Robustified controllers reduce fine-tuning time for sim-to-real transfer learning.
problem Reducing fine-tuning time for sim-to-real transfer learning of complex robotic tasks.
method Learn robustified controllers in simulation by changing parameters for successive episodes.
result Fine-tuning time is substantially reduced for robustified controllers.
Paper robustifies reinforcement learning agents against action space perturbations.
problem Vulnerability of reinforcement learning agents to action space perturbations (e.g. actuator attacks).
method Adversarial training to robustify DRL agents against perturbations.
result DRL agents can be robustified against action space perturbations through adversarial training.
This paper quantifies privacy-robustness and generalization-robustness trade-offs in adversarial training.
problem Privacy and generalization issues in adversarial training.
method Defines robustified intensity and empirical robustified intensity to measure robustness, proving differential privacy and generalization bounds.
result Proves adversarial training is (ε,δ)-differentially private and provides generalization bounds. Robustifies Markowitz portfolios to reduce transaction costs and improve performance.
problem Markowitz portfolios are unreliable due to estimation errors and extreme weights.
method Projected gradient descent and robust statistics for stable weights and costs.
result Robustified Markowitz portfolios have lower turnover and maintain or improve performance.
DMPC combines MPC and value function estimation for efficient control tasks.
problem Efficiently solve control tasks with sparse and binary reward signals.
method Actor-critic algorithm combining MPC and value function estimation.
result DMPC actor minimizes an upper bound of cross-entropy to optimal policy.
Robustifies tree learning algorithms for corrupted data.
problem Learning latent tree structures with corrupted vector observations.
method Presented robustified algorithms using truncated inner product.
result Optimalities of robust CLRG and NJ verified by sample complexities and impossibility results.
Method scales Bayesian inference to large datasets and robustifies against outliers.
problem Scalability and robustness to outliers in Bayesian inference.
method Variational inference with β-divergence and Riemannian coresets. result Efficiently constructs cleansed data summaries robust to outliers.
Develops robust knockoffs for controlling false discoveries in financial data.
problem Challenges in variable selection with highly correlated data in finance and economics.
method Robustified knockoff framework addressing high dependence and time correlation.
result Identifies new important groups of factors on top of known drivers.
New algorithm guarantees performance on noisy data.
problem Learning with noisy data and heavy-tailed distributions.
method Anytime online-to-batch conversion for smooth objectives.
result Stochastic gradient-based algorithm with sub-Gaussian error bounds.
Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers - that is, data markedl…
Paper robustifies reinforcement learning with risk-averse methods.
problem Making predictions robust to changes in system dynamics or rewards.
method Approximates Robust Reinforcement Learning using Φ-divergence and Risk-Averse formulation. result Classical Reinforcement Learning can be robustified using standard deviation penalization.
Improved robustness in multivariate regression and classification with DRO under Wasserstein metric.
problem Outliers in covariates and responses.
method Distributionally Robust Optimization (DRO) with Wasserstein metric ambiguity set and regularization.
result Significant improvement in predictive error and robustness.
Novel methods robustify Gromov-Wasserstein distance for cross-domain alignment.
problem Robustifying Gromov-Wasserstein distance for cross-domain alignment.
method Three novel techniques derived from robust statistics to improve GW and its variants.
result Empirical validation shows superior resilience to contamination.
Develops a model for personalized diabetes and hypertension treatment using robust regression and K-NN.
problem Optimal personalized treatment rules for patients based on EHRs.
method Robust regression informed K-NN approach for predicting and selecting optimal treatments.
result Algorithm leads to larger reduction in HbA1c for diabetics and systolic BP for hypertensive patients compared to alternatives.
Paper supports robust estimation in regression with heavy-tailed errors.
problem Support estimation in high-dimensional heteroscedastic mean regression.
method Use of Huber loss function and adaptive LASSO penalty for robust estimation.
result Sign-consistency and optimal rates of convergence in ℓ∞ norm. DARTS fails to generalize well; adding regularization improves robustness.
problem DARTS fails to find architectures that generalize well across different tasks.
method Identified failure modes, added regularization, proposed variations.
result Regularization robustifies DARTS to find better generalizing architectures.
