CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
DRCS selects a subset of data to minimize worst-case test error under covariate shift.
problem Selecting a subset of data that performs well across different deployment scenarios when data distributions differ.
method DRCS derives an upper bound for the worst-case test error assuming covariate shift and selects instances to minimize this bound.
result DRCS achieves distributionally robust training instance selection.
New scalable methods for robust model learning from large datasets.
problem Training robust models resistant to data distribution shifts.
method Composite optimization for distributionally robust optimization (DRO).
result Scalable methods for learning robust models from large datasets.
Paper develops a robust Bayesian optimization method for noisy zeroth-order settings.
problem Achieving robustness to distributional shift in machine learning.
method Distributionally robust Bayesian optimization (DRBO) algorithm for noisy zeroth-order optimization.
result DRBO algorithm provably obtains sub-linear robust regret in various settings.
Safe-DRFS selects features robust to covariate shifts for reliable performance.
problem Feature selection fails in diverse deployment environments.
method Safe-DRFS extends safe screening to distributionally robust settings under covariate shift.
result Safe-DRFS identifies a feature subset encompassing optimal subsets across distribution shifts.
DRSSS method reduces model training costs by eliminating unsafe samples.
problem Reducing storage and training costs for customized models across different environments.
method Combines DR optimization and SSS for covariate shift.
result Models trained on reduced dataset perform similarly to those on full dataset.
Proposes robust ITRs integrating multiple datasets to handle posterior shift.
problem Posterior shift in conditional outcome distributions between source and target populations.
method Distributionally robust approach with closed-form solution and adaptive uncertainty tuning.
result Achieves superior performance compared to existing methods in simulations and real-data applications.
Paper proves robust estimators' generalization guarantees without dimensionality issues.
problem Generalization guarantees for Wasserstein distributionally robust models.
method Analyzes and extends existing guarantees to broader classes of models and regularized versions.
result Generalization guarantees hold without dimensionality issues and cover distribution shifts.
DRSS method identifies unnecessary samples and features in DR covariate shift.
problem Identifying unnecessary samples and features in DR covariate shift.
method Combines DR learning and safe screening techniques.
result DRSS method provides reliable identification of unnecessary samples and features under specified distribution uncertainty.
Combines adversarial and interventional robustness for machine learning models.
problem Designing robust models for distribution shifts in machine learning.
method RISe formulation using distributionally robust optimization.
result Demonstrates efficacy of RISe approach with synthetic and real-world datasets.
Proposes a fair machine learning framework robust to distribution shifts without causal graph knowledge.
problem Fairness issues in machine learning models under distribution shifts.
method Stochastic distributionally robust optimization with Exponential Renyi Mutual Information (ERMI) fairness measure.
result First stochastic framework for fair learning robust to distribution shifts without causal graph knowledge.
New method reduces over-pessimism in Bayesian control under parameter uncertainty.
problem Over-pessimism in Bayesian control due to misspecified priors.
method Distributionally robust Bayesian control (DRBC) with strong duality and optimization.
result Validated algorithm on synthetic and real data, reducing over-pessimism.
Proposes MRO to achieve uniformly low regret in distributionally robust learning.
problem Learning under unknown test distributions (distribution shift).
method Minimax Regret Optimization (MRO) for robust machine learning.
result MRO achieves uniformly low regret across all test distributions.
A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts, or unmodeled temporal effects. We develop and analyze a distributionally robust stochastic optimization (DRO) framework that lea…
This work bridges offline RL and DRL to address distributional shift.
problem Distributional shift in offline RL due to difference in state-action visitation distributions.
method Proposes offline RL algorithms using DRL framework, characterizes sample complexity under single policy concentrability.
result Demonstrates superior performance of proposed algorithms through simulations.
New algorithms learn robust policies from shifted distributions.
problem Learning robust policies in environments with distributional shifts.
method Two novel model-free algorithms: distributionally robust Q-learning and variance-reduced distributionally robust Q-learning.
result Achieves minimax sample complexity upper bound of ildeO(∣S∣∣A∣(1−γ)−4ε−2). K-means clustering improved for robustness to outliers and distribution shifts.
problem K-means is brittle to outliers, distribution shifts, and limited samples.
method Developed a distributionally robust variant using Wasserstein-2 ball around the empirical distribution.
result Substantial gains in outlier detection and robustness to noise demonstrated.
