Unified framework for imitating tasks across domains with discrepancies.
problem Learning tasks across domains with embodiment, viewpoint, and dynamics mismatches.
method Two-step approach: alignment followed by adaptation. Alignment uses Generative Adversarial MDP Alignment (GAMA) for state and action correspondences from unpaired, unaligned demonstrations. Adaptation leverages these correspondences for zero-shot imitation.
result Effectiveness of the proposed approach in embodiment, viewpoint, and dynamics mismatch scenarios.
Deep transfer learning improves sleep staging with small datasets.
problem Insufficient data for deep neural networks due to channel mismatch.
method Transfer learning from large to small datasets, finetuning the pretrained network.
result Significant performance improvement in sleep staging.
This paper improves ASR robustness by learning domain invariant features.
problem Robustness issues in ASR due to mismatched training and testing distributions.
method Factorized Hierarchical Variational Autoencoder (FHVAE) for unsupervised learning of domain invariant features.
result 41% and 27% absolute word error rate reductions on mismatched domains.
End-to-end denoising framework improves SDR and PESQ metrics.
problem Spectrum and metric mismatches in speech enhancement networks.
method Optimizes network on time-domain signals after ISTFT and uses improved loss functions.
result Significantly improved SDR and PESQ performance.
Copula models improve domain adaptation without labeled target data.
problem Improving target label estimation without labeled target data.
method Copula-based regression framework to model conditional predictive densities.
result Effective feature mappings mitigate domain mismatch, leading to more robust and accurate target label estimation.
DRDA robustly adapts models across domains with mismatched distributions.
problem Vulnerability of DA methods to noise and inability to generalize to unseen samples.
method DRDA uses distributionally robust optimization (DRO) with MMD metric to learn robust decision functions.
result DRDA outperforms existing robust learning approaches in experiments.
Ensemble models improve prediction calibration for mismatched distributions.
problem Calibration issues in deep neural networks with mismatched train and test distributions.
method Simple data augmentation and mixing techniques for ensemble models.
result Improves calibration and accuracy on CIFAR10 and CIFAR100 benchmarks.
New approach improves domain adaptation with label shift assumptions.
problem Improving domain adaptation when label distributions differ between source and target domains.
method Proposes generalized label shift (GLS) and modifies three DA algorithms (JAN, DANN, CDAN) to handle label distribution mismatches. result Modified DA algorithms outperform base versions, especially with large label distribution mismatches.
Improves ASR accuracy for domain mismatch using machine translation.
problem Domain mismatch in ASR systems leads to suboptimal results.
method Machine translation to map out-of-domain ASR errors to in-domain terms.
result 7% absolute improvement in word error rate, 4 point BLEU score improvement.
Split-BN improves SSL by separating BN stats for unlabeled data.
problem SSL benefits from unlabeled data are often lost or even harmful when class mismatch exists.
method Split-BN uses separate BN statistics for unlabeled data.
result Split-BN improves SSL performance in shifted domain settings.
New approach tackles class imbalance in long-tailed datasets using domain adaptation techniques.
problem Class imbalance in long-tailed datasets leading to poor model performance.
method Proposes a meta-learning approach to estimate differences between class-conditioned distributions.
result Validated approach on six benchmark datasets and three loss functions.
KalmanNet uses neural networks to improve state estimation in systems with unknown dynamics.
problem State estimation of systems with non-linear dynamics and partial information.
method KalmanNet integrates a recurrent neural network with the Kalman filter to handle non-linearities and model mismatches.
result KalmanNet outperforms classic filtering methods in systems with both mismatched and accurate domain knowledge.
Method learns feature map between source and target domains for high-dimensional regression with missing features.
problem High-dimensional regression with differing feature sets in target and source domains.
method First learns a feature map between missing and observed features using source data, then imputes missing features in target domain, and performs two-step transfer learning for penalized regression.
result Developed upper bounds on estimation and prediction errors for HTL, showing dependence on model complexity, sample size, feature map quality, and domain differences.
End-to-end DA method for domain-invariant CNNs using parallel audio recordings.
problem Distribution mismatches between training and application data in machine listening.
method Enforcing equal hidden layer representations for domain-parallel samples.
result Learn domain-invariant classifiers without requiring classification labels.
