Proposes a framework for defining robustness in neural networks.
problem Lack of a precise foundation for robustness concepts in neural networks.
method Develops a rigorous and flexible framework based on mathematical postulates.
result Proposes new learning approaches for optimizing robustness objectives.
Unified framework improves robust causal inference, overcoming Gaussian barriers and optimization issues.
problem Improving robust causal inference in non-Gaussian settings.
method Combines gamma-Divergence, GNC, and Gatekeeper mechanism.
result Enhanced robustness and global optimization in causal effect estimation.
Framework optimizes multiple objectives considering input uncertainty.
problem Efficiently optimizing multiple objectives with input uncertainty.
method Robust Gaussian Process model and two-stage Bayesian optimization process.
result Found a robust Pareto frontier considering input uncertainty.
A framework for robust exploration in reinforcement learning under ambiguity.
problem Optimal stopping under ambiguity in reinforcement learning.
method Continuous-time robust reinforcement learning framework using g-expectation and backward stochastic differential equations. result Constructs a robust exploratory stopping time approximating the optimal stopping time under ambiguity.
A new robust prefix-tuning framework improves model robustness against adversarial attacks.
problem Lack of robustness in prefix-tuning for adversarial attacks.
method Leveraging layerwise activations of pretrained models for additional prefix finetuning during the test phase.
result Framework substantially improves robustness over strong baselines while maintaining comparable accuracy on clean texts.
Paper presents a robust Kalman filter for state estimation.
problem Robust state estimation under process and measurement noise.
method Generalized Bayesian approach to a Weighted Observation Likelihood Filter (WoLF) framework.
result Achieved robust state estimation against both process and measurement noise.
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.
We propose a framework for distributed robust statistical learning on {\em big contaminated data}. The Distributed Robust Learning (DRL) framework can reduce the computational time of traditional robust learning methods by several orders of magnitude. We analyze the robustness property of DRL, showing that DRL not only…
Paper generalizes bipolar theorems for non-negative random variables.
problem Problems with existing bipolar theorems under stronger assumptions.
method Generalizes existing theorems in a robust probabilistic framework.
result Provides necessary and sufficient conditions for bipolar representation.
A framework integrates machine learning with robust control for safer, more reliable systems.
problem Combining machine learning with robust control for systems with stringent safety and reliability requirements.
method Integrates Gaussian Process Regression and state-of-the-art robust controller synthesis within a framework that provides rigorous guarantees.
result Demonstrated improved performance with more data while maintaining rigorous guarantees.
Paper introduces robust market making using Wasserstein distance and entropy regularization.
problem Market making robustness under uncertainty.
method Wasserstein distance, entropy regularization, convex optimization, optimal radius selection.
result The robust market making problem can be reformulated as a convex optimization problem.
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged. In this paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark) which can be…
Risk-averse model uncertainty framework for safe reinforcement learning.
problem Safe decision making in uncertain environments.
method Risk-averse perspective towards model uncertainty using coherent distortion risk measures; equivalent to distributionally robust safe reinforcement learning problems; efficient, model-free implementation.
result Demonstrates robust performance and safety across perturbed test environments.
New framework calibrates decision robustness using inverse conformal risk control.
problem Inadequate robustness levels in decision-making due to ad hoc choices.
method Constructs valid estimators to trace miscoverage-regret Pareto frontier.
result Provides distribution-free, finite-sample guarantees on robustness levels.
Unified framework improves PCA for outliers and distributed data.
problem Outliers and limitations in PCA for large-scale applications.
method φ-PCA framework that retains PCA efficiency and adds robustness.
result HM-PCA achieves optimal robustness and efficiency.
We solve robust optimization problems using Wasserstein balls and apply it to mean-CVaR optimization.
problem Distributionally robust optimization with Wasserstein ambiguity sets.
method Transformed robust optimization into non-robust with penalty term, selecting ambiguity set size.
result Impressive results in robust mean-CVaR optimization compared to other strategies.