Robustifies elicitable functionals to handle small distribution misspecifications.
problem Determining uniquely optimal forecasts under distributional misspecification.
method Integrates statistical robustness into elicitable functionals using Kullback-Leibler divergence.
result Robust elicitable functionals admit unique solutions at the boundary of uncertainty regions.
This work robustifies Wasserstein distance estimation with MoM estimators for outlier-polluted data.
problem Estimating Wasserstein distance between two distributions with outliers.
method Introducing MoM-based robust estimators for Wasserstein distance.
result Consistent MoM-based estimators for Wasserstein distance with convergence rates.
New framework robustifies loss functions with quantiles for outlier resistance.
problem Widespread outliers in big data affect statistical estimation and inference.
method Introduces a framework connecting to trimming, scalable algorithms, and new techniques.
result Robust estimators achieve minimax rate optimality in regression, classification, and neural networks.
Study dynamic risk measures with distributional uncertainty using optimal transport.
problem Risk robustification under distributional uncertainty in Markovian models.
method Characterize risk measures via convex monotone semigroups and optimal transport costs.
result Identify generator and correction terms for dynamic risk measures under different scaling regimes.
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
problem Optimizing portfolio allocation with a penalty for deviation from a reference portfolio.
method Formulated as a McKean-Vlasov control problem, provides explicit solutions and asymptotic expansions.
result The penalized portfolio strategy outperforms standard mean-variance and reference portfolios in most cases.
Bayesian framework robustifies classifiers against malicious attacks.
problem Adversarial attacks on classification algorithms in security and business.
method Adversarial risk analysis and approximate Bayesian computation.
result Robustifies statistical classification algorithms against attacks.
New method calibrates crypto option prices more robustly.
problem Large bid-ask spreads and missing quotes in crypto markets.
method Designs a novel calibration procedure for crypto options.
result Calibration is more robust and accurate than standard methods.
Byrd-SAGA reduces variance to robustify SGD against Byzantine attacks.
problem Learning over networks with malicious Byzantine attacks.
method Byrd-SAGA uses geometric median for robust aggregation of corrected stochastic gradients.
result Byrd-SAGA achieves provably linear convergence to optimal solution in the presence of Byzantine workers.
Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In this context, the fresh look advocated here permeates benefits from variable sele…
In this paper, we establish a robustification of an on-line algorithm for modelling asset prices within a hidden Markov model (HMM). In this HMM framework, parameters of the model are guided by a Markov chain in discrete time, parameters of the asset returns are therefore able to switch between different regimes. The p…
We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound). A simple idea of trimming the inner product is applied to the elastic net model. Specifically, we robustify the covariance matrix by trimm…
Enhances DNNs by selectively learning key image edges.
problem Improving DNN robustness against adversarial attacks.
method Introduces Secure Selective Convolution (SSC) to learn important image edges.
result Significant reduction in attack success rate and imperceptibility of adversarial images.
Adversarial training linked to operator norm regularization, proving network sensitivity to attacks.
problem Robustifying neural networks against adversarial attacks.
method Theoretical link established between adversarial training and operator norm regularization.
result Adversarial training is equivalent to data-dependent operator norm regularization.
Paper shows how to use geometric median for robust SGD in high dimensions.
problem Robustifying SGD for high-dimensional optimization problems with gross corruption.
method Applying geometric median to only chosen blocks of coordinates at a time.
result Retains optimal breakdown point of 0.5 for smooth non-convex problems.
The paper proposes a robust method for estimating super-level sets using Gaussian processes.
problem Determining a large region where a function exceeds a threshold with high probability.
method Maximizing the expected volume of the domain identified as above the threshold as predicted by a Gaussian process, robustified by a variance term.
result The proposed method outperforms existing techniques in practice and provides asymptotic guarantees.
High sensitivity of neural networks against malicious perturbations on inputs causes security concerns. To take a steady step towards robust classifiers, we aim to create neural network models provably defended from perturbations. Prior certification work requires strong assumptions on network structures and massive co…
Deep Relevance Regularization improves neural network performance in tumor typing.
problem Confounding factors hinder neural network performance in multi-laboratory imaging mass spectrometry data.
method Introduces Deep Relevance Regularization to restrict neural network focus.
result Deep Relevance Regularization robustifies neural networks and improves interpretability.