We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From …
New algorithm reduces performance loss in IRL with mismatched transition dynamics.
problem Performance degradation in inverse reinforcement learning due to mismatched transition dynamics.
method Proposed a robust Maximum Causal Entropy (MCE) IRL algorithm leveraging robust reinforcement learning insights.
result Empirically demonstrated stable performance improvement under transition dynamics mismatches.
New method estimates individual treatment effects using domain generalization.
problem Estimating causal individual treatment effects from observational data with treatment bias.
method Invariant Risk Minimization (IRM) framework to learn predictors invariant to domain-dependent factors.
result IRM-based ITE estimator shows gains over classical regression approaches in settings with pronounced support mismatch.
CORAL+ adapts PLDA for unsupervised domain adaptation.
problem Domain mismatch in speaker recognition systems.
method Inspired by CORAL, unsupervised PLDA adaptation.
result Improves PLDA performance on unlabeled in-domain data.
Paper introduces Influence Function to assess OOD generalization stability.
problem Assessing OOD generalization accuracy when target domains are unknown.
method Introduced Influence Function from robust statistics to monitor model stability.
result Accuracy on test domains and Influence Function variance can distinguish OOD algorithms and generalization quality.
This paper considers the classification of linear subspaces with mismatched classifiers. In particular, we assume a model where one observes signals in the presence of isotropic Gaussian noise and the distribution of the signals conditioned on a given class is Gaussian with a zero mean and a low-rank covariance matrix.…
Bayesian method matches uncertainty to adapt across domains.
problem Label distribution shift across domains degrades model performance.
method Bayesian neural network quantifies uncertainty; joint feature and label distribution matching.
result Improves model performance on domain adaptation tasks.
The paper addresses score-mismatched diffusion models and zero-shot conditional samplers.
problem Theoretical guarantees for score-mismatched diffusion models in zero-shot conditional sampling.
method Theoretical analysis of score-mismatched diffusion models and zero-shot conditional samplers.
result Theoretical performance guarantees with explicit dimensional dependencies for score-mismatched diffusion samplers.
This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference …
Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions.…
Paper identifies objective mismatch in MBRL, affecting control task performance.
problem Objective mismatch in MBRL framework affects control task performance.
method Proposes re-weighting dynamics model training to mitigate mismatch.
result Likelihood of one-step ahead predictions is not always correlated with control performance.
A fast deep learning method for parallel MRI without calibration.
problem Calibration issues in parallel MRI reconstruction.
method Model-based deep learning, self-learning non-linear annihilation filters, Fourier domain pre-learning.
result Significantly faster than SLR methods (3 orders of magnitude), improved performance with spatial domain prior.
BinaryDuo improves BNNs by coupling binary activations, outperforming state-of-the-art models.
problem Gradient mismatch in BNNs due to binarizing activations.
method Using gradient of smoothed loss function to estimate gradient mismatch, proposing BinaryDuo scheme with coupled ternary activations.
result BinaryDuo outperforms state-of-the-art BNNs on various benchmarks.
Proposes a new semi-supervised learning method to reduce distribution mismatch.
problem Empirical distribution mismatch between labeled and unlabeled data in semi-supervised learning.
method Adversarial training and interpolation strategy to align labeled and unlabeled data distributions.
result Demonstrates improved performance on benchmark datasets SVHN and CIFAR10.
Paper proposes embedding attributes across domains using CNN for better classification.
problem Transfer learning with stable attributes across domains.
method Embed attributes in a common space using CNN, combine domain-independent and domain-specific CNN outputs.
result Effective classification model with minimized errors.
Thompson Sampling shows polynomial regret for combinatorial semi-bandits with subgaussian rewards.
problem Finding optimal solutions in combinatorial semi-bandits with suboptimal sampling.
method Proposes Thompson Sampling with polynomial regret for linear combinatorial semi-bandits.
result Demonstrates 'mismatched sampling paradox' where knowing distributions can lead to worse performance.
DI-SVM improves brain condition decoding performance via domain independence.
problem Transfer learning in brain imaging data with large p and small n.
method DI-SVM minimizes domain dependence via HSIC to learn common features.
result DI-SVM outperforms eight competing methods on brain decoding tasks.
UDA improves ABI robustness but fails under certain prior misspecifications.
problem Robustness of ABI in noisy real-world data.
method Systematic evaluation of UDA across various misspecification scenarios.
result UDA aligns summary spaces but can fail under prior misspecifications.
