simulations and applications.
We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks. Combining energy landscape techniques developed in computational chemistry with tools drawn from formal methods, we produce empirical evidence indicating that networks corresponding to lower-lying minima in the opt…
New method addresses error bounds for PnP-ULA under mismatched models.
problem Error bounds for PnP-ULA under mismatched measurement and prior models.
method Posterior-L2 pseudometric to quantify error bounds.
result Explicit error bound for PnP-ULA under mismatched posterior distribution.
Inverse classification is the process of perturbing an instance in a meaningful way such that it is more likely to conform to a specific class. Historical methods that address such a problem are often framed to leverage only a single classifier, or specific set of classifiers. These works are often accompanied by naive…
PIE-PINN estimates elastic properties from noisy, low-res displacement data.
problem Estimating heterogeneous elastic properties from low-resolution, noisy data.
method Probabilistic Physics-Informed Neural Network (PIE-PINN) framework combining B-spline and hierarchical scale model.
result Robust estimation of Young's modulus and Poisson's ratio from noisy, low-resolution displacement data.
In solving a system of n linear equations in d variables Ax=b, the condition number of the n,d matrix A measures how much errors in the data b affect the solution x. Estimates of this type are important in many inverse problems. An example is machine learning where the key task is to estimate an underlyin…
Study recovers investor preferences from portfolio data using synthetic data and robust optimization.
problem Recovering latent investor preferences from observed portfolio allocations under uncertainty.
method Inverse portfolio optimization framework integrating robust optimization and regret-based inference.
result Accurate recovery of transaction cost parameters and partial identifiability of ESG penalties under preference misspecification and market shocks.
New method learns optimal variance schedule for diffusion models.
problem Diffusion models' sensitivity to variance schedule.
method Probabilistic conditioning, learning schedule during training.
result Comparable or superior results in super-resolution microscopy and quantitative phase imaging.
The paper explores privacy-preserving methods for counting unique elements in distributed settings.
problem Counting unique elements in a distributed setting while maintaining privacy.
method Analyzes and proves lower bounds for differentially private protocols in various settings.
result Achieves optimal error bounds for multi-message shuffle protocols in estimating distinct elements.
Jointly correct bias fields and reconstruct undersampled MRI images.
problem Recovering fully sampled MRI images from undersampled data while accounting for bias field differences.
method An unsupervised learning-based reconstruction algorithm combined with a N4-based bias field estimation method in a joint optimization scheme.
result The proposed method improves reconstruction quality, both visually and in terms of RMSE.
New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and institutions have raised concerns about the …
Differentiable programming aids in solving differential equations and their sensitivities.
problem Computing gradients of numerical solutions of differential equations.
method Review of existing techniques and mathematical foundations.
result Established a coherent framework for combining differential equations with data-driven approaches.
Distortion (Denneberg 1990) is a well known premium calculation principle for insurance contracts. In this paper, we study sensitivity properties of distortion functionals w.r.t. the assumptions for risk aversion as well as robustness w.r.t. ambiguity of the loss distribution. Ambiguity is measured by the Wasserstein d…
Bayesian framework improves minority class performance in class-imbalanced data.
problem Class imbalance in predictive toxicology models.
method Weighted likelihood approach modifying likelihood function weights inversely proportional to class proportions.
result Improves balanced accuracy and sensitivity for minority class (toxic compounds).
This research reverses feature visualization in neural networks to optimize for specific feature objectives.
problem The invertibility of feature visualization in neural networks is not well understood.
method The approach involves optimizing for the feature objective that generates the input used in feature visualization, using the gradient of a specific objective function.
result A closed-form solution is found to minimize the gradient, providing an alternative view on network sensitivity.
Paper proposes hybrid modeling to improve surrogate accuracy using multiple data sources.
problem Improving surrogate model accuracy by integrating simulation and real-world data.
method Two novel probabilistic approaches: separate and combined surrogates with weighting strategy.
result Hybrid models improve predictive accuracy and coverage compared to single-source surrogates.
Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain da…