Enhanced Bayesian target encoding uses sampling techniques to improve model performance.
problem Improving target encoding for better model performance in machine learning.
method Using sampling techniques in Bayesian target encoding to extract intra-category distribution information.
result Improves generalization and reduces target leakage in machine learning models.
Generative multitask learning mitigates confounders causing targets.
problem Unobserved confounders causing targets but not inputs.
method Generative multitask learning (GMTL) modifies inference objective to remove joint target influence.
result Improved robustness to target shift across multitask learning methods.
RL models improve target control in SSGs for security applications.
problem Improving RL algorithms for target control in SSGs.
method Investigates improvements to target representations in RL algorithms.
result Enhanced RL models control targets better in SSGs.
New approach improves domain adaptation by enforcing cluster assumption in target domain.
problem Lack of robustness in target classifier due to violation of cluster assumption.
method Enforces cluster assumption in target domain (Target Consistency) paired with Class-Level InVariance.
result Significant improvement in image classification and segmentation benchmarks.
Improved prediction of soil parameters using Multi-target Stacked Generalisation on EDXRF spectra.
problem Challenges in predicting multiple soil parameters accurately from EDXRF spectra.
method Multi-target Stacked Generalisation (MTSG) method combining multiple regression models.
result MTSG significantly improved prediction accuracy for multiple soil parameters, reducing average error from 0.67 to 0.64.
With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this f…
Framework improves target domain prediction using quantile matching.
problem Improving prediction accuracy in data-scarce target domains.
method Conditional quantile matching for distributional alignment.
result Empirical risk minimizer achieves tighter excess risk bound.
Stein discrepancy improves UDA performance in low-data scenarios.
problem Improving model performance on unlabeled target domains with limited data.
method Proposes a novel UDA framework using Stein discrepancy, an asymmetric measure that depends on the target distribution through its score function.
result Consistently outperforms prior UDA approaches under limited target data across multiple benchmarks.
TILT improves target domain performance by penalizing an auxiliary component on unlabeled target inputs.
problem Improving performance on target domain under covariate shift.
method TILT uses a novel objective function to decompose the source predictor and penalize an auxiliary component on unlabeled target inputs.
result TILT improves target domain performance over source-only training and other baselines.
Improves probability distribution compression with KT algorithm.
problem Efficiently compressing probability distributions.
method Kernel thinning (KT) algorithm with four improvements.
result KT yields tighter, dimension-free guarantees for any kernel.
Improved loss scaling for stochastic momentum algorithms in high dimensions.
problem Improving loss scaling for stochastic momentum algorithms in high dimensions.
method Dimension-adapted Nesterov acceleration (DANA) scales momentum hyperparameters based on model size and data complexity.
result DANA improves loss scaling exponents across various data and target complexities.
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.
Improves unsupervised domain adaptation by mixing source and target domains.
problem Improves unsupervised domain adaptation by mixing source and target domains.
method Enforces training constraints across domains using mixup formulation and feature-level consistency regularizer.
result Significantly improves state-of-the-art performance on image classification and human activity recognition tasks.
New approach to meaningful and robust algorithmic recourse.
problem Ineffective and unmeaningful algorithmic recourse explanations.
method Meaningful Algorithmic Recourse (MAR) and Effective Algorithmic Recourse (EAR).
result Proposes new constraints for algorithmic recourse that improve both prediction and target.
In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data. This method builds on the state-of-the-art Multisource Tradaboost, but weighs the importance of each datapoint taking into account the amount of target and source data available. A compa…
This research improves online learning by correcting for target shift in machine learning.
problem Online learning struggles with distributional shift, especially in target values.
method Derives closed-form expressions for online and offline learning, and target correction.
result Online kernel-based learning can learn the same predictor as offline learning with target correction.
Improves supervised learning with target-embedding autoencoders.
problem Improving generalization in purely supervised settings with high-dimensional target spaces.
method Target-Embedding Autoencoders (TEA) for jointly optimizing latent representations for prediction and feature predictability.
result Guaranteed generalization for linear TEAs through uniform stability, and empirical validation across multivariate sequence forecasting.
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be …
Domain Fusion uses GANs to augment data for low-volume target datasets.
problem High costs in data development for deep learning applications.
method Multi-domain learning GANs to generate new samples.
result Domain Fusion achieves better classification accuracy with less data.
Improves AI agents' 3D navigation by learning from failures and 3D spatial relationships.
problem Challenges in data efficiency, obstacle avoidance, and generalization in 3D visual navigation.
method Incorporates attention on 3D spatial relationships and a target skill extension module into DRL framework.
result Significantly improves navigation performance and generalization across targets and scenes.
Improves domain adaptation by aligning source and target distributions and mitigating noisy labels.
problem Improving performance on target images with different acquisition conditions.
method Combines optimal transport, MixUp regularization, and robust loss for noisy labels.
result Improves domain adaptation performance on various benchmarks and real-world problems.
Paper proposes SDDP for improving time series forecasting with high-dimensional predictors.
problem Improving time series forecasting with high-dimensional predictors.
method SDDP framework that incorporates target variable and lagged observations into factor extraction process.
result SDDP improves predictive accuracy in time series forecasting.
Optimizes target value in stochastic black box functions.
problem Finding input to minimize expected squared error to target value.
method Derives acquisition functions for expected improvement, probability of improvement, and lower confidence bound, assuming Gaussian aleatoric effects.
result Acquisition functions can outperform classical Bayesian optimization under certain conditions.
New estimators improve efficiency in two-phase designs with coarsened data.
problem Efficient estimation in two-phase designs with incomplete data.
method Developed new estimators within the TMLE framework.
result New estimators are asymptotically equivalent and more efficient.
