Network science reveals corruption risk in EU procurement markets.
problem Identifying corruption risk in EU procurement markets.
method Analyzing a large dataset of public procurement contracts using network science.
result Corruption risk is clustered and varies by country, not just by market core or periphery.
Robust learning method minimizes risk with corrupted data.
problem Statistical learning with unknown corrupted data fraction.
method Develops a robust learning method with specified corrupted data fraction upper bound.
result Optimal weights provide robustness against corrupted data.
Study improves binary classification with multiple corrupted samples.
problem Binary classification with multiple corrupted training samples.
method Minimizes weighted combination of corruption-corrected empirical risks.
result Optimal weights are functions of sample sizes and corruption degrees.
Develops a comprehensive theory of corruption in supervised learning.
problem Widespread corruption in data collection affects supervised learning problems.
method Introduces a general theory of corruption using Markov kernels, distinguishing and comparing corruption types.
result Establishes a unified framework for corruption types and develops mitigation strategies.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
problem Predicting and mitigating internal fraud losses in retail banking.
method Developed a dynamic model considering internal factors and macroeconomic indicators.
result Internal fraud losses are pro-cyclical and positively affected by corruption perceptions.
Estimates GLMs robustly against label corruptions.
problem Learning GLMs under adversarial label corruptions.
method Iterative trimmed maximum likelihood estimator.
result Achieves minimax near-optimal risk.
In supervised learning one wishes to identify a pattern present in a joint distribution P P P , of instances, label pairs, by providing a function f f f from instances to labels that has low risk E P ℓ ( y , f ( x ) ) \mathbb{E}_{P}\ell(y,f(x)) E P ℓ ( y , f ( x )) . To do so, the learner is given access to n n n iid samples drawn from P P P . In many real world problem…
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.
This paper aims to provide a better understanding of a symmetric loss. First, we emphasize that using a symmetric loss is advantageous in the balanced error rate (BER) minimization and area under the receiver operating characteristic curve (AUC) maximization from corrupted labels. Second, we prove general theoretical p…
New algorithm for robust density estimation in corrupted data.
problem Density estimation in the presence of adversarial corruption.
method Proposes an algorithm for constructing a density estimator within a star-shaped density class, derived minimax bounds for estimation.
result Obtained minimax upper and lower bounds for density estimation under adversarial corruption.
Study robust distribution estimation with Wasserstein distance, achieving optimal risk.
problem Robust distribution estimation under adversarial corruption.
method Combining partial OT and minimum distance estimation, proving structural properties and deriving a novel dual form.
result Achieves minimax-optimal robust estimation risk in many settings.
Paper introduces robust learning methods using coordinate gradient descent.
problem Supervised learning with corrupted features and labels.
method Coordinate gradient descent combined with robust estimators of partial derivatives.
result Robust learning methods with nearly identical numerical complexity to non-robust ones.
Efficiently learns distributions corrupted by both global and local adversarial modifications.
problem Learning distributions with both global and local adversarial corruptions.
method Develops an efficient algorithm to minimize Wasserstein distance with orthogonal projections.
result Achieves optimal risk bounds with error ε k + ρ + i l d e O ( d k n − 1 / ( k ∨ 2 ) ) \sqrt{\varepsilon k} + ρ+ ilde{O}(d\sqrt{k}n^{-1/(k \lor 2)}) ε k + ρ + i l d e O ( d k n − 1/ ( k ∨ 2 ) ) . A method for learning skeleton of Bayesian networks robust to outliers and corruption.
problem Learning the exact skeleton of discrete Bayesian networks from corrupted data.
method Distributionally robust optimization and regression approach, optimizing worst-case risk over distributions within bounded Wasserstein distance or KL divergence.
result Logarithmic sample complexities for successful structure learning of bounded-degree graphs.
ERM and RERM minimize error even with malicious label corruptions.
problem Malicious label corruptions in regression problems.
method Empirical Risk Minimizers (ERM) and Regularized Empirical Risk Minimizers (RERM) under a local Bernstein condition.
result The L 2 L_2 L 2 -error rate is bounded by $r_N + AL |\cO|/N$ under the local Bernstein condition. A new method detects and removes false trailing balances in credit data.
problem False trailing balances in credit data corrupt risk event timing.
method TruEnd-procedure defines and removes false trailing balances.
result Improved accuracy in predicting risk events and reducing credit losses.
