Enhances DQN for subtasks with mixed transfer effects.
problem Improving sample efficiency in reinforcement learning with mixed transfer effects.
method Combining options framework with deep Q-networks (DQNs) through different 'option heads' and a supervisory network.
result Augmented DQN shows lower sample complexity in subtasks with negative transfer.
Proposes Gaussian optimal transport for image style transfer.
problem Image style transfer and mixing different artistic styles.
method Encoder/Decoder framework with optimal transport for Gaussian measures.
result Simple methodology for generating stylized content interpolating between many styles.
The paper proves exponential mixing for hyperbolic manifolds, with applications to geodesic holonomy.
problem Establishing exponential mixing for frame flows on hyperbolic manifolds.
method Using spectral bounds on transfer operators twisted by holonomy, building on Dolgopyat's method.
result Exponential mixing of frame flows for convex cocompact hyperbolic manifolds.
AMEAN tackles BTDA by learning meta-sub-targets to bridge domain gaps and misalignments.
problem Blending-target Domain Adaptation (BTDA) with multiple sub-targets that are hard to distinguish.
method AMEAN uses two adversarial processes: first to align source and mixed target domains, second to learn meta-sub-targets.
result AMEAN significantly outperforms existing DA algorithms in BTDA scenarios.
This study compares transfer learning and multi-agent learning for AI-driven traffic agents.
problem Improving traffic flow in mixed-intelligence highway scenarios.
method Online MIT DeepTraffic simulation, deep reinforcement learning, elitist evolutionary algorithm, hyperparameter search, transfer learning, multi-agent learning.
result Transfer learning and multi-agent learning yield different average speeds for AI-driven traffic agents.
Study improves Bayesian optimisation with ensemble transfer learning.
problem Improving sample efficiency in Bayesian optimisation of expensive functions.
method Empirical analysis of ensemble-based transfer learning methods and pipeline components.
result Two components (warm start initialisation and positive weight constraint) improve transfer learning Bayesian optimisation performance.
This paper proves exponential mixing for frame flows on hyperbolic manifolds with cusps.
problem Establishing exponential mixing for frame flows on geometrically finite hyperbolic manifolds with cusps.
method Symbolic coding of geodesic flow, Dolgopyat's method, large deviation property, combinatorics of cusp excursions, renewal theorem.
result Frame flows for geometrically finite hyperbolic manifolds of arbitrary dimensions are exponentially mixing.
Transfer learning improves model accuracy on sparse materials datasets.
problem Irreducible errors in analyses due to differing measurements across datasets.
method Three transfer learning techniques: multi-task, difference, and explicit latent variable architectures.
result Explicit latent variable method is most accurate for activation energies of NO reduction steps.
Mix and match networks enable image translation without paired data.
problem Image translation between domains or modalities with no direct paired data.
method Multiple encoders and decoders aligned for test-time composition.
result Model outperforms baselines in cross-modal image translation.
HOUDINI learns algorithms across domains using program synthesis.
problem Lifelong learning of algorithmic tasks mixing perception and reasoning.
method Combining gradient descent with combinatorial search over programs.
result HOUDINI transfers high-level concepts more effectively than traditional methods.
A new unsupervised contrastive learning framework improves time series representation learning.
problem Lack of labeled data in time series data.
method Proposes an unsupervised contrastive learning framework using a novel contrastive loss and data augmentation.
result Framework outperforms other approaches on univariate and multivariate time series, and benefits transfer learning.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
problem Difficulty in comparing surgical outcomes due to racial/ethnic and geographic differences.
method Causal inference framework and transfer learning to incorporate data from multiple populations.
result Racial and ethnic differences in surgical outcomes are found, with non-Hispanic Black patients experiencing wide variability.
End-to-end analysis of SGD for STL with adaptive sub-sampling.
problem Designing SGD for STL with statistical guarantees without prior knowledge of source quality.
method Mixed-sample SGD procedure that alternates between source and target data, maintaining transfer guarantees.
result Mixed-sample SGD converges to a target-adaptive solution with 1 / T 1/\sqrt{T} 1/ T rate. Study proves projective Anosov subgroups lead to mixing flows in specific spaces.
problem Understanding mixing properties of flows on specific geometric spaces.
method Constructing non-empty domain of discontinuity in homogeneous space, using spectral estimates for transfer operators.
result Exponential mixing, spectral gap, and meromorphic continuation of zeta functions established.
Study reveals a link between Ruelle-Pollicott resonances and cohomology eigenvalues for Anosov diffeomorphisms.
problem Understanding the speed of mixing in Anosov diffeomorphisms.
method Investigates Ruelle-Pollicott resonances on manifolds of any dimension, connecting them to cohomology eigenvalues of a quasi-compact transfer operator.
result Established a cohomological bound for the speed of mixing of Anosov diffeomorphisms.
