PROVEN extends neural network robustness verification to probabilistic settings.
problem Quantifying robustness of neural networks under probabilistic noise distributions.
method PROVEN: Probabilistic Verification of Neural Networks (PROVEN) with statistical guarantees.
result PROVEN achieves up to 75% improvement in robustness certification compared to worst-case methods.
This research focuses on invariant probabilistic predictions, showing they are not robust under distribution shifts.
problem The challenge of creating robust probabilistic predictions that remain consistent under distribution shifts.
method A causality-inspired framework to investigate invariance and robustness of probabilistic predictions with respect to proper scoring rules.
result Arbitrary distribution shifts do not admit invariant and robust probabilistic predictions, unlike point predictions.
New probabilistic approach to neural network robustness.
problem Challenges in verifying neural network robustness.
method Probabilistic robustness approach, abstract interpretation, importance sampling.
result Practical algorithm for checking probabilistic robustness of neural networks.
Estimates robustness of BNNs with statistical guarantees.
problem Measuring robustness of BNNs against adversarial examples.
method Statistical verification techniques for probabilistic models.
result Quantifies uncertainty of BNN predictions in adversarial settings.
Geometric approach improves probabilistic robustness in neural networks.
problem Widespread lack of robustness in deep neural networks to adversarial examples.
method Geometric view on Probabilistically Robust Learning (PRL) and introduction of new perimeters.
result Existence of solutions and properties of modified PRL models.
FedGVI improves FL robustness to model misspecification.
problem Limited robustness in FL approaches to model misspecification.
method Probabilistic Federated Learning framework that generalizes previous methods.
result FedGVI provides robust and calibrated predictions under model misspecification.
Efficiently certifies global robustness of large neural networks with probabilistic guarantees.
problem Certifying robustness of large neural networks in a scalable and efficient manner.
method Sampling an ε-net and invoking a local robustness oracle.
result Certifies a probabilistic relaxation of robustness efficiently and globally.
Probabilistic STNs improve image classification and robustness.
problem Training and robustness issues in STNs.
method Probabilistic extension of STNs that estimates stochastic transformations.
result Improved classification performance, robustness, and model calibration.
PRoA assesses deep learning robustness against practical functional perturbations.
problem Inadequate practical robustness verification methods for deep learning systems.
method Probabilistic robustness assessment based on adaptive concentration.
result Statistical guarantees on probabilistic robustness against functional perturbations.
ptype infers data types robustly in real-world data.
problem Type inference fails with missing data and anomalies.
method Probabilistic robust type inference method.
result Outperforms existing methods.
Unified view linking differential privacy and adversarial robustness.
problem Combining differential privacy and adversarial robustness in machine learning.
method Abstracting definitions and using probabilistic mappings and Renyi divergence.
result Results from one domain can be transferred to the other.
Paper introduces Prob-SSI for robust OMA in noisy data.
problem Challenges in estimating modal parameters from noisy data.
method Probabilistic formulation of SSI, robust Prob-SSI algorithm.
result Robust Prob-SSI outperforms conventional SSI in corrupted data.
New metrics improve probabilistic forecasting, especially for rare events.
problem Current evaluation frameworks for probabilistic forecasting assume independence and lack sensitivity to tail events.
method Proposed signature kernel-based metrics: Sig-MMD and CSig-MMD.
result These metrics capture complex dependencies and prioritize tail event prediction.
COLEP improves robustness of conformal prediction via probabilistic circuits.
problem Adversarial perturbations can undermine the coverage guarantees of conformal prediction.
method COLEP uses probabilistic circuits to learn and reason about different semantic concepts, providing certifiable coverage guarantees.
result COLEP achieves higher prediction coverage and accuracy than a single model, especially with non-trivial knowledge models.
This paper improves conformal prediction to be robust to perturbations.
problem Ensuring robustness of conformal prediction to natural and adversarial perturbations.
method Probabilistically robust conformal prediction (PRCP) and its adaptive version (aPRCP).
result aPRCP achieves better trade-offs between nominal performance and robustness.
Proposes MVG-CRPS for robust multivariate forecasting.
problem Outliers in multivariate forecasting lead to significant errors.
method Integrates CRPS for MVG distributions, optimizing with MVG-CRPS.
result Improves robustness, accuracy, and uncertainty quantification.
A new neural network model MDRBM improves noise-robustness in classification.
problem Improving noise-robustness in classification problems.
method Stacking a probabilistic-ELM layer on a discriminative restricted Boltzmann machine (DRBM).
result MDRBM outperforms other models, especially in noise-robustness.
Enhanced route planning with probabilistic prediction and uncertainty sets.
problem Improving route planning reliability under uncertainty.
method CQR-GAE model integrating conformal prediction and uncertainty sets.
result Significantly outperforms baseline methods in real-world traffic scenarios.
This paper improves PPCA robustness using t-distributions.
problem Improving robustness of probabilistic PCA.
method Using multivariate t-distributions and a hierarchical model. result Clarified the correct correspondence between the multivariate t-PPCA framework and the hierarchical model. We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variable…
New framework uses conformal predictions for robust, scalable machine learning classification.
problem Developing robust and reliable machine learning models for classification.
method Introducing scalable classifiers linked to statistical order theory and probabilistic learning theory, defining a score function and conformal safety set.
result Demonstrated practical implications in cybersecurity for identifying DNS tunneling attacks.
Adversarial attacks on probabilistic state-space models affect latent state and policy decisions.
problem Robust reinforcement learning under adversarial observability.
method Analyzing adversarial attacks on linear probabilistic state-space models.
result Demonstrating the influence of adversarial observations on latent state and policy decisions.
