Paper improves uncertainty estimation in LLM-as-a-judge systems.
problem Improving uncertainty estimation in LLM-as-a-judge frameworks.
method Generalised probabilistic modelling and improved uncertainty estimates.
result Proposed uncertainty estimates significantly improve system efficiency.
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been widely used for unsupervised anomaly detection. By the recent advance of deep learning, the density estimation performance has been greatly i…
The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized. Model uncertainty estimation can address this issue, where uncertainty estimates are often based on the variation in predictions produced by a diverse ensemble of models …
Self-distillation optimally improves model performance in spiked covariance models.
problem Improving model performance in spiked covariance models.
method Developed spectral shrinkage estimators and analyzed self-distillation.
result Self-distillation achieves optimal performance among spectral shrinkage estimators for spiked covariance matrices.
New method improves off-policy critic evaluation in reinforcement learning.
problem High variance and instability in off-policy policy evaluation.
method Doubly robust estimators applied to actor-critic algorithms.
result Doubly robust estimation significantly improves performance in continuous control tasks.
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for th…
Unified framework for distribution shift estimation, explanation, and improvement.
problem Estimating, explaining, and improving model performance on target domains with distribution shift.
method Entropic Projection Alignment (EPA) aligns source and target distributions by matching moments and minimizing KL divergence.
result EPA consistently outperforms state-of-the-art baselines while offering computational efficiency.
Improved confidence interval estimation with control variates.
problem Estimating confidence intervals with minimal samples.
method Designing an estimation algorithm using control variates and leveraging order statistics.
result Improved asymptotic efficiency compared to existing algorithms.
SAVE combines Q-learning and MCTS with amortized value estimates for improved performance.
problem Combining model-free Q-learning and model-based MCTS for efficient learning and planning.
method SAVE uses a learned prior to guide MCTS, which estimates improved state-action values. These estimates are used to update the prior, creating a cooperative relationship between learning and search.
result SAVE achieves higher rewards with fewer training steps and strong performance with small search budgets.
Improved MoM estimator enhances classical shadows protocol for quantum measurements.
problem Efficient estimation of expectation values with reduced measurement shots.
method Modified median-of-means estimator with optimal constants and U-statistics.
result Improved performance of modified estimator for Clifford measurements.
Improved speech recognition model with better performance.
problem Speech recognition accuracy on Librispeech.
method Integrates an external language model with an internal LM correction.
result Over 14% relative improvement in performance.
Meta-learning improves Gaussian process uncertainty estimation.
problem Poor uncertainty estimation in Gaussian processes with deep kernels.
method Meta-learning to calibrate deep kernel GPs using task-specific uncalibrated and calibrated distributions.
result Improves uncertainty estimation performance with high regression performance.
Simple method improves uncertainty estimation for distribution shifts.
problem Improving uncertainty estimation in deep image classification under distribution shifts.
method Exposing original model to corrupted images and performing simple statistical calibration.
result Superior performance on various distribution shifts and unsupervised domain adaptation tasks.
Improved quantile estimation model for VaR.
problem Improving quantile estimation under distribution estimation.
method Develops a compensatory model with a penalty term to control convergence error.
result Significant improvement in VaR performance.
Unified DICE estimators as regularized Lagrangians for improved off-policy evaluation.
problem Improving off-policy evaluation from behavior-agnostic data.
method Unified derivation of DICE estimators as regularized Lagrangians of a linear program.
result Dual solutions offer greater flexibility and provide superior estimates in practice.
One-step policy improvement outperforms iterative RL methods on D4RL.
problem Improving offline RL without off-policy evaluation.
method One-step constrained/regularized policy improvement using on-policy Q estimates.
result One-step algorithm outperforms iterative algorithms on D4RL benchmark.
Paper proposes a new method for estimating mixture proportions without irreducibility assumption.
problem Estimating mixture proportions when component distributions are not irreducible.
method Developed a resampling-based meta-algorithm that adapts existing MPE algorithms to non-irreducible settings.
result Empirical results show improved estimation performance compared to baseline methods and regrouping-based algorithms.
