New method maps protein sequences to embeddings encoding structural information.
problem Inferring structural properties from amino acid sequences when structures are unknown.
method Representation learning using bidirectional LSTM models with structural similarity and residue contact maps.
result Trained embeddings improve structural similarity prediction and transfer to other tasks.
Generative model improves TMP reconstruction from ECG data.
problem Reconstructing cardiac transmembrane potential from surface ECG data is an ill-posed inverse problem.
method A novel model-constrained inference framework using a variational auto-encoder (VAE) with LSTM networks to learn the conditional likelihood of TMP and the prior distribution of generative factors.
result The method significantly improves TMP reconstruction accuracy compared to conventional methods.
Fourier Neural Operators accurately predict dynamics of high-dimensional ionic models.
problem Approximating stiff, multiscale ionic models using neural networks.
method Fourier Neural Operators for learning dynamics of high-dimensional ionic models.
result Fourier Neural Operators can accurately predict dynamics of high-dimensional ionic models.
Paper proposes a new framework for predictive optimization without training data.
problem Prediction in a new domain without training samples.
method Proposes a simple framework for predictive optimization with zero-shot domain adaptation.
result Demonstrates the potential usefulness of the proposed framework through numerical experiments.
Deep learning models have shown state-of-the-art performance in many inverse reconstruction problems. However, it is not well understood what properties of the latent representation may improve the generalization ability of the network. Furthermore, limited models have been presented for inverse reconstructions over ti…
Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.
problem Traditional machine learning models struggle to predict disk failures due to limited data.
method Multi-layer domain adaptive learning with source and target domains.
result The proposed method improves failure prediction accuracy on disk data with few failure samples.
Proposes CTSDG model for better vehicle intention prediction across domains.
problem Domain generalization for vehicle intention prediction in dynamic environments.
method Structural causal model with recurrent latent variable integration.
result Consistent improvement in prediction accuracy compared to state-of-the-art methods.
Proposes a method to predict RUL with domain adaptation for PHM.
problem Domain shift reduces predictive model performance in PHM.
method Long Short-Term Neural Networks (LSTM) for domain adaptation.
result Proposed method provides more reliable RUL predictions under different conditions.
Improves deep network generalization for image sequence reconstruction.
problem Improving generalization of deep networks for inverse image reconstruction.
method Proposes a network optimized by a variational approximation of the information bottleneck principle with stochastic latent space.
result Demonstrates improved generalization ability of inverse reconstruction networks through stochasticity and information bottleneck.
We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples. Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adap…
Transformer models show robustness across domains with domain adversarial training.
problem Domain adaptation from multiple sources with no labeled data.
method Domain adversarial training and mixture of experts.
result Domain adversarial training improves representation but not performance.
Harmonization schemes limit accuracy due to domain information.
problem Harmonization schemes lead to inaccurate predictions due to domain information.
method Analysis of mutual information and real label value informativeness.
result Accuracy is limited by the domain with least information.
DS-CP improves reliability of uncertainty quantification for large language models under domain shift.
problem Overconfident and factually incorrect outputs (hallucinations) from large language models.
method Adapts conformal prediction to large language models under domain shift by reweighting calibration samples.
result DS-CP delivers more reliable coverage than standard conformal prediction, especially under substantial distribution shifts.
New method recovers predictions from unobservable source subpopulation in binary classification.
problem Challenging binary classification with unobservable subpopulation in source domain.
method Distribution matching method to estimate subpopulation proportions, rigorous derivation of prediction models.
result Our method outperforms naive benchmarks in synthetic and real-world datasets.
DSSM separates domain-invariant dynamics from domain-specifics in sequential data.
problem Learning cross-domain sequence representations from diverse data domains.
method Introduce disentangled state space models (DSSM) using unsupervised VAE-based training.
result Improves knowledge transfer and robust prediction across domains.
Efficient approach improves prediction calibration for domain shifts.
problem Improving uncertainty-aware predictions for domain shifts.
method Combining entropy-encouraging and adversarial calibration losses.
result Substantially outperforms existing approaches in domain drift calibration.
Selective pseudo-labeling improves unsupervised domain adaptation.
problem Classifying unlabeled target domain samples with labeled source domain samples.
method Structured prediction for selective pseudo-labeling.
result Selective pseudo-labeling outperforms state-of-the-art methods.
ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
Proposes methods to aggregate prediction intervals for domain shift uncertainty.
problem Uncertainty quantification in distribution shifts.
method Aggregates prediction intervals for minimal width and adequate coverage.
result Effective methodologies for unsupervised domain shift under labeled source and unlabeled target.
New algorithm improves model generalization in structured biomedical domains.
problem Improving model generalization in structured biomedical domains.
method Proposes a new regret minimization (RGM) algorithm and its structured extension for better performance in diverse environments.
result Significantly outperforms previous state-of-the-art baselines on molecular property prediction, protein homology, and stability prediction.
Model predicts counterfactuals under domain shift and inaccessible variables.
problem Runtime domain corruption impairs counterfactual prediction.
method Subsumes counterfactual prediction under domain adaptation, uses adversarial domain adaptation to reduce distribution disparity.
result VEGAN outperforms baselines in individual-level treatment effect estimation.
timeXplain bridges AI and time series, making predictions understandable.
problem Making time series classifier predictions interpretable.
method Developed a framework that combines time series data with model-agnostic explainers.
result timeXplain improves the interpretability of time series classifiers.
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.
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-item preference data. In many real-world applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring k…
The paper tackles stock prediction models by improving their generalizability to out-of-sample domains using causal representation learning.
problem Low signal-to-noise ratio and nonstationary nature of financial markets lead to poor performance of stock prediction models.
method The paper investigates Domain Generalization techniques, focusing on causal representation learning to improve model generalizability. It introduces a novel error bound and a causal discovery technique to mitigate spurious correlations.
result The proposed approach enhances the generalizability of stock prediction models, as demonstrated by numerical results.
