Paper tackles source attribution for LLM-generated texts.
problem Identify the data provider of synthetic texts generated by LLMs.
method Watermarking to embed information about data providers in synthetic texts.
result Proposes and evaluates a source attribution framework that achieves effective source attribution.
The paper tackles robust policy learning from multiple data sources.
problem Learning a policy that generalizes across diverse settings from multiple heterogeneous data sources.
method Proposes a minimax regret optimization objective and a policy learning algorithm combining doubly robust offline policy evaluation and no-regret learning.
result Achieves minimal worst-case mixture regret up to a moderated vanishing rate of the total data across all sources.
Bayesian approach uses generative models as priors for better source separation.
problem Artifacts in source separation for richly structured data.
method Bayesian approach with generative models as priors and noise-annealed Langevin dynamics.
result Achieves state-of-the-art performance for MNIST digit separation.
Improves understanding of PWS by calculating influence of sources and data.
problem Understanding the influence of each component in PWS.
method Proposes source-aware Influence Function (IF) to decompose and calculate influence.
result Improves end model's generalization performance and identifies mislabeling.
Novel unsupervised audio source separation using generative priors.
problem Challenges of supervised methods in audio source separation.
method Generative priors trained on individual sources using projected gradient descent optimization.
result Effective recovery of constituent sources with good-quality estimates.
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation …
Improves neural network performance by dynamically adjusting model weights based on source reliability.
problem Training neural networks on data from unreliable sources leads to poor performance.
method Dynamic re-weighting strategy using likelihood tempering to adjust model weights based on estimated source reliability.
result Significant improvement in model performance when trained on mixtures of reliable and unreliable data sources.
Study shows multi-source learning is more resilient to adversarial corruption than single-source learning.
problem Learning from multiple untrusted data sources, especially when some are adversarially corrupted.
method Analyzed the scenario where an adversary can corrupt a fixed fraction of data sources, derived a generalization bound for this setting.
result PAC-learnability is possible in the multi-source setting even when some data sources are adversarially corrupted.
Model learns from multiple data sources for D2T and T2D tasks.
problem Limited performance due to single-source corpora.
method Variational auto-encoder with disentangled style and content variables.
result Model outperforms single-source counterpart on multiple datasets.
New technique for multiple-source adaptation without density estimation.
problem Multiple-source adaptation problem.
method Discriminative technique that uses conditional probabilities from unlabeled data.
result Our technique outperforms previous generative solutions and other domain adaptation baselines.
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
problem Overoptimistic cross-validation estimates for new patient sources.
method Empirical evaluation of K-fold and leave-source-out cross-validation methods.
result Leave-source-out cross-validation provides more reliable performance estimates.
The paper tackles distribution-free prediction intervals for multi-source data.
problem Challenges in achieving valid inferences due to distribution shifts and privacy concerns.
method Derives efficient influence functions, incorporates machine learning, and proposes data-adaptive strategies.
result Achieves parametric rates of convergence to nominal coverage probabilities for prediction intervals.
This study tackles offline RL with perturbed data sources, deriving a lower bound and proposing an optimal algorithm.
problem Understanding offline RL with multiple perturbed data sources.
method Derives an information-theoretic lower bound, proposes HetPEVI algorithm considering sample and source uncertainties.
result HetPEVI is optimal up to a polynomial factor of the horizon length and can solve offline RL tasks.
This paper develops a method to estimate the rate-distortion function for general data sources.
problem Estimating the rate-distortion function for general data sources.
method Develops an algorithm for sandwiching the R-D function of a general (not necessarily discrete) source using i.i.d. data samples.
result Estimates R-D sandwich bounds for various data sources, including natural images, indicating potential for improving compression methods.
Method transfers knowledge without label overlap, source data, or target architecture consistency.
problem Difficulties in transfer learning due to label mismatch, restricted source data, and specialized target architectures.
method Uses deep generative models in two stages: pseudo pre-training and pseudo semi-supervised learning.
result Outperforms scratch training and knowledge distillation methods.
Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source se…
Proposes a transfer learning framework for sparse SIMs without raw source data.
problem Lack of direct access to raw source data and known link functions in transfer learning.
method Source-data-free framework based on SIM, using summary statistics and a multilayer perceptron.
result Consistent improvements over existing approaches in synthetic and real-world data.
Study improves model fit by transferring info from related datasets.
problem Improving model fit on target data using source data.
method Proposes a transfer learning algorithm for GLMs, derives error bounds, and introduces detection of informative sources.
result Theoretical and practical improvements over classical methods in high-dimensional GLM settings.
Survey explores methods to adapt deep learning models across multiple labeled domains.
problem Difficulty in obtaining labeled data for deep learning models.
method Multi-source domain adaptation (MDA) to transfer knowledge from labeled to unlabeled or sparsely labeled target domains.
result MDA methods improve performance by minimizing domain shift.
Developing a visual platform for faster astronomical source cataloging.
problem Speeding up cataloging of large area surveys in radio astronomy.
method Integration of advanced source finding and classification tools into a visual analytic platform.
result Improvement and acceleration of cataloging process in astronomical surveys.
Bayesian method mitigates negative transfer in unknown source data.
problem Negative transfer in transfer learning where target performance worsens after source data consideration.
method Proxy-informed robust method for probabilistic transfer learning (PROMPT).
result Negative transfer can be mitigated without prior knowledge of source data.