Paper proposes a shape-constrained approach to distributionally robust learning.
problem Challenges in statistical learning under distribution shift.
method Shape-constrained approach to distributionally robust learning (DRL). Assumes isotonic density ratio.
result Improved accuracy demonstrated in empirical studies.
DORO improves DRO's performance and stability in tasks with subpopulation shift.
problem DRO's poor performance and instability in tasks with subpopulation shift.
method DORO, a refined risk function that prevents overfitting to outliers.
result DORO improves DRO's performance and stability on large modern datasets.
Recent work shows GRW approaches do not improve over ERM in distributional shift.
problem Improving robustness to distributional shift in machine learning models.
method Generalized Reweighting (GRW) algorithms, which iteratively update model parameters based on reweighting of training samples.
result GRW approaches do not significantly improve over ERM in real applications with distribution shift.
New framework for robust reinforcement learning policies in uncertain environments.
problem Robust reinforcement learning policies in environments with distributional shifts.
method Comprehensive modeling framework centered around robust Markov decision processes (RMDPs).
result Existence and conditions for the dynamic programming principle (DPP) in RMDPs.
This work tackles robust Bayesian optimization under data shift using φ-divergences.
problem Bayesian optimization under uncertainty and data shift.
method Distributionally robust optimization with φ-divergences.
result A computationally tractable algorithm with provable sublinear regret bounds.
A new algorithm reduces bias and variance in distributionally robust optimization.
problem Distributionally robust optimization with bias and variance issues.
method Prospect, a stochastic gradient-based algorithm that reduces hyperparameter tuning.
result Prospect achieves linear convergence and 2-3x faster convergence on various benchmarks.
This work addresses local fairness in machine learning models.
problem Ensuring fairness within subregions of feature space, not just global averages.
method Introduces ROAD, a Distributionally Robust Optimization (DRO) approach with adversarial learning.
result Achieves Pareto dominance in local fairness and accuracy across datasets.
Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance can be significantly degraded because test data may follow a different distribution from training data. DRSL with f-divergences explicitly …
Study shows convergence of stochastic gradient method for unregularized Wasserstein optimization.
problem Wasserstein distributionally robust optimization under potential distribution shifts.
method Regularized approximation with stochastic gradient methods, convergence analysis.
result Stochastic gradient method converges to subgradients of unregularized objective as regularization vanishes.
This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.
problem Tackles cost-sensitive distributionally robust log-optimal portfolio problem with ambiguous return distributions.
method Uses Wasserstein metric for distributional ambiguity, incorporates convex transaction costs, and approximates infinite-dimensional problem with finite convex program.
result Establishes conditions for robustly survivable trades and validates theoretical framework with empirical studies.
New algorithms tackle robust RL with linear models, revealing unique challenges.
problem Distributionally robust offline RL with uncertainty in dynamics.
method Proposes minimax optimal and computationally efficient algorithms using novel function approximation mechanisms.
result Function approximation in robust offline RL is distinct and harder than in standard offline RL.
A method for robust reinforcement learning in large state spaces.
problem Challenges in RL with large state spaces, costly data, and real-world dynamics deviation.
method Distributionally robust Markov decision processes with Gaussian Processes and maximum variance reduction.
result Efficient learning of multi-output nominal transition dynamics with statistical sample complexity bounds.
This paper analyzes statistical properties of the Robust Satisficing model.
problem Lack of statistical theory for the Robust Satisficing model.
method Comprehensive analysis of statistical properties, including confidence intervals and generalization error bounds.
result Established two-sided confidence intervals and finite-sample generalization error bounds for the RS optimizer.
Exact generalization guarantees for robust models using Wasserstein distance are established.
problem Capturing data uncertainty and distribution shifts in machine learning models.
method Establishes exact generalization guarantees for robust models based on the Wasserstein distance, covering various cases and transport costs.
result Exact generalization guarantees are provided for a wide range of cases, including deep learning objectives with nonsmooth activations.
Adaptive optimal transport priors improve few-shot learning robustness.
problem Limited supervision and distribution shifts in few-shot learning.
method Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework.
result PG-DRO achieves stronger robust generalization in few-shot scenarios.