Self-adaptive training improves deep learning robustness.
problem Improving deep learning performance on corrupted data.
method Dynamic correction of problematic labels using model predictions.
result Self-adaptive training significantly improves generalization over ERM under various levels of noise.
Symmetric losses improve classifier robustness from corrupted labels.
problem Improving classifier performance from corrupted labels.
method Symmetric losses that satisfy a certain condition.
result Symmetric losses enhance robust classification from corrupted labels.
New algorithm robust to label corruptions in active learning.
problem Active learning under unknown adversarial label corruptions.
method Proposed a new active learning algorithm that is provably correct without assumptions on corruptions.
result Achieves minimax label complexity in non-corrupted setting and only requires additional labels to achieve desired accuracy in corrupted setting.
Study finds corruption negatively impacts firm performance.
problem The impact of corruption on firm performance is examined.
method Cross-sectional data analysis of a large international dataset.
result Corruption negatively affects corporate performance.
Study improves image classifier robustness to random p-norm corruptions.
problem Improving robustness of image classifiers to real-world imperceptible corruptions.
method Training and testing with random p-norm corruptions, evaluating robustness against different p-norms.
result Training with a combination of p-norm corruptions significantly improves robustness.
New algorithm for linear optimization with adaptive corruption.
problem Stochastic linear optimization under adversarial corruption.
method Algorithm uses Löwner-John's ellipsoid for exploration and divides time into epochs.
result Regret increases linearly with corruption amount.
Unified framework for corruption-robust linear bandits with optimal gap-dependent misspecification bounds.
problem Effective learning in linear bandits with corrupted rewards across different corruption models.
method Unified framework for analyzing strong and weak corruption, connection to gap-dependent misspecification, and specialized algorithm.
result Optimal bounds for gap-dependent misspecification in linear bandits.
We report quantitative relations between corruption level and economic factors, such as country wealth and foreign investment per capita, which are characterized by a power law spanning multiple scales of wealth and investments per capita. These relations hold for diverse countries, and also remain stable over differen…
CUTS removes corruption from models without clean data, improving utility and security.
problem Removing corruption from models without access to clean training data.
method CUTS uses a proxy set to amplify corruption and subtract it from model weights.
result CUTS recovers a large fraction of lost utility and nearly eliminates attacks with minimal damage.
Study robust estimation under varying corruption probabilities in data.
problem Robust estimation in scenarios with heterogeneous corruption rates.
method Developed estimators for mean and regression under various corruption patterns.
result Optimal estimators can discard corrupted samples beyond a specific threshold.
New approach improves model robustness and calibration in latent space.
problem Improving model robustness and calibration under input perturbations.
method VarMixup (Variational Mixup) in latent space of VAEs.
result Models trained with VarMixup in latent space are more robust and calibrated.
Binary classification improves with a small fraction of corrupted labels.
problem Binary classification with corrupted labels.
method Established corruption as a form of regularization and computed upper bounds on estimation error.
result Corruption is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size.
New setting combines state evolution and corrupted context for better decision-making.
problem Decision-making in a changing state with unreliable context.
method Proposes a new algorithm using a referee to dynamically combine contextual bandit and multi-armed bandit policies.
result Improved empirical performance compared to existing algorithms.
We study the problem of corrupted sensing, a generalization of compressed sensing in which one aims to recover a signal from a collection of corrupted or unreliable measurements. While an arbitrary signal cannot be recovered in the face of arbitrary corruption, tractable recovery is possible when both signal and corrup…
New algorithm optimizes noisy, potentially corrupted functions.
problem Optimizing unknown functions with noisy bandit feedback, especially when evaluations are corrupted.
method Fast-Slow GP-UCB algorithm, combining robust and non-robust evaluations, enlarged confidence bounds.
result Theoretical analysis upper bounds cumulative regret, showing dependencies on corruption level and kernel.
New algorithm reduces linear contextual bandit regret with adversarial corruption.
problem Linear contextual bandit with adversarial reward corruption.
method Optimism in the face of uncertainty principle, weighted ridge regression.
result Achieves nearly optimal regret for both corrupted and uncorrupted cases.
Robust algorithm optimizes corrupted Gaussian process bandits.
problem Sequential optimization of corrupted, expensive reward functions.
method Robust GP Phased Elimination (RGP-PE) algorithm.
result Algorithm balances robustness to corruptions with exploration and exploitation.