Study introduces TeMoP model for better stock market predictions.
problem Decreasing prediction errors and robustness across datasets in machine learning models.
method Probabilistic multiple lag order model based on trend encoding.
result TeMoP model outperforms machine learning models in accuracy and stability across different stock indexes.
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mism…
Improved robust latent variable estimation for neural dynamics.
problem Inconsistent results due to noise and nonlinearity in existing models.
method Probabilistic approach to latent variable estimation in decomposed models.
result More accurate latent variable inference in nonlinear systems with diverse noise conditions.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable in order to improve robustness to noise an…
New solver avoids memory issues for long differential equations.
problem Memory constraints in adaptive probabilistic ODE solvers.
method Fixed memory demands adaptive probabilistic solver using robust state estimation.
result Eliminates memory issues for long time series simulations.
We propose a robust estimator to improve maximum likelihood in probabilistic models.
problem Overfitting and sensitivity to noise in maximum likelihood estimation.
method Distributionally robust maximum likelihood estimator that minimizes worst-case expected log-loss.
result The robust estimator is statistically consistent and performs well in regression and classification tasks.
We consider the problem of optimal risk sharing in a pool of cooperative agents. We analyze the asymptotic behavior of the certainty equivalents and risk premia associated with the Pareto optimal risk sharing contract as the pool expands. We first study this problem under expected utility preferences with an objectivel…
PRAE identifies outliers and reconstructs inliers in autoencoders.
problem Accurately identifying anomalies in data.
method Probabilistic Robust AutoEncoder (PRAE) approach.
result PRAE effectively removes outliers and reconstructs inliers.
Capsule models enforce object pose relationships for robustness, explored with probabilistic generative and variational methods.
problem Enforcing object pose relationships for robustness to viewpoint changes.
method Probabilistic generative model with variational bound, exploring capsule assumptions and inference mechanisms.
result Unified objective and test time optimisation demonstrated for capsule models.
Develops a new robust risk measure for neural networks.
problem Adversarial risk metrics fail to capture probabilistic perturbations and poor train-test generalization.
method Statistically robust risk (SRR) framework considering pointwise corruption distributions.
result SRR provides superior generalization performance compared to adversarial risks.
Proposes DGCN with trajectory sampling for data-efficient policy search in MBRL.
problem Improving data efficiency in model-based reinforcement learning.
method Combines trajectory sampling and DGCN for uncertainty propagation in probabilistic world models.
result Improves sample-efficiency over other uncertainty propagation methods and probabilistic models.
New algorithm for robust Boolean matrix factorization handles noise and missing data.
problem Robust probabilistic Boolean matrix factorization in the presence of noise and missing values.
method Probabilistic Expectation Maximization algorithm without latent factor assumptions.
result Outperforms state-of-the-art probabilistic algorithms on real data.
Proposes a probabilistic model to improve hydrology predictions and trust.
problem Noisy or missing basin characteristics impact streamflow prediction.
method Probabilistic inverse modeling framework to reconstruct basin characteristics.
result 6% improvement in R2 for streamflow prediction, 17% reduction in uncertainty. New model detects anomalies without needing clean data.
problem Anomaly detection without clean data.
method Robust denoising diffusion probabilistic models (RDDPM) using robust regression.
result Significantly outperforms existing methods in anomaly segmentation.
Researchers develop a new method to learn from incomplete data.
problem Learning from incomplete data with imprecise probabilities.
method Credal sum-product networks (SPNs) for robust probabilistic representations.
result Imprecise SPNs can capture robustness to missing data.
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for …
A new attack for probabilistic classifiers adapts to noise levels.
problem Adversarial examples can mislead probabilistic classifiers.
method Adapts HopSkipJump attack for probabilistic classifiers, adjusting queries based on noise levels.
result Decision-based attacks are effective against probabilistic classifiers, even with noise.
Paper introduces LPCs for robust classification with performance bounds.
problem Conventional classification techniques constrain rules and use surrogate losses.
method Robust risk minimization (RRM) for unconstrained classification rules, optimizing 0-1 loss.
result LPCs provide performance bounds and competitive performance with state-of-the-art techniques.
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the…
Proposes CCE to assess point-wise reliability of neural network predictions.
problem Overconfidence and misaligned predictive distributions in neural networks.
method Introduces Conditional Congruence (CCE) metric using conditional kernel mean embeddings.
result CCE exhibits correctness, monotonicity, reliability, and robustness in high-dimensional regression tasks.
ProbRes calibrates probabilistic forecasts by learning volatility dynamics.
problem Quantifying risk and uncertainty in time series forecasting.
method ProbRes learns conditional mean and volatility separately, generating well-calibrated prediction intervals.
result ProbRes accurately captures predictive distributions and produces well-calibrated prediction intervals.
Proposes a new framework for balancing average- and worst-case performance in machine learning.
problem Robustness issues in machine learning, especially in safety-critical domains.
method Probabilistic robustness framework that balances average- and worst-case performance.
result Effective algorithm balances average- and worst-case performance with lower computational cost.
Study efficient interactive learning for structured outputs with reliable computation.
problem Interactive learning with noisy labels and structured output spaces.
method Identify and utilize CRISPs (probabilistic models) that guarantee reliable and efficient computation of probabilistic quantities.
result CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.
Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point…
Notwithstanding the popularity of conventional clustering algorithms such as K-means and probabilistic clustering, their clustering results are sensitive to the presence of outliers in the data. Even a few outliers can compromise the ability of these algorithms to identify meaningful hidden structures rendering their o…