RACER optimizes LLM-as-judge accuracy with dynamic reasoning selection.
problem Balancing reasoning accuracy with computational cost in LLM-as-judge settings.
method Formulates routing as a constrained distributionally robust optimization problem, accounting for distribution shift via KL-divergence uncertainty set.
result RACER achieves superior accuracy-cost trade-offs under distribution shift.
Improves robust transfer learning with side information.
problem Addressing environmental shift in MDPs with side information.
method Estimate-centered uncertainty sets with side information integration.
result Improved robust policy with reduced sub-optimality gap.
This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. Tapping into a rich existing literature, we show that DRO is a principled tool for counterfactual decision making. We also show that well-established solutions to the CRM probl…
CreDRO learns credal ensembles via distributionally robust optimization, improving EU quantification.
problem Quantifying predictive epistemic uncertainty in credal models.
method Distributionally robust optimization to capture EU from training randomness and potential distribution shifts.
result Empirically, CreDRO outperforms existing credal methods on various tasks.
Paper introduces DRM for selecting robust CATE estimators.
problem Selecting CATE estimators without counterfactual outcomes.
method Distributionally Robust Metric (DRM) for CATE estimator selection.
result DRM selects robust CATE estimators robust to distribution shift.
Framework uses human annotations to make models robust to spurious correlations.
problem Machine learning models fail when unmeasured variables change test distributions.
method Human annotations to augment training examples, UV-DRO objective for robustness.
result Improvements of 5-10% on digit recognition task and 1.5-5% on NYPD Police Stops analysis.
Paper tackles robust policy learning with incomplete data.
problem Learning policies from past data assumes future environment is the same, which is often false.
method Develops a distributionally robust policy evaluation and learning algorithm.
result Proposed algorithm provides robustness to adversarial perturbations and covariate shifts.
The paper tackles gradual domain adaptation with manifold-constrained DRO, showing error bounds across distributions.
problem Gradual domain adaptation challenge with manifold-constrained data distributions.
method Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius.
result Theoretical bounds on classification error across distributions, demonstrating error propagation dynamics.
A new method improves adversarial robustness by optimizing importance weights.
problem Adversarial training's non-uniform robustness across different data points.
method Doubly-robust instance reweighted adversarial training using distributionally robust optimization.
result Improves robustness against attacks on the weakest data points.
LIME is a popular approach for explaining a black-box prediction through an interpretable model that is trained on instances in the vicinity of the predicted instance. To generate these instances, LIME randomly selects a subset of the non-zero features of the predicted instance. After that, the perturbed instances are …
New method calibrates ambiguity sets for robust decision-making under contamination.
problem Minimizing worst-case expected loss over distributional shifts in out-of-sample environments.
method Bulk-calibrated credal ambiguity sets that learn a high-mass bulk set from data and bound tail contributions.
result Closed-form, finite robust objective and tractable optimization for various losses and geometries.
Improved reinforcement learning for environments with distributional shifts.
problem Learning optimal policies in environments with distributional shifts.
method Distributionally robust Q-learning with multi-level Monte Carlo estimator.
result Proved upper bound on sample complexity for robust RL.
Proposes a risk parity portfolio optimization method that accounts for uncertainty in asset returns.
problem Risk parity portfolio optimization under uncertainty.
method Distributionally robust optimization with ambiguity set for worst-case scenario analysis.
result Distributionally robust risk parity portfolios can yield higher risk-adjusted returns.
This paper tackles robust policy learning under concept drifts, improving upon existing methods.
problem Tackles robust policy learning under concept drifts, improving upon existing methods.
method Develops a doubly-robust estimator and a learning algorithm to maximize policy value within a given policy class.
result The proposed algorithm achieves sub-optimality gap of the order κ(Π)n−1/2, demonstrating substantial improvement over existing benchmarks. In this paper, we investigate the multi-variate sequence classification problem from a multi-instance learning perspective. Real-world sequential data commonly show discriminative patterns only at specific time periods. For instance, we can identify a cropland during its growing season, but it looks similar to a barren…
Develops a minimax optimal estimator for system stability under distribution shift.
problem Ensuring system reliability under changes in the underlying environment.
method Minimax optimal estimation of stability defined in terms of acceptable performance degradation.
result Characterizes the minimax convergence rate and demonstrates practical utility.