Improved sample complexity for target Q-learning in finite MDPs with generative oracle.
problem Sample complexity of target Q-learning in finite MDPs with a generative oracle.
method Analyzed target Q-learning algorithm in tabular case with a generative oracle, improved sample complexity.
result Improved sample complexity for target Q-learning in various scenarios.
Adversarial AutoAugment improves image classification with less computation.
problem Improving deep neural network generalization through data augmentation.
method Adversarial method to optimize augmentation policies without retraining the target network.
result Significant performance improvements on ImageNet and CIFAR-10.
We observe that several existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may suffer from overly large gradients when the current policy is close to deterministic (even in some very simple environments), leading to an unstable training process. To address this issue, we propose a new method, …
XMixup improves transfer learning accuracy by 1.9% with less training time.
problem Efficiently transfer knowledge from large source datasets to target tasks with small samples.
method Cross-domain Mixup technique that selects auxiliary samples from source datasets and augments training samples via mixup strategy.
result Improves accuracy by 1.9% on average over six real-world transfer learning datasets.
This work explores Target Networks and Functional Regularization in deep Reinforcement Learning.
problem Stability and performance issues in deep Reinforcement Learning due to target value instability.
method Proposes and studies an explicit Functional Regularization approach as a replacement for Target Networks.
result Functional Regularization improves performance and stability compared to Target Networks.
Framework improves policy generalizability under biased training data.
problem Learning policies that generalize to a target population from biased training data.
method Characterizes sample selection bias using a selection variable, optimizes minimax value over uncertainty set, derives efficient algorithm.
result Policies generalize to target population, outperform standard methods.
Federated learning method improves covariate shift adaptation for missing target values.
problem Missing target values in federated learning.
method Federated covariate shift adaptation algorithm for missing target output values.
result Asymptotically unbiased and efficient algorithm for federated learning.
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the target states, birth and death times, and association of observations to targets, …
Target encoding is an effective technique to deliver better performance for conventional machine learning methods, and recently, for deep neural networks as well. However, the existing target encoding approaches require significant increase in the learning capacity, thus demand higher computation power and more trainin…
New split rules improve subpopulation targeting in policy-making.
problem Improving binary classification for subpopulation targeting in policy-making.
method MDFS, PFS, wEFS for maximizing distance and penalizing final splits.
result Proposed methods target more vulnerable subpopulations than classic CART/KD-CART.
Effective multilingual search with instance-based transfer learning.
problem Search in multilingual setting, especially next-sentence prediction and inverse cloze.
method Instance-based transfer learning, analyzing vocabulary overlaps and transitive overlaps.
result Positive transfer on all 35 target languages and two tasks, even with no vocabulary overlap.
Improved sampling efficiency for inverse problems using variance-reduced diffusion methods.
problem Efficiently estimating noisy scores in inverse problems.
method Developed a nonparametric self-normalized importance sampling estimator and a state-dependent blending rule.
result Improved sample quality for fixed simulation budgets in synthetic targets and PDE-governed inverse problems.
New sampling methods improve statistical efficiency for intractable targets.
problem Sampling from complex, intractable probability distributions.
method Gaussian invariant versions of RWM, MALA, and Hessian MALA.
result Gaussian invariant sampling leads to improved statistical efficiency.
We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a fam…
Improves score estimation for noised targets using known clean scores.
problem Poor score estimation at low noise levels in Denoising Score Matching.
method Introduces Target Score Identity and Target Score Matching loss.
result Score estimates are more accurate at low noise levels.
Improves unsupervised domain adaptation by enforcing feature extractor to focus on task-relevant information.
problem Leveraging label information from source domain for accurate target domain models without labels.
method Variational Information Bottleneck (VBDA) method that explicitly enforces feature extractor to ignore irrelevant task factors.
result Significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.
TL-PCA uses transfer learning to improve PCA performance with limited target data.
problem PCA performance is limited with scarce target data.
method Transfer learning approach to PCA (TL-PCA) that combines source task knowledge with target task data.
result Improved PCA representation for dimensionality reduction with limited target data.
In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investmen…
A new method improves target selection for manipulating complex systems like the brain.
problem Improper incorporation of low-variance outcomes into latent space of predictive models.
method Developed a novel objective based on supervised variational autoencoders (SVAEs) for PPCA (Probabilistic Principal Component Analysis).
result gPCR (Generative Principal Component Regression) dramatically improves target selection in manipulation compared to standard PCR and SVAEs.
HTAD predicts diagnoses from EHRs using target-aware attention.
problem Lack of specificity in aggregating clinical records for diagnosis prediction.
method Hierarchical target-attentive mechanism for EHRs.
result Target-aware attention significantly improves diagnosis prediction performance.
AIS uses a suboptimal extended target distribution, which this paper improves using SGM.
problem Improving the efficiency of Annealed Importance Sampling for marginal likelihood estimation.
method Leveraging score-based generative modeling to approximate the optimal extended target distribution.
result Demonstrated novel, differentiable AIS procedures on synthetic and real-world data.
Paper tackles target shift in zero-shot learning using adversarial learning.
problem Target shift in zero-shot learning leads to performance degradation.
method Estimates target shift using class-attribute mapping and applies grouped adversarial learning.
result Improves zero-shot learning performance on multiple datasets.
Improved KSD test for better detection of differences in distributions.
problem Low power of KSD test when distributions have same modes but different mixing proportions.
method Perturb the observed sample using Markov transition kernels to improve KSD test power.
result Perturbed KSD test can lead to substantially higher power than the original KSD test.
Optimal weights improve particle-based approximations of discrete distributions.
problem Improving particle-based approximations of discrete distributions.
method Proving optimality of weights and showing how to compute them efficiently.
result Optimal weights can be computed from existing particle-based methods without extra costs.