New bounds for contrastive learning handle domain shifts and generalization.
problem Domain shifts and generalization challenges in downstream tasks.
method Novel generalization bounds accounting for both domain shift and generalization.
result Performance of contrastively learned representations depends on statistical discrepancy between pretraining and downstream distributions.
New method addresses error bounds for PnP-ULA under mismatched models.
problem Error bounds for PnP-ULA under mismatched measurement and prior models.
method Posterior-L2 pseudometric to quantify error bounds.
result Explicit error bound for PnP-ULA under mismatched posterior distribution.
Underspecified ML models can behave unpredictably in real-world use.
problem ML models can fail in real-world deployment due to ambiguous predictors.
method Identified underspecification as the cause, showing it affects various ML domains.
result Underspecified models can behave differently in deployment domains.
Paper addresses linear regression with partially mismatched data using local search with theoretical guarantees.
problem Linear regression with partially mismatched data.
method Optimization formulation and greedy local search algorithm with theoretical guarantees.
result Local search algorithm converges to nearly-optimal solution at a linear rate under certain conditions.
The Mismatch Principle improves Lasso robustness to model uncertainties.
problem Estimation robustness under model misspecifications.
method Generalized Lasso with the Mismatch Principle.
result The Mismatch Principle provides robust error bounds for Lasso.
PACMAN provides bounds for classification tasks considering accuracy vs. negative log-loss mismatch.
problem Mismatch between accuracy and negative log-loss in classification tasks.
method Point-wise PAC approach over generalization gap, using likelihood ratio and concentration inequalities.
result PACMAN provides point-wise PAC bounds for the generalization problem.
Paper proposes a probabilistic alignment method for domain adaptation.
problem Latent distribution mismatch and miscalibrated uncertainty in adapting large-scale models.
method Bayesian latent transport framework with PAC-Bayesian regularization.
result Reduction in latent manifold discrepancy and improved uncertainty calibration.
Deep learning predicts mismatched ratings in Amazon reviews.
problem Identifying reviews with mismatched ratings on Amazon.
method Converted reviews to vectors using paragraph vector, trained a recurrent neural network with gated recurrent unit, incorporated semantic relationships.
result Model accurately predicts rating mismatches and provides feedback.
Study on LMMSE estimation with model mismatch, quantifying MSE trade-offs.
problem Model mismatch in LMMSE estimation with undermodeling.
method Analyzing the average MSE of LMMSE estimation with random regressors.
result Performance improvement depends on sufficient samples and model complexity.
Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution. In this paper, we demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for image classification networks. For illustration, we consider scenarios where this is a comp…
Paper proposes a method to handle linear regression with partially shuffled data.
problem Linear regression with mismatched predictors and responses.
method Pseudo-likelihood approach based on two-component mixture densities with EM optimization.
result The method can tolerate larger fractions of mismatches and estimate noise level.
A new method handles mismatched data in multivariate regression.
problem Handling mismatched data in multivariate linear regression.
method Two-stage approach: first stage estimates parameters, second stage estimates permutation.
result Permutation recovery conditions become less stringent with increasing number of responses.
Generative framework tackles zero-shot learning with adversarial domain adaptation.
problem Domain shift between seen and unseen class distributions in zero-shot learning.
method End-to-end learning of seen and unseen class distributions, adversarial domain adaptation.
result Superior accuracies compared to state-of-the-art models on various benchmark datasets.
Bayesian optimization adapts domain parameters for more robust robot policies.
problem Learning policies for robot control from simulation data often fails in the real world due to the 'reality gap'.
method Bayesian Domain Randomization (BayRn) uses Bayesian optimization to adapt domain parameter distributions during training.
result BayRn achieves better sim-to-real transfer compared to fixed distribution methods.
New method corrects state distribution mismatch for off-policy policy optimization.
problem Mismatch between behavior and evaluation policy state distributions.
method Off-policy policy gradient with state distribution correction.
result Significantly improved policy quality in simulations.
Study improves voice disorder detection system robust to channel effects.
problem Voice signals are sensitive to recording devices.
method Bidirectional LSTM network with domain adversarial training (DAT).
result Increased PR-AUC from 0.8448 to 0.9455 (and 0.9522 with labels).