A new federated learning framework ensures fairness and robustness.
problem Collaborative fairness and adversarial robustness in federated learning.
method RFFL framework with a reputation mechanism to identify and remove non-contributing or malicious participants.
result RFFL achieves high fairness and robustness to different types of adversaries.
Robust PCA detects anomalies and fills gaps in seasonal time series data.
problem Anomaly detection and data imputation in seasonal time series.
method Online robust PCA framework for temporal observations.
result Empirically compared and showed effectiveness in practical situations.
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.
New framework improves sample efficiency and robustness in RL with smooth policies.
problem Sample inefficiency and lack of robustness in deep reinforcement learning.
method SR^2L framework, smoothness-inducing regularization.
result Improved sample efficiency and robustness in both on-policy and off-policy RL algorithms.
Non-parametric bootstrap improves robust portfolio and trading strategy optimization.
problem Mitigating uncertainty in expected returns and covariances in financial decision-making.
method Non-parametric bootstrap framework for robust optimization without distributional assumptions.
result Improved out-of-sample performance with smoother, more stable results.
DRO-Augment framework enhances deep neural network robustness.
problem Robustness of deep neural networks against various perturbations and adversarial attacks.
method Integrates Wasserstein Distributionally Robust Optimization with data augmentation.
result Significantly improves robustness across various corruptions and adversarial attacks.
Paper introduces novel model selection for CRO to balance robustness and decision risk.
problem Balancing robustness and decision risk in CRO.
method Conformalized Robust Optimization with Model Selection (CROMS) framework.
result Significant improvements in decision efficiency across various applications.
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.
New framework tightens certified robustness gaps in machine learning models.
problem Persistent gap between theoretical certified robustness and empirical accuracy.
method Leverages Lipschitz continuity and novel confidence intervals.
result Improves robust accuracy, compressing the gap between theory and practice.
Framework learns robust control policies from expert demonstrations.
problem Adversarial robustness and closed-loop generalization in feedback control policies.
method Lipschitz-constrained loss minimization for certified robustness and generalization.
result Finite sample bound on policy learning error and robust closed-loop stability.
Unified framework for robust causal directionality in quantum systems under MNAR observation.
problem Determining causal directionality in quantum systems under MNAR observation.
method Integrates CVAE-based latent constraints, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization.
result Achieves lower bias and variance, near-nominal coverage, and superior quantum-specific diagnostics.
The paper evaluates machine learning cyber defenses using log data against adversarial attacks.
problem Evaluating the robustness of machine learning cyber defenses against adversarial attacks.
method Developed a testing framework using deep reinforcement learning and adversarial natural language processing.
result Higher dropout levels increase robustness, with 90% dropout probability showing the highest robustness.
This paper assesses Gaussian and Exponential mechanisms for certifying adversarial robustness.
problem Certifying adversarial robustness using randomized smoothing mechanisms.
method Proposes a generic framework to assess the appropriateness of randomized smoothing mechanisms.
result Gaussian mechanism is an appropriate option for certifying both ℓ2-norm and ℓ∞-norm robustness. Proposes fair and robust methods for estimating treatment effects.
problem Estimating treatment effects while maintaining fairness.
method Simple, nonparametric framework with fairness constraints.
result Estimators are double robust and characterize welfare trade-offs.
Robust optimization is becoming increasingly important in machine learning applications. In this paper, we study a unified framework of robust submodular optimization. We study this problem both from a minimization and maximization perspective (previous work has only focused on variants of robust submodular maximizatio…
New robust loss functions improve matrix completion accuracy.
problem Outliers in data corrupting matrix completion accuracy.
method Developed nonconvex M-estimator functions to down-weight outliers.
result Proposed methods outperform competitors in recovery accuracy and runtime.