Study robust linear regression with outliers, providing exact asymptotics for ERM performance.
problem Robust linear regression in high-dimension with outliers.
method Analyzes ℓ 2 \ell_2 ℓ 2 , ℓ 1 \ell_1 ℓ 1 , and Huber losses, providing asymptotic performance metrics. result Optimally-regularised ERM is asymptotically consistent with simple calibration, but Huber loss requires norm calibration.
Picket guards against corrupted data in machine learning models.
problem Data corruption biases models and invalidates predictions.
method PicketNet detects corrupted data using self-supervised deep learning; flags corrupted queries online.
result Picket consistently protects models from corrupted data during training and deployment.
New method for contextual bandits with corrupted context.
problem Contextual bandits with corrupted context in online settings.
method Combining contextual bandit and multi-armed bandit approaches.
result Improved learning from all iterations, including corrupted ones.
Study connects compressed signal to AWGN model for risk estimation.
problem Estimating high-dimensional signals under compression constraints.
method Utilizes Gaussian approximation and Wasserstein distance to relate compressed and noisy signals.
result Establishes a connection between estimator risks under different conditions.
Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels. It is vitally important to reiterate robustness and generalization in DNNs against label corruption. To this end, this paper studies the 0-1 loss, which has a monotonic relationship with an empirical adversary (r…
Algorithm maximizes total reward in multi-agent bandits with adversarial corruptions.
problem Maximizing total reward in multi-agent bandits with adversarial corruptions.
method Proposes a cooperative learning algorithm robust to adversarial corruptions.
result Demonstrates an additive O ( ( L / L min ) C ) O((L / L_{\min}) C) O (( L / L m i n ) C ) regret term for an adversary with unknown corruption budget. The main purpose of this paper is to formalize the modelling process, analysis and mathematical definition of corruption when entering into a contract between principal agent and producers. The formulation of the problem and the definition of concepts for the general case are considered. For definiteness, all calculati…
New algorithm robustly trains deep neural networks under corrupted supervision.
problem Training deep neural networks with corrupted supervision data.
method Unified framework for classification and regression, focusing on collective impact of data points on average gradient.
result Achieves strong guarantees without assuming the type of corruption, robust under various types of corruption.
Paper develops robust SGLD for solving non-convex DRO problems.
problem Solving non-convex distributionally robust optimisation problems with adversarially corrupted samples.
method Developed a Stochastic Gradient Langevin Dynamics (SGLD) algorithm with non-asymptotic convergence bounds.
result The robust SGLD estimator outperforms vanilla SGLD in terms of test accuracy.
We inspect a possible clustering structure of the corruption perception among 134 countries. Using the average linkage clustering, we uncover a well-defined hierarchy in the relationships among countries. Four main clusters are identified and they suggest that countries worldwide can be quite well separated according t…
This study tackles adversarial corruption in model-based reinforcement learning.
problem Adversarial corruption in model-based reinforcement learning.
method Maximum likelihood estimation (MLE) approach for learning transition model in both online and offline settings.
result Proves a regret of i l d e O ( T + C ) ilde{\mathcal{O}}(\sqrt{T} + C) i l d e O ( T + C ) for CR-OMLE and a suboptimality of O ( C / n ) \mathcal{O}(C/n) O ( C / n ) for CR-PMLE. Lower bounds on Bayes risk for realizable models derived using information theory.
problem Deriving lower bounds on Bayes risk for realizable machine learning models.
method Information-theoretic analysis using rate-distortion theory and mutual information.
result Lower bounds on Bayes risk for realizable models, matching known bounds up to logarithmic factors.
Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem within the formalism of Corrupt Reward Markov Decision Processes (CRMDPs). We show that if the reward corruption in a CRMDP is sufficiently "…
Improved SGD for robust linear and ReLU regression with adversarial corruptions.
problem Robust regression with adversarial corruptions in streaming data.
method Stochastic gradient descent (SGD-exp) with exponentially decaying step size.
result Nearly linear convergence to true parameter with up to 50% Massart corruption rate.
Monotone adversarial corruptions degrade optimal learning algorithms.
problem Optimal learning algorithms' reliance on exchangeability and independence is challenged.
method Introduces a monotone adversarial corruption model where an adversary adds monotone corruptions to a clean dataset.
result Optimal learning algorithms achieve suboptimal expected error on new test points.