Improves off-policy evaluation weights for balanced state-action pair distribution.
problem Imbalance in importance sampling weights for off-policy evaluation of contextual bandits.
method Balanced Off-Policy Evaluation (B-OPE) method that minimizes imbalance to desired counterfactual distribution of state-action pairs.
result Experimental evidence shows B-OPE improves offline policy evaluation in both discrete and continuous action spaces.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
problem Removing noise from deep neural network weights for better approximation.
method Model weights as signal + noise, use random matrix theory to estimate thresholds, evaluate using cosine similarity.
result Proposed threshold estimation method improves approximation quality.
Study shows refugee matching gains are robust to different evaluation methods.
problem Stability of refugee matching gains under various evaluation methods.
method Used multiple off-policy evaluation methods including IPW and AIPW.
result Impact estimates remain consistent in magnitude and statistically significant.
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
problem Improving stock selection accuracy and performance.
method Combined machine learning algorithms with static and dynamic weighting methods.
result IC-based dynamic weighting outperforms static evaluation metrics in backtested returns and predictive performance.
MLDS dataset reveals hidden model behavior via weight-space analysis.
problem Neural networks' opacity makes them hard to evaluate.
method Presented MLDS dataset of trained neural networks.
result Weight-space analysis reveals meaningful divergence with small changes in training data.
New criterion improves predictive evaluation in weighted inference scenarios.
problem Improving predictive evaluation in scenarios with different likelihoods for estimation and evaluation.
method Developed the posterior covariance information criterion (PCIC) to handle weighted likelihood inference.
result PCIC is asymptotically unbiased for quasi-Bayesian generalization error in weighted inference.
New method stabilizes FQE by reweighting Bellman targets.
problem Stability guarantees for FQE often rely on Bellman completeness, which can fail with function approximation.
method Proposes stationary-weighted FQE, reweighting Bellman targets by stationary target-to-behavior density ratio.
result Proves finite-sample linear convergence to stationary projected Bellman fixed point without Bellman completeness.
This paper identifies and discusses bias in offline recommendation algorithm evaluations.
problem Bias in offline evaluation of recommendation algorithms.
method Describes and discusses the relevance of weighted offline evaluation.
result The need for weighted offline evaluation to reduce bias.
New method evaluates policies with latent confounders using optimal balance.
problem Evaluating policies with unobserved confounders in costly exploration scenarios.
method Importance weighting method to avoid latent outcome regression, minimizing adversarial balance objective.
result Provable consistency in policy evaluation with latent confounders, demonstrated empirically.
Boosts neural network performance by improving weight separability.
problem Improving the separability of weight vectors in neural networks.
method Proposes a new evaluation metric and feed-backward reconstruction loss to encourage weight separability.
result Improves visual recognition performance across various tasks.
The paper improves DR estimators for off-policy evaluation in contextual bandits.
problem Challenges in evaluating policies with adaptively collected data.
method Adaptive weighting to control variance in DR estimators.
result Improved t-statistic based on the estimator is asymptotically normal.
New approach to policy evaluation and learning from observational data.
problem Evaluating and learning personalized decision policies from past data.
method Balance-based approach optimizing weights for balance between weighted data and target policy.
result Markedly outperforms existing methods in evaluation and learning.
A new weighted MCC measure improves classifier performance evaluation.
problem Lack of measures sensitive to observation weights in multiclass classification.
method Proposes weighted versions of Pearson-Matthews Correlation Coefficient (MCC) for binary and multiclass classification.
result Weighted MCC values are higher for classifiers that perform better on highly weighted observations.
KOOW method provides optimal covariate balance for continuous treatments.
problem Estimating effects of continuous treatments with robustness to model misspecification and extreme weights.
method Kernel Optimal Orthogonality Weighting (KOOW) using convex optimization.
result KOOW provides optimal covariate balance and controls for extreme weights.
New method for off-policy evaluation in contextual bandits reduces bias-variance tradeoff.
problem Improving off-policy evaluation in contextual bandits.
method Asymptotically optimal doubly robust estimator with shrinkage of importance weights.
result New estimators outperform state-of-the-art methods in experiments.
New methods for off-policy evaluation in reinforcement learning.
problem Improving off-policy evaluation in reinforcement learning.
method Developed MWL and MQL estimators for importance weights and value functions.
result MWL and MQL provide a unified view of algorithms in reinforcement learning.
