Proposes MEDM to balance entropy minimization and diversity maximization for better domain adaptation.
problem Trivial solutions in entropy minimization for unsupervised domain adaptation.
method Introduces diversity maximization to balance with entropy minimization, controlled by deep embedded validation.
result MEDM outperforms state-of-the-art methods on four domain adaptation datasets.
New method improves text classification without labeled target data.
problem Improving text classification under domain shift without labeled target data.
method Diversity-based generalization using multi-head attention with diversity constraints.
result Method matches state-of-the-art performance without labeled target data.
Proposes a method to generate diverse translations by conditioning on target domain.
problem NMT models lack diversity in translations, even with search algorithms.
method Condition the decoder on a latent variable representing target domain, generated by a target encoder.
result Generated diverse translations without affecting performance or training time.
Proposes Vendi Score for evaluating diversity in ML models.
problem Lack of flexible diversity evaluation metrics in ML.
method Integrates ecological and quantum statistical mechanics concepts to define Vendi Score.
result Vendi Score enables flexible diversity evaluation without requiring a reference dataset.
SETrLUSI combines diverse knowledge from multiple domains for faster convergence.
problem Handling diverse knowledge from multiple domains in transfer learning.
method Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant (SETrLUSI).
result SETrLUSI accelerates convergence and outperforms related methods.
DIVA generates diverse tasks for complex simulators, enabling adaptive agent training.
problem Lack of diverse training data for complex, open-ended simulators.
method Evolutionary approach using domain randomization and procedural generation.
result Successfully trains adaptive agent behavior in complex simulators.
This paper improves model generalization by integrating diverse pretrained models.
problem Leveraging diverse pretrained models for robust out-of-distribution generalization.
method Characterize and integrate diverse pretrained models based on diversity and correlation shifts.
result Demonstrates state-of-the-art out-of-distribution generalization performance.
Neural Laplace models diverse DEs in the Laplace domain for better dynamics.
problem Inadequate ODEs for long-range dependencies and discontinuities.
method Unified framework in Laplace domain, using stereographic map for smoothness.
result Superior performance in diverse DEs, including complex history dependency and abrupt changes.
New metric evaluates generative models across domains, diagnosing fidelity, diversity, and generalization.
problem Evaluating generative models in diverse domains with limited metrics.
method Introduces a 3D evaluation metric (α-Precision, β-Recall, Authenticity) for domain-agnostic diagnostics. result Unified metric characterizes fidelity, diversity, and generalization, diagnosing model performance.
SKADA-bench evaluates unsupervised DA methods across diverse modalities.
problem Evaluating unsupervised DA methods on diverse modalities with realistic validation.
method Nested cross-validation and unsupervised model selection scores.
result Highlights the importance of realistic validation and provides practical guidance.
As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? To approach this question, deep multi-task learning is extended in this paper to the setting where there is no obvious overlap between task a…
New algorithm enhances generative modeling for bounded domains.
problem Ad-hoc thresholding techniques for boundary enforcement in diffusion models.
method Reflected Schrödinger Bridge algorithm for entropy-regularized optimal transport.
result Generative modeling in diverse bounded domains with optimal transport properties.
CMA-ME combines CMA-ES and MAP-Elites for better quality and diversity in continuous domains.
problem Finding a diverse set of high-quality solutions in complex continuous domains.
method Combines CMA-ES self-adaptation with MAP-Elites archiving and mapping.
result CMA-ME outperforms MAP-Elites in both quality and diversity of solutions.
Adversarial attacks on deep neural networks traditionally rely on a constrained optimization paradigm, where an optimization procedure is used to obtain a single adversarial perturbation for a given input example. In this work we frame the problem as learning a distribution of adversarial perturbations, enabling us to …
URT layer improves few-shot image classification across diverse domains.
problem Few-shot image classification in multi-domain settings.
method Meta-learns to dynamically re-weight and compose domain-specific representations.
result Sets new state-of-the-art on Meta-Dataset.
Automates neural network design for diverse tasks.
problem Designing neural networks for new, under-explored domains.
method Introduces XD-Operations and a weight-sharing scheme to transform standard backbones into search spaces of operations.
result Models using XD-Operations achieve lower error than baseline and expert-designed networks on diverse tasks.
A method to improve image synthesis diversity using mutual information.
problem Mode collapse in conditional GANs for multimodal image synthesis.
method Explicitly estimate and maximize mutual information between latent code and output image.
result Prevents mode collapse and encourages synthesis of diverse images.
DreamerV3 learns diverse tasks with a single configuration.
problem Generalizing reinforcement learning across multiple domains.
method World model and farsighted strategies.
result Outperforms specialized methods across 150 diverse tasks.
Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain. We propose a simple but effective way for few-shot classification in which a task di…
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.