Proposes LVGP for multi-source data fusion in science and engineering.
problem Differences in quality and comprehensiveness of data sources.
method Latent Variable Gaussian Process (LVGP) framework.
result Improved predictions for sparse-data problems.
A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.
problem Analyzing multi-source, multi-way data from different high-throughput technologies.
method Multiple Linked Tensor Factorization (MULTIFAC) extending CP decomposition with L2 penalties and EM algorithm for incomplete data.
result MULTIFAC approximates underlying signal, identifies shared and unshared structures, and imputes missing data.
A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision sources, like user-defined heuristics or knowledge bases, to label training data. Prior work has explored learning accuracies for these source…
Framework reuses pre-trained models for data-free transfer learning.
problem Challenges in retrieving source data for model training.
method Model Recycling Framework for parameter-efficient training.
result Makes multi-source data-free supervised transfer learning possible.
Algorithm adapts pretrained semantic segmentation models to new domains.
problem Adapting pretrained models to new, unlabeled domains without source data.
method Learn prototypical distribution in embedding space, align target domain with source domain.
result Method achieves competitive performance on benchmark tasks.
Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. Thi…
New method restores source features for SFDA without source data.
problem Domain adaptation without access to source data.
method Feature Restoration (FR) and Bottom-Up Feature Restoration (BUFR).
result BUFR outperforms existing SFDA methods in accuracy, calibration, and data efficiency.
Method fuses low and high-resolution data for better health estimates.
problem Improving high-resolution health estimates from mixed data sources.
method Fusion of unbiased low-resolution and potentially biased high-resolution data, learning a distribution consistent with sampling bias.
result Significant reduction in bias in high-resolution estimates.
The paper develops methods to identify stable associations across multiple studies.
problem Identifying stable associations across multiple studies with possible distributional shifts.
method Modeling heterogeneous multi-source data with multiple high-dimensional regressions and devising a novel sampling method for valid confidence intervals of maximin effects.
result Significant maximin effects indicate stable associations that can be generalized to target populations.
SYNC generates synthetic data from aggregated sources using Gaussian copulas.
problem Creating synthetic datasets from aggregated sources.
method SYNC uses Gaussian copula models to infer high-resolution data from low-resolution sources.
result SYNC successfully merges sampled subsets into a single synthetic dataset.
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
problem Data scarcity in African agriculture limits machine learning innovations.
method SAGDA is an open-source Python toolkit that generates, augments, and validates synthetic agricultural datasets.
result SAGDA enhances ML applications in agriculture, such as yield prediction and fertilizer recommendation.
Improves domain adaptation by combining multiple source domains and target domain data.
problem Poor performance of empirical risk minimization in distributionally shifted target domains.
method Distributionally robust model optimizing adversarial reward based on explained variance across multiple source domains.
result The robust model is a weighted average of conditional outcome models from source domains.
Adaptive kernel approach learns causal effects from diverse data sources.
problem Learning causal effects from multiple, decentralized data sources in a federated setting.
method Adaptive transfer algorithm using Random Fourier Features to estimate similarities and disentangle loss function components.
result Empirically outperforms baselines on decentralized data sources with different distributions.
Suppose that we are given a time series where consecutive samples are believed to come from a probabilistic source, that the source changes from time to time and that the total number of sources is fixed. Our objective is to estimate the distributions of the sources. A standard approach to this problem is to model the …
Paper tackles robust transfer learning with unreliable source data.
problem Challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals.
method Introduces ambiguity level, proposes Transfer Around Boundary (TAB) model, establishes general theorem.
result Demonstrates efficiency and robustness of TAB model improving classification while avoiding negative transfer.
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.
Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological di…
Study optimizes data collection from biased, costly sources to minimize risk.
problem Estimating population means and group-conditional means from multiple sources with varying costs and biases.
method Develops a sampling plan that maximizes effective sample size, paired with a post-stratification estimator.
result Achieves budgeted minimax optimal risk for estimating population means and group-conditional means.
Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavailable. One important question in unsupervised domain adaptation is how to measure the difference between the source and target domains. A pr…
For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that…
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.
Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions. In this paper, we explore the application of making predictions over multiple data sources of different sizes without disclosing data between the sources. We propose that each data source applies a trans…
FinGPT democratizes financial data for LLMs, enabling innovation.
problem Limited financial text datasets and disparities between general and financial text data.
method Automates collection and curation of real-time financial data from diverse Internet sources, fine-tuning with RLSP and LoRA.
result Democratizes access to financial data for LLMs, enabling innovation.
A novel framework synthesizes treatment data across sites using optimal transport.
problem Estimating treatment effects across different sites with varying conditions.
method Distributional causal inference, Optimal Transport for alignment of control group distributions.
result Synthetic treatment group data aligns with true target distribution under general conditions.
Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these different information sources could bring major benefits to applications such as situation awareness in disaster zones and mapping the spread of…
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in prac…
Clarinet uses complementary labels to train classifiers with less source data.
problem Training classifiers with true-label data from source domain is costly.
method Proposes CLARINET to train classifiers with complementary-label source data and unlabeled target data.
result CLARINET significantly outperforms baselines in unsupervised domain adaptation.