Paper introduces robust Gaussian process regression without sacrificing computational efficiency.
problem Violation of independent and identically distributed Gaussian observation noise assumption in Gaussian process regression.
method Proves robust and conjugate Gaussian process regression (RCGP) at no additional cost using generalised Bayesian inference.
result RCGP enables exact conjugate closed form updates in all settings where standard GPs admit them.
MadNet uses MAD optimization to enhance deep model robustness against adversarial attacks.
problem Defending deep models against adversarial attacks.
method Inspired by certificate defense, MAD optimization increases separability of class clusters and decreases sensitivity to small distortions.
result MadNet improves adversarial robustness compared to state-of-the-art methods.
Variational inference is a powerful approach for approximate posterior inference. However, it is sensitive to initialization and can be subject to poor local optima. In this paper, we develop proximity variational inference (PVI). PVI is a new method for optimizing the variational objective that constrains subsequent i…
We consider the problem of robustifying high-dimensional structured estimation. Robust techniques are key in real-world applications which often involve outliers and data corruption. We focus on trimmed versions of structurally regularized M-estimators in the high-dimensional setting, including the popular Least Trimme…
It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorith…
We develop a new statistical test for comparing variables with varying scales.
problem Comparing variables with different scales in multidimensional spaces.
method Order based on expectations of random variables, generalized stochastic dominance (GSD) order, regularized statistical test, linear optimization, imprecise probability models.
result Validated through multidimensional data from various fields.
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
problem Risk mitigation and gain-seeking in hedging path-dependent financial derivatives.
method Robust risk-aware reinforcement learning (RL) with policy gradient approach.
result Robust hedging strategies outperform non-robust ones under varying data generating processes.
Enhances GCNs' robustness to graph perturbations.
problem Vulnerability of GCNs to graph structure perturbations.
method Generates edge-dithered auxiliary graphs and uses them in an adaptive GCN.
result Significantly improved performance in SSL tasks with noisy inputs and adversarial attacks.
Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for multilinear data analysis against outliers, gross corruptions and missing values and has a diverse array of applications. In this paper, we study the problem of robust low-rank tensor recovery in a convex optimization framework,…
A Bayesian framework models adversarial uncertainty for robust machine learning.
problem Vulnerability of machine learning models to adversarial attacks.
method Formal Bayesian framework that models adversarial uncertainty through a stochastic channel, articulating probabilistic assumptions.
result Explicitly modeling adversarial uncertainty leads to improved robustification strategies.
Integrates side information for robust portfolio optimization.
problem Portfolio optimization under uncertainty and side information.
method Distributionally robust optimization with optimal transport ambiguity set.
result The problem can be reformulated as a finite-dimensional optimization problem.
SIGUA prevents overfitting in noisy data by forgetting unwanted memorization.
problem Overfitting in deep learning models with noisy labels.
method SIGUA combines gradient descent on good data and learning-rate-reduced gradient ascent on bad data.
result SIGUA improves the performance of two base learning methods significantly.
Generative Ensembles improve anomaly detection by reducing OoD errors.
problem Out-of-Distribution (OoD) errors in likelihood models for anomaly detection.
method Generative Ensembles estimate epistemic uncertainty to robustify density-based OoD detection.
result WAIC performs surprisingly well in practice despite not accounting for the typical set of a distribution.
A risk-aware RL approach using RDEU and Wasserstein ball for robust performance.
problem Optimizing risk-aware performance criteria in uncertain environments.
method Rank dependent expected utility (RDEU) for risk assessment, Wasserstein ball for robustness, actor/agent framework.
result Explicit policy gradient formulae for robust optimization.
Recently, researchers have discovered that the state-of-the-art object classifiers can be fooled easily by small perturbations in the input unnoticeable to human eyes. It is also known that an attacker can generate strong adversarial examples if she knows the classifier parameters. Conversely, a defender can robustify …