Improves robustness of propensity score estimators in challenging settings.
problem Limited overlap, small sample sizes, or unbalanced data.
method Extends calibration techniques for propensity score models, focusing on sample-splitting schemes.
result Calibration reduces variance and bias in inverse probability weighting and double/debiased machine learning frameworks.
The paper improves density estimation in high dimensions using tensor decompositions.
problem Density estimation struggles in high-dimensional data due to the curse of dimensionality.
method The paper uses nonnegative tensor decompositions to simplify dependence assumptions and estimate marginal distributions.
result Theoretical results show that restricting estimation to low-rank nonnegative PARAFAC or Tucker decompositions removes the dimensionality exponent on bin width rates.
DMLE improves active learning by correcting MLE for sample dependencies.
problem Dependencies among samples in active learning affect model parameter estimation.
method Dependency-aware Maximum Likelihood Estimation (DMLE).
result DMLE achieves superior performance across multiple benchmark datasets.
Improves SSL with doubly robust estimation of unlabeled class distribution.
problem Limited labeled data and long-tailed class distributions in unlabeled data.
method Explicitly estimate unlabeled class distribution using doubly robust estimator.
result Improves performance of SSL methods on unlabeled data.
Develops framework for estimating and improving DTRs with time-varying IV in the presence of unmeasured confounding.
problem Estimating DTRs from observational data with unmeasured confounding.
method Time-varying instrumental variable (IV) framework for estimating and improving DTRs.
result IV-optimal and IV-improved DTRs perform better than DTRs assuming no unmeasured confounding.
Proposes a new cross-validation method to estimate model performance.
problem The standard cross-validation method does not accurately estimate the performance of the recommended model.
method Develops a new random-effects model framework to improve naive cross-validation estimators.
result Proposed estimators outperform conventional and naive methods in estimating model performance.
New method improves NN performance across various settings.
problem Improving neural network performance across different datasets and architectures.
method Population Gradients (PG) method to calculate non-local gradient estimates.
result Significantly improves final performance across architectures, data-sets, and hyper-parameters.
Optimizes spectral density estimation for stationary and nonstationary processes.
problem Estimating spectral density of time series with complex structure.
method Optimally adaptive Bayesian spectral density estimation using smoothing spline covariance structure.
result Optimal eigendecomposition provides superior performance compared to alternative covariance functions.
Random variables of the generalized Pareto distribution, can be transformed to that of the Pareto distribution. Explicit expressions exist for the maximum likelihood estimators of the parameters of the Pareto distribution. The performance of the estimation of the shape parameter of generalized Pareto distributed using …
New estimator uses clustering to improve off-policy evaluation accuracy.
problem Improving off-policy evaluation accuracy when logging and evaluation policies differ.
method Proposes an estimator that shares information across similar contexts using clustering.
result Clustering contexts improves estimation accuracy, especially in deficient information settings.
Paper improves neural network robustness certification with tighter radii estimates.
problem Certifying neural networks' robustness against adversarial attacks.
method Advanced algorithms for discrete and continuous domains, optimizing sample size, standard deviation, and temperature.
result Significant improvement in certified test-set accuracy with tighter certified radii bounds.
Bagging, a powerful ensemble method from machine learning, improves the performance of unstable predictors. Although the power of Bagging has been shown mostly in classification problems, we demonstrate the success of employing Bagging in sparse regression over the baseline method (L1 minimization). The framework emplo…
Global optimization in Bayesian inference yields little additional benefit.
problem Improving psychometric parameter estimation using global optimization strategies.
method Experimental simulations comparing myopic and global strategies in multiple models.
result Global optimization strategies provide negligible additional utility improvement beyond the immediate next steps.