Enhanced visual feature attribution via adaptive baseline weighting.
problem IG's sensitivity to baseline images leads to noisy or unstable explanations.
method Weighted Integrated Gradients (WG) evaluates and weights baselines for improved reliability.
result WG improves over Expected Gradients (EG) by up to 36% across various models.
Enhances mixture models with classifier-defined weights.
problem Density evaluation and sampling in mixture models.
method Introduces Classifier Weighted Mixtures (CWM) with functional weights.
result Improves expressivity in variational estimation without increasing complexity.
Unified framework evaluates different nearest neighbor classification methods.
problem Evaluating and comparing classical, fuzzy, and fuzzy rough nearest neighbor classification methods.
method Standardized nearest neighbor weighting with kernel functions applied to distance and/or rank values of nearest neighbors.
result NN, FNN, and FRNN perform best with Boscovich distance, and NN and FRNN perform best with specific combinations of weights and scaling measures.
A new method for evaluating and selecting policies in contextual bandits improves confidence intervals and policy quality.
problem Evaluating and selecting policies in contextual bandits with logged data.
method Self-normalized Importance Weighting (SN) estimator with Efron-Stein tail inequality and multiplicative bias control.
result The method provides tighter confidence intervals and better policy selection compared to competitors.
Proposes MOPED method for choosing priors in Bayesian DNNs.
problem Challenges in specifying meaningful priors for deep neural networks.
method Two-stage hierarchical modeling with empirical Bayes.
result MOPED enables scalable variational inference and reliable uncertainty quantification.
This paper improves auto-augment efficiency by sharing augmentation weights.
problem Efficient evaluation of augmentation policies for model training.
method Augmentation-Wise Weight Sharing (AWS) to create a fast yet accurate proxy task.
result Augmentation policies found achieve superior accuracies compared to existing methods.
CAT framework improves AI medical screening fairness and reliability.
problem Imbalanced data, varying performance across cohorts, and patient-level inconsistencies in traditional metrics.
method CAT framework introduces patient-level assessment, entropy-based distribution weighting, and cohort-weighted sensitivity and specificity.
result Enhanced predictive reliability, fairness, and interpretability of AI-driven medical screening models.
Automates debiasing for large language model evaluations through Fisher random walk.
problem Rigorous and scalable evaluation of large language models.
method Semiparametric efficient estimator using Fisher random walk for weighted residual balancing.
result Efficient estimation of contextual preference scores for large language models.
Unified minimax value interval for off-policy evaluation and optimization.
problem Overcoming the exponential variance in off-policy evaluation and policy optimization.
method Unified minimax value interval using marginalized importance weights.
result Unified value interval with double robustness, valid when either value-function or importance-weight class is well specified.
This paper evaluates heuristics and hyperparameters in weight-sharing NAS methods.
problem Improving the performance of weight-sharing NAS methods.
method Systematic evaluation of heuristics and hyperparameters in weight-sharing NAS algorithms.
result Some heuristics negatively impact super-net and stand-alone performance correlation.
Efficient policy learning from observational data using weighted classification reductions.
problem Efficient policy evaluation does not necessarily lead to efficient estimation of policy parameters.
method Proposed an estimation approach based on generalized method of moments, efficient for policy parameters.
result Demonstrated empirical efficiency and regret benefits of a proposed method.
Adapts example weights to optimize black-box metrics.
problem Optimizing metrics defined by black-box functions.
method Adaptive example weighting and iterative post-shifting.
result Improves classification performance compared to baselines.
This work improves policy evaluation and selection using logarithmic smoothing for pessimistic off-policy estimation.
problem Offline evaluation and selection of policies from past data.
method Develops novel concentration bounds and a logarithmically smoothed estimator (LS) for improved policy selection and learning.
result The logarithmically smoothed estimator (LS) provides tighter bounds and better policy selection and learning.
Corrects bias in learned generative models using likelihood-free importance weighting.
problem Bias in learned generative models relative to true data distribution.
method Estimate likelihood ratio using a classifier, apply importance weighting.
result Consistently improves goodness-of-fit metrics for deep generative models.
This paper improves SSL methods using ensemble techniques with data-dependent weighted losses.
problem Improving SSL performance and robustness with large unlabeled data.
method Developed a framework for weighted cross-entropy losses in ensembling SSL methods without altering the backbone.
result Our method outperforms state-of-the-art SSL methods on ImageNet-1K, especially in few-shot learning.
RSCNs improve neural network performance with KDE for noisy data.
problem Uncertain data regression problems with noisy or outlier samples.
method RSCNs with weighted least squares, KDE for penalty weights, alternating optimization.
result RSCNs with KDE outperform other robust models in function approximation and real-world applications.