Model for inferring multivariate functions from areal data.
problem Inferring multivariate functions from areal data with varying granularities.
method Probabilistic model using Gaussian processes with spatial aggregation.
result Model effectively estimates spatial correlations and dependencies between areal data sets.
Mixed finite element methods solve a PDE using two or more variables. The theory of Discrete Exterior Calculus explains why the degrees of freedom associated to the different variables should be stored on both primal and dual domain meshes with a discrete Hodge star used to transfer information between the meshes. We s…
Proposes a model for multi-agent reinforcement learning with hierarchical graph attention network.
problem Limited transferability of trained policies to new multi-agent tasks.
method Uses hierarchical graph attention network for representation learning and multi-agent actor-critic for policy learning.
result Demonstrates superior performance in mixed cooperative and competitive tasks compared to existing methods.
Uniform mixing proved for hyperbolic manifolds using congruence covers.
problem Proving uniform exponential mixing for hyperbolic manifolds.
method Using Dolgopyat's method and spectral bounds for congruence transfer operators.
result Uniform exponential mixing for congruence covers of hyperbolic manifolds.
Paper proposes RL for efficient dispatching in dynamic manufacturing environments.
problem Efficient dispatching in dynamic, stochastic manufacturing environments.
method Reinforcement learning (RL) with policy transfer for dynamic shop floor settings.
result Proposed RL approach outperforms other methods in terms of total discounted reward and average lateness, tardiness.
Study on Transfer Elastic Net error bounds and grouping effect.
problem Estimation error and grouping effect in Transfer Elastic Net.
method Derives non-asymptotic error bound and examines grouping effect scenarios.
result Effective error bounds and grouping effect observed in Transfer Elastic Net.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
problem Flexible and composable nonlinear mixed-effects modeling
method Macro-based modeling language and unified interface
result Substantially expand the range of nonlinear mixed-effects models
metabeta uses neural networks to speed up Bayesian mixed-effects regression.
problem Bayesian mixed-effects regression is computationally expensive.
method metabeta is a neural network model that pre-trains to estimate posterior distributions.
result metabeta achieves comparable performance to MCMC at a fraction of the time.
This work transfers causal knowledge between tasks for Individual Treatment Effect estimation.
problem Estimating Individual Treatment Effects (ITE) requires a large amount of data, making it challenging.
method The authors introduce a practical framework for efficient transfer of causal knowledge between tasks, using a Causal Inference Task Affinity (CITA) measure.
result ITE knowledge transfer can significantly reduce the amount of data needed for ITE estimation.
Study compares neural and statistical models for Parkinson's disease progression from voice data.
problem Difficult statistical analysis of longitudinal voice biomarkers due to subject correlation, small cohorts, and varied disease trajectories.
method Evaluated Neural Mixed Effects (NME), Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs).
result GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure.
The study uses Hidden Markov Models to analyze student enrollment patterns and academic performance.
problem Limited understanding of how enrollment patterns affect academic performance.
method Applied Hidden Markov Models to categorize enrollment strategies and compare academic outcomes.
result Mixed enrollment strategies lead to better academic performance, especially during part-time semesters.
New Krylov subspace methods speed up mixed-effects models with crossed random effects.
problem Slow computations for high-dimensional crossed random effects in mixed-effects models.
method Krylov subspace-based methods for generalized mixed-effects models with cross effects.
result Speedups by factors of up to 10,000 in computations for mixed-effects models.
GBMixed boosts mixed models for clustered data, estimating mean and variance flexibly.
problem Flexible estimation of mean and variance components in clustered data.
method Gradient Boosting framework for linear mixed models with likelihood-based gradients.
result GBMixed accurately recovers complex nonlinear fixed effects and covariances.
A new method combines machine learning with mixed-effects models for better repeated measurement analysis.
problem Inference of linear coefficients in partially linear mixed-effects models with complex interactions and high-dimensional variables.
method Double machine learning approach to estimate nonparametrically nonlinear variables, then use standard linear mixed-effects techniques to estimate the linear coefficient.
result The estimated fixed effects coefficient converges at the parametric rate and is semiparametrically efficient.
New algorithms use transfer learning to estimate treatment effects efficiently.
problem Estimating heterogeneous treatment effects using neural networks.
method Combining transfer learning with causal inference insights.
result Methods perform significantly better than existing benchmarks.
This paper explores the connection between adversarial and knowledge transferability.
problem Understanding the factors affecting knowledge transferability.
method Theoretical analysis and practical metrics for adversarial transferability.
result Adversarial transferability and knowledge transferability are closely related.