The structure of complex networks has been of interest in many scientific and engineering disciplines over the decades. A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and …
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction. Final goals are specified to our agent via images o…
Deep architecture learns transferable features for robust speech emotion recognition.
problem Robust and discriminative features for diverse speech emotion domains.
method Jointly uses CNN for domain-shared features and LSTM for domain-specific emotion classification.
result Transferable features provide gains up to 18.4% in speech emotion recognition.
Bayesian nonparametrics adapt model complexity to diverse datasets.
problem Complex challenges across statistics, computer science, and engineering.
method Flexible Bayesian nonparametric models that adapt model complexity.
result Bayesian nonparametrics offer innovative solutions to multi-object tracking.
A new framework pretrains a single GNN model for diverse graphs, overcoming domain-specific challenges.
problem Difficulty in generalizing across graphs from different domains using existing GNNs.
method Cross-domain pretraining framework with gating functions to choose experts for new graphs.
result Superior performance on link prediction and node classification tasks across various domains.
Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it a…
This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for ge…
Meta-learning improves few-shot land cover classification across diverse regions.
problem Capturing diversity in land cover classification across different geographic regions.
method Model-agnostic meta-learning (MAML) algorithm applied to classification and segmentation tasks.
result Few-shot model adaptation outperforms traditional methods in diverse land cover classification tasks.
DDN dynamically combines weights for domain-specific models, improving performance.
problem Limited compute resources for edge devices.
method Domain-aware Dynamic Network (DDN) that combines specialized weights based on input domain.
result DDN achieves up to 2.6% higher AP50 than a static network on the BDD100K benchmark.
New framework shows diverse training data improves subgroup and overall performance.
problem Lack of understanding how diverse training data affects subgroup and overall performance.
method Casts data collection as part of the learning process, analyzes dataset compositions, and guides dataset design.
result Diverse representation in training data improves subgroup and overall performance.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
FinCast is a foundation model for financial time-series forecasting that outperforms existing methods.
problem Challenges in financial time-series forecasting due to temporal non-stationarity, multi-domain diversity, and varying temporal resolutions.
method FinCast is a foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets.
result FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning.
ATHENA builds flexible defenses against adversarial attacks.
problem Extensive research on adversarial attacks is domain-specific and cannot be easily extended.
method Designing an extensible framework based on diverse weak defenses.
result Comprehensive empirical study demonstrates the effectiveness of ATHENA.
TSFMs improve financial forecasting from diverse datasets.
problem Challenges in forecasting financial time series due to noisy, non-stationary, and heterogeneous data.
method Empirical study of TSFMs in global financial markets, evaluating zero-shot inference, fine-tuning, and pre-training from scratch.
result Pre-trained TSFMs on financial data achieve substantial forecasting and economic improvements, highlighting the value of domain-specific adaptation.
Improves exploration in reinforcement learning with diverse population.
problem Lack of diversity in reinforcement learning agents leads to limited exploration.
method Optimizes a population of agents using volume of behavioral manifold and online learning techniques.
result Improves exploration without sacrificing performance.
New measures quantify diversity of latent representations using metric space magnitude.
problem Evaluating the diversity of latent representations in machine learning models.
method Developed magnitude-based measures for latent representations, stable under data perturbations.
result Demonstrated superior performance across various domains and tasks.
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist. To specifically learn cross-domain sequence representations, we introduce disentangled state space models (DSSM) -- a class of SSM in which domain-invariant state dynamics is explicitly disentangled f…
Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain expert to efficiently explore the space of reasonable solutions. Unfortunately, existing approaches t…
Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. Ho…
CODA simulates future data to generalize models across different datasets.
problem Concept drift in real-world machine learning models.
method CODA framework using a predicted feature correlation matrix to simulate future data.
result CODA effectively achieves temporal domain generalization across different model architectures.
AutoWS-Bench-101 evaluates automated weak supervision methods for diverse domains.
problem Limited applicability of weak supervision due to difficulty in designing labeling functions.
method Automates labeling function design using a small set of ground truth labels.
result AutoWS methods often require foundation models to outperform simple few-shot baselines.
TAROT enhances robustness and domain adaptability with domain-invariant features.
problem Developing models robust to adversarial attacks across diverse domains.
method Derives a new generalization bound and proposes TAROT algorithm.
result TAROT outperforms state-of-the-art methods in accuracy and robustness.
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
problem Efficiently label samples for regression models with limited labeled data.
method Integrates informativeness, representativeness, and diversity in pool-based sequential active learning.
result Demonstrates effectiveness of new ALR approaches on 12 datasets.
Valid certifies LLMs' domain adherence, bounding out-of-domain behavior.
problem Adversarial susceptibility of LLMs to generate out-of-domain outputs.
method VALID approach providing adversarial bounds as a certificate.
result Validates LLMs' domain adherence with meaningful certificates.
To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image label…
Unified approach for multimodal data prediction using synthetic data generation.
problem Challenges in integrating heterogeneous data types for accurate predictive performance.
method Generative Distribution Prediction (GDP) framework that uses multimodal synthetic data generation.
result Empirical validation across four tasks demonstrates versatility and effectiveness of GDP.