Efficient exploration improves large language model performance with fewer queries.
problem Improving large language model performance with fewer human feedback queries.
method Sequentially generates queries, fits a reward model to feedback, uses double Thompson sampling with epistemic neural network uncertainty.
result Efficient exploration enables high performance with far fewer queries.
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.
Matrix estimation improves individual fairness without sacrificing performance.
problem Ensuring fairness in algorithmic decision-making.
method Using singular value thresholding (SVT) to preprocess data.
result SVT pre-processing improves IF guarantees and maintains performance.
We consider the problem of estimating multiple principal components using the recently-proposed Sparse and Functional Principal Components Analysis (SFPCA) estimator. We first propose an extension of SFPCA which estimates several principal components simultaneously using manifold optimization techniques to enforce orth…
Improved pre-trained embeddings through effective entropy maximization.
problem Developing high-quality pre-trained embeddings for future tasks.
method E2MC criterion defined in terms of low-dimensional constraints.
result Significant improvement in downstream performance.
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing …
Improves model calibration and selection in unsupervised domain adaptation.
problem Distribution shifts in unsupervised domain adaptation.
method Developed a novel importance weighted group accuracy estimator.
result Improves state-of-the-art performances by 22% in model calibration and 14% in model selection.
Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters. In this work, we introduce a novel method to allow simple non-differentiable functions at intermediary layers of deep neural networks. We do so by training with a differentiable…
Paper tackles inconsistent CATE estimation across group assignments.
problem Inconsistent learning behavior for the same instance across different group assignments.
method CLAGA method to eliminate inconsistency.
result Significant performance improvements with CLAGA method.
Study improves unbiased recommender learning by addressing missing-reward bias.
problem Data bias caused by missing-reward observations in recommender systems.
method Proposes a novel estimator using propensity scores to mitigate both position and reward bias.
result The proposed estimator outperforms other methods, even with increased reward observation bias.
DAEDL improves EDL's OOD detection and classification performance by integrating feature space density.
problem Limited OOD detection and classification performance of EDL.
method Integrates feature space density with EDL's output and uses a novel parameterization.
result Demonstrates state-of-the-art performance across uncertainty estimation and classification tasks.
Estimates MLDS using tensor decomposition, improving upon existing methods.
problem Learning mixtures of linear dynamical systems from input-output data.
method Proposes a moment-based estimator using tensor decomposition.
result Improves sample complexity bounds for estimating MLDS.
Post-calibration improves the accuracy of causal effect estimation.
problem Improperly calibrated propensity scores lead to inaccurate causal effect estimation.
method Performed a simulation study to assess the impact of post-calibration on causal effect estimation.
result Post-calibration reduces the error in estimating the average treatment effect, especially for expressive uncalibrated statistical estimators.
This paper improves error estimation in covariate shift by incorporating target information.
problem Error estimation is inaccurate in covariate shift scenarios.
method Proposes a redefinition of importance using target information for better error estimation.
result Incorporating target information leads to more accurate error estimation, especially with KLIEP.
Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with Baseline Bootstrapping (SPIBB) fix: reproduce the baseline policy in the uncertain state-action pairs, in order to control the variance on th…
FE-GAN improves VaR and ES estimation in financial risk management.
problem Improving VaR and ES estimation in financial risk management.
method Feature-Enriched Generative Adversarial Networks (FE-GAN) with specialized models like WGAN and Tail-GAN.
result FE-GAN significantly outperforms traditional GANs in VaR and ES estimation.
Paper improves MRI reconstruction by separating target labels and prediction error.
problem Improving MRI reconstruction accuracy by estimating prediction error.
method Proposes a novel method to estimate target labels and prediction error separately.
result Significantly better MRI reconstruction results achieved compared to state-of-the-art methods.
Two new ensemble methods improve CATE estimation across various scenarios.
problem Estimating CATE in clinical trials to understand treatment effects heterogeneity.
method Proposed two ensemble methods: Stacked X-Learner and Consensus Based Averaging (CBA).
result Ensemble methods achieve good performance across diverse scenarios.