Theoretical guarantees for transfer learning improve target generalization.
problem Transfer learning effectiveness with limited within-target labeled data.
method Theoretical analysis using model complexity and learning algorithm stability.
result New generalization bound for multi-source transfer learning.
Study discovers rules linking patient symptoms to unplanned ICU transfers.
problem Identifying patients at risk for unplanned ICU transfers.
method Mixed-integer optimization approach to learn association rules.
result Significant rules discovered for each patient subgroup.
DGCNN improves graph CNNs by handling irregular graphs.
problem Handling structural information loss and redundancy in graph CNNs.
method Proposes DGCNN using DGCL with mixed Gaussian model to handle irregular graphs.
result DGCNN outperforms state-of-the-art methods in graph classification and retrieval.
Accurate Bayesian analysis for mixed-effects studies.
problem Statistical significance and inter-individual differences in group studies.
method Variational Bayesian approach for hierarchical mixed-effects models.
result Accurate assessment of group-level effects and inter-individual differences.
A scalable Bayesian inference method for mixed-effects models in systems biology.
problem Scalable Bayesian inference for complex hierarchical mixed-effects models in systems biology.
method Constructing amortized approximations of likelihood and posterior distributions, refined for each individual dataset.
result Our method is both fast and competitive in statistical accuracy compared to exact pseudomarginal Bayesian inference.
Develops a test for linear mixed models' fixed effects in high dimensions.
problem Understanding unobserved heterogeneity in high-dimensional models.
method Robust matching moment construction for adaptive sparse estimators of fixed effects.
result Consistent and unbiased test for high-dimensional models with constant cluster-level heterogeneity.
Bayesian Tweedie mixed models are improved with adversarial variational inference.
problem Intractable likelihood function and hierarchical structure of mixed effects.
method Adversarial variational inference with reparameterization and flexible hyper prior.
result Proposed method reduces estimation bias and achieves state-of-the-art predictive performance.
We link disjoint longitudinal data for rare disease patients using latent representations and mixed-effects regression.
problem Analyzing treatment switches in rare diseases with limited data and changing measurement instruments.
method We embed item values into a shared latent space using variational autoencoders and apply mixed-effects regression to quantify treatment effects.
result Our approach allows for statistical inference and quantifies the impact of treatment switches in spinal muscular atrophy.
Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.
problem Modeling aggregate data with varying granularities and spatial supports.
method Multi-output Gaussian process (MoGP) with linear mixing of independent latent GPs, aggregation process, and prior distribution of mixing weights.
result Proposed model outperforms in refining coarse-grained aggregate data.
New attention mechanism improves meta-transfer learning in dynamic tasks.
problem Underfitting in meta-transfer learning with dynamic tasks.
method Proposed Recurrent Memory Reconstruction (RMR) attention mechanism.
result ASNP-RMR significantly outperforms baselines in various tasks.
MC-GMENN improves neural networks for clustered data using Monte Carlo methods.
problem Improving neural network performance on clustered data with correlations.
method MC-GMENN employs Monte Carlo methods to train generalized mixed effects neural networks.
result MC-GMENN outperforms existing models in generalization and quantifying inter-cluster variance.
A new MARL framework for community-based cooperation with transfer and active exploration.
problem Flexible coordination patterns in multi-agent systems with community structures.
method Community-based multi-agent reinforcement learning with transfer and active exploration.
result Provably convergent actor-critic algorithms for structured information sharing and transfer learning.
Transfer learning improves model robustness against adversarial attacks.
problem Understanding how transfer learning affects model robustness against adversarial attacks.
method Extensive empirical evaluations of white-box and black-box attacks on fine-tuned transfer learning models.
result Adversarial examples are more transferable when fine-tuning is used than when networks are trained independently.
Study uses deep learning to predict mycotoxin levels in Irish oats.
problem Predicting mycotoxin contamination in Irish oats to improve crop quality and safety.
method Investigated neural networks and transfer learning models for multi-response prediction.
result Transfer learning model TabPFN provided the best performance.
The paper uses transfer stacking to improve tropical cyclone intensity prediction.
problem Challenging tropical cyclone intensity prediction due to climate changes.
method Transfer stacking and conventional neural networks for improving prediction performance.
result Transfer stacking enhances generalization in predicting tropical cyclone intensity.
Develops methods for statistical inference in high-dimensional linear mixed models.
problem Statistical inference for high-dimensional linear mixed models with large fixed effects and small random effects.
method Inspired by de-biasing penalized estimators, corrects a `naive' ridge estimator to build asymptotically valid confidence intervals.
result Demonstrates that the proposed method outperforms those that ignore correlation induced by random effects.
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.