Predicts student outcomes in real-time using domain adaptation.
problem Real-time student performance prediction in online courses.
method GritNet architecture with unsupervised domain adaptation.
result GritNet enhances real-time predictions, especially in early weeks.
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.
TASFAR adapts regression models without labeled source data.
problem Lack of labeled source data for domain adaptation.
method Uses prediction confidence to estimate target label distribution and calibrate source model.
result Substantially reduces errors in various regression tasks.
Proposes Infomax and Domain-Independent Representations for robust causal inference.
problem Handling treatment selection bias and domain imbalance in causal inference with real-world data.
method Utilizes mutual information to learn domain-invariant representations that maximize predictive common information.
result Achieves state-of-the-art performance on causal effect inference across various data distributions.
Post-hoc calibration improves uncertainty under domain shift.
problem Improving uncertainty calibration under domain shift.
method Apply perturbations to validation set before post-hoc calibration.
result Perturbation step results in better calibration under domain shift.
Mix-up domain adaptation improves dynamic RUL predictions across various conditions.
problem Dynamic RUL predictions under non-i.i.d conditions.
method Three-staged mechanism with mix-up strategy for source and target domains alignment, self-supervised learning.
result MDAN outperforms existing methods in 12 out of 12 cases for dynamic RUL predictions.
Proposes first method for continuously indexed domain adaptation.
problem Challenges of transferring knowledge between continuously indexed domains.
method Combines adversarial adaptation with a novel discriminator.
result Outperforms state-of-the-art methods on synthetic and real-world datasets.
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in whic…
Improves predictive algorithm performance with domain adaptation.
problem Improves performance of predictive algorithms in distributional shift scenarios.
method Domain adaptive bootstrap aggregating with iterative nearest neighbor sampling.
result Proposes a method to improve predictive algorithm performance in distributional shift scenarios.
Causal inference is similar to prediction with treatment bias.
problem Generalizing from labeled to unlabeled data with treatment effects.
method Reframing causal inference as a prediction problem with explicit assumptions.
result Causal assumptions are not uniquely strong but more explicit.
Study shows cross-domain X-ray prediction performance discrepancies and label shifts.
problem Quantifying generalization limits across different X-ray datasets.
method Large-scale study on multiple X-ray datasets, focusing on performance and label shifts.
result Interesting discrepancies found between model performance and agreement, and concept similarity across tasks.
TED framework teaches AI to explain decisions, improving accuracy.
problem Providing understandable explanations for AI predictions in high-stakes applications.
method Augmenting training data with explanations from domain users, using embeddings and multi-task learning.
result AI models can be taught to provide meaningful explanations, sometimes improving accuracy.
Paper improves bike-sharing demand prediction by adapting to changing patterns.
problem Improving bike-sharing demand prediction under temporal domain shifts.
method Gen-ROTDA, a robust optimal transport-guided residual domain adaptation framework.
result Gen-ROTDA achieves the lowest MAE and is the best OT-family method on average.
Improved segmentation model adaptation for new domains.
problem Reduced performance of pre-trained models on new domains.
method Calculated soft-label prototypes and predicted closest to class probabilities.
result Significant performance improvements on synthetic-to-real segmentation.
Algorithm calibrates predictions for covariate shift using domain adaptation.
problem Uncertainty estimates overestimate certainty when real-world data differs from training data.
method Uses importance weighting and learns a feature map to equalize distributions.
result Outperforms existing approaches in calibrated prediction when covariate shift occurs.
Causal predictors don't generalize better across domains than non-causal predictors.
problem How well do causal predictors generalize across different domains?
method 16 prediction tasks on tabular datasets, selecting causal features.
result Causal predictors do not outperform non-causal predictors in domain generalization.
New algorithm constructs PAC prediction sets for meta-learning.
problem Uncertainty quantification in safety-critical systems.
method Proposes a novel algorithm to construct PAC prediction sets.
result Prediction sets satisfy a PAC guarantee with high probability over future tasks.
G-FuNK learns solutions for nonlinear PDEs on multiple domains and parameters.
problem Predicting time-dependent dynamics of complex systems governed by nonlinear PDEs with varying parameters and domains.
method Graph Fourier Neural Kernels combining domain-adapted and transferable components for non-diffusive and diffusive terms.
result G-FuNK achieves low relative errors on unseen domains and fiber fields, significantly accelerating predictions.
The paper proposes a method to adapt models from source to target domains by calibrating their predictive uncertainties.
problem Inferring class labels for unlabeled target domain given a related labeled source dataset.
method The approach involves calibrating predictive uncertainties quantified as Renyi entropy, using variational Bayes learning and sample variance regularization.
result The proposed method effectively adapts models across three domain-adaptation tasks.
Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based inf…
Synthesizes robust estimators for domain adaptation.
problem Improving prediction accuracy in target domain with limited data.
method Synthesizes a family of robust least squares estimators using convex optimization.
result Robust strategies can outperform non-robust interpolations.
Develops neural network for implied volatility surface prediction with financial domain knowledge.
problem Predicting implied volatility surface using neural networks.
method Incorporates prior financial domain knowledge into neural network architecture and training process.
result Model outperforms benchmarks and satisfies financial conditions.
Study evaluates how much knowledge LLMs have by comparing their prediction accuracy to flexible models.
problem Evaluating the predictive power of LLMs without access to their training data.
method Equivalent sample size measure, comparing LLM's prediction error to flexible models trained on varying amounts of domain-specific data.
result LLMs encode varying amounts of predictive information across different economic variables.