Paper tackles robust offline RL with heavy-tailed rewards.
problem Real-world applications often encounter heavy-tailed rewards, challenging offline RL.
method Proposes ROAM and ROOM algorithms using median-of-means method for robust off-policy evaluation and OPO.
result Demonstrates superior performance on heavy-tailed reward datasets compared to existing methods.
Robust scatter estimation is a fundamental task in statistics. The recent discovery on the connection between robust estimation and generative adversarial nets (GANs) by Gao et al. (2018) suggests that it is possible to compute depth-like robust estimators using similar techniques that optimize GANs. In this paper, we …
MIRRAMS framework tackles robust tabular learning under unseen missingness shifts.
problem Challenges in achieving robust predictive performance due to shifts in missingness distribution between training and test inputs.
method Introduces MI robustness conditions and MIRRAMS framework to enforce these conditions without specific missingness assumptions.
result Consistently outperforms existing state-of-the-art baselines and maintains stable performance under diverse missingness conditions.
Unified deep sequential and state-space models for robust option pricing with uncertainty.
problem Combining robustness to noise and uncertainty measurement in option pricing models.
method Unscattered reservoir smoother (URS) integrating deep sequential and state-space models.
result URS achieves competitive forecasting accuracy and uncertainty measurement in noisy datasets.
Develops robust learning framework under distributional perturbations.
problem Learning robust to data distributional changes.
method Distributionally Robust Optimization (DRO) under Wasserstein metric.
result Establishes performance guarantees and tractable formulations.
CSGM framework applied to clinical MRI data for robust reconstructions.
problem Applying deep generative priors to clinical MRI data for high-quality reconstructions.
method Training a generative prior on brain scans from the fastMRI dataset and using Langevin dynamics for posterior sampling.
result Posterior sampling via Langevin dynamics achieves high quality reconstructions in clinical MRI data.
Proposes a new method for nonlinear models with robustness guarantees.
problem Distributional robustness in nonlinear models with causality.
method Representation learning and identifiable representation learning.
result First causality-inspired robustness method with finite-radius guarantees in nonlinear settings.
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.
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms. We specifically focus on incorporating robustness into a state-of-the-art continuous control RL algorithm call…
Framework combines adversarial training and provable robustness for neural networks.
problem Training certifiably robust neural networks with provable robustness guarantees.
method Formulates joint optimization problem with adversarial and provable robustness objectives; develops gradient-descent technique.
result Consistently matches or outperforms prior approaches for provable l infinity robustness on MNIST and CIFAR-10.
Training certifiable neural networks enables one to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to bound the adversary-free region in the neighborhood of the input data by a polyhedral envelope, which yields finer-grained certified robustness. We further …
A new deep hedging framework improves efficiency and robustness.
problem Pricing and hedging of option portfolios with complex models.
method Neural model for training model embeddings using paths of advanced equity option models.
result The proposed method rapidly adapts to new market regimes through recalibration of a low-dimensional embedding vector.
A new framework for verifying robustness of neural networks.
problem Verifying the robustness of neural networks against adversarial attacks.
method LayerCert framework exploiting the nested hyperplane arrangement structure of ReLU networks.
result LayerCert reduces the number and size of convex programs needed for robustness verification.
Proposes DCV-ROOD framework for robust OOD detection evaluation.
problem Ensuring reliable OOD detection methods under diverse conditions.
method Dual Cross-Validation (DCV) adapted for OOD detection evaluation.
result Demonstrates fast convergence to true performance of OOD detection methods.
Boosting framework improves adversarial robustness in deep learning.
problem Adversarial robustness of deep neural networks.
method Multiclass boosting framework with theoretical guarantees.
result Multiclass boosting achieves adversarial robustness faster than state-of-the-art methods.
This paper proposes a framework for certifying neural network defenses against data poisoning attacks.
problem Vulnerability of neural networks to data poisoning attacks.
method Random selection based defenses that average predictions on sub-datasets sampled from the training set.
result The certified radius of bagging derived by the framework is tighter than previous work.