Randomly guessing weights helps analyze RL benchmarks objectively.
problem Understanding the complexity of reinforcement learning benchmarks.
method Generate policy networks by randomly guessing their parameters, evaluate on benchmarks, and analyze results.
result Small untrained networks can provide a robust baseline for various RL tasks.
Study continuous treatment policy evaluation and optimization.
problem Policy evaluation and learning from batched contextual bandit data with continuous treatments.
method Extended IPW and DR methods using kernel functions for continuous settings.
result Consistent policy estimator and convergent regret for learnable policy classes.
Comment on entropy learning for dynamic treatment regimes.
problem Evaluating dynamic treatment regimes using entropy loss.
method Optimization-based alternative to IPW estimate.
result Suggests optimization-based approach for evaluation.
OPERA blends multiple OPE estimators to evaluate new policies offline.
problem Lack of reliable offline policy evaluation methods for new policies.
method Adaptive blending of multiple OPE estimators without explicit selection.
result Consistent and reliable policy evaluation framework for offline RL.
Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.
problem Optimal data collection for policy evaluation in linear bandits with heteroscedastic reward noise.
method Formulated an optimal design for weighted least squares estimates, derived the optimal sample allocation, introduced SPEED algorithm, and derived regret bounds.
result SPEED leads to policy evaluation with MSE comparable to oracle strategy and significantly lower than random policy execution.
Reduces bias in recommendation system evaluations.
problem Offline evaluation bias in collaborative filtering algorithms.
method Weighted offline evaluation method.
result Reduces bias in recommendation system evaluations.
Improved kernel quadrature with convex weights using subsampling.
problem Constructing quadrature rules with small worst-case error.
method Combining spectral properties of the kernel with recombination results.
result Effective algorithms for constructing convex quadrature rules with i.i.d. samples.
New OPE estimator improves offline policy evaluation for large action spaces.
problem Existing OPE estimators fail with large action spaces, leading to extreme bias and variance.
method Proposes a new estimator using marginalized importance weights and action embeddings.
result Empirical performance improvement enables reliable OPE even with many actions.
Paper proposes a new evaluation method for NAS search phase.
problem NAS search phase effectiveness not well evaluated.
method Compare NAS solutions with random selection; evaluate weight sharing strategy.
result State-of-the-art NAS algorithms perform similarly to random selection.
Estimates RL data for dynamic treatment effects using GMM.
problem Estimating dynamic treatment effects from RL data with nonstationary behavior policies.
method Weighted GMM approach to stabilize variance in adaptive RL settings.
result Valid hypothesis testing and confidence regions for dynamic treatment effects.
HOIST uses multiple surrogates to optimize DNN hyperparameters efficiently.
problem Insufficient evaluation data for Bayesian optimization of DNN hyperparameters.
method HOIST combines complete and intermediate evaluation data using weighted bagging of multiple surrogates.
result HOIST outperforms state-of-the-art approaches on various DNN types.
FairNAS improves fairness in neural architecture search evaluation.
problem Improving accuracy of model ranking in neural architecture search.
method Proposes expectation and strict fairness constraints to ensure equal optimization opportunities for all blocks.
result Demonstrates improved model ranking and accuracy with FairNAS-A.
New estimator improves off-policy evaluation for large action spaces.
problem Conventional importance-weighting approaches suffer from excessive variance in off-policy evaluation for large discrete action spaces.
method Proposes OffCEM estimator based on conjunct effect model (CEM), applying importance weighting only to action clusters and using model-based reward estimation for residual effects.
result Proposed estimator is unbiased under local correctness condition, providing substantial improvements in OPE especially with many actions.
BONAS accelerates NAS while maintaining reliability.
problem Computational inefficiency in sample-based NAS.
method Bayesian Optimized Neural Architecture Search (BONAS) using weight-sharing.
result BONAS accelerates sample-based NAS significantly while maintaining reliability.
This study evaluates different portfolio designs for Indian stocks.
problem Optimizing portfolio weights for risk and return in volatile stock markets.
method Three portfolio design approaches: risk minimization, risk optimization, and equal weighting. Historical data from 2017-2022 used.
result Equal-weight portfolios outperformed other designs in most sectors.
An ensemble approach learns vector-weighted formulae for RLR.
problem Learning Relational Logistic Regression (RLR) with vector-weighted features.
method Functional-gradient boosting methods for probabilistic logic models.
result Our approach outperforms other methods for learning RLR.