This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning. We develop the mathematical foundation for the appr…
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48 results for “selective knowledge sharing”
Improves domain classification across multiple locales with shared language.
problem Improves domain classification accuracy in Spoken Language Understanding across multiple locales with shared language.
method Selective multi-task learning to create a joint representation of utterances over locales with different sets of domains.
result The proposed approach outperforms other baselines models especially when classifying locale-specific domains and low-resourced domains.
DARL framework tackles partial domain adaptation by selecting source instances for positive transfer.
problem Tackles the challenge of selecting source instances for positive transfer in partial domain adaptation.
method Proposes a Domain Adversarial Reinforcement Learning (DARL) framework that uses deep Q-learning and domain adversarial learning to select source instances and learn domain-invariant features.
result Demonstrates superior performance over existing methods for partial domain adaptation on several benchmark datasets.
Bayesian neural networks improve knowledge sharing across networks.
problem Improving knowledge sharing between neural networks.
method Application of Bayesian interpretations to neural networks for feature sharing.
result Enhanced feature sharing across networks.
AIBC uses AI and blockchain for efficient resource sharing.
problem Efficient resource sharing in large-scale decentralized systems.
method Two-consensus scheme with DABFT and DPoEV.
result Predictive deep learning for optimal BFT algorithm selection.
L2SC improves spectral clustering performance by selectively transferring knowledge across tasks.
problem L2SC tackles the challenge of incorporating new spectral clustering tasks without relearning all previous tasks.
method L2SC uses an orthogonal basis library and feature embedding library to selectively transfer knowledge from previously learned tasks to new tasks.
result L2SC outperforms state-of-the-art spectral clustering algorithms on real-world benchmark datasets.
Enhances generative model accuracy through knowledge transfer.
problem Improving generative model precision across different tasks.
method Introduces a novel framework for transfer learning using shared structures.
result Demonstrates enhanced performance in diffusion and normalizing flows models.
Novel framework for data sharing and coordinated exploration in concurrent RL with non-identical environments.
problem Learning more data-efficient and better policies in concurrent RL with non-identical environments.
method Proposes a novel algorithmic framework that leverages causal inference via ANM-MM to extract model parameters and a new data sharing scheme based on similarity measures.
result Demonstrates superior learning speeds on various tasks and effectiveness of diverse action selection.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
problem Representing clinical concepts and patients in electronic health records
method Spectral-based unsupervised learning with flexible knowledge transfer
result Outperforms competing approaches in challenging scenarios
This paper tackles federated learning for automatic latent variable selection in multi-output Gaussian processes.
problem Challenges in determining the adequate number of latent processes and relying on centralized learning for privacy and computational issues.
method Proposes a hierarchical model with spike-and-slab priors for automatic latent process selection and variational inference-based federated learning algorithm.
result Demonstrates the advantageous features of the proposed federated approach through simulations and real-world data.
AlphaNet improves supernets training with alpha-divergence.
problem Improving the uncertainty distillation in weight-sharing NAS.
method Proposes alpha-divergence for better uncertainty distillation in supernets.
result Significant improvements in model performance across various FLOPs regimes.
Paper tackles robust knowledge transfer in parallel RL tasks.
problem Transfer knowledge from low-tier to high-tier tasks in parallel RL without shared dynamics or reward functions.
method Identifies Optimal Value Dominance condition and proposes online learning algorithms for both tasks.
result Achieves constant regret on partial states and near-optimal regret when tasks are dissimilar.
Paper extends LfC to distributed learning with asynchronous optimization.
problem Learning from constraints in a distributed, nonconvex setting.
method Applying asynchronous Method of Multipliers to Learning from Constraints.
result Demonstrates privacy-preserving distributed learning with local constraints.
RTNet uses both deep and pixel-level features for partial domain adaptation.
problem Selecting relevant source samples for knowledge transfer in partial domain adaptation.
method Reinforced Transfer Network (RTNet) with a reinforced data selector (RDS) and domain adaptation model.
result RTNet achieves state-of-the-art performance on benchmark datasets.
Boosts share routing for multi-task learning with flexible sparse connections.
problem Designing suitable sharing mechanisms among multiple tasks in multi-task learning.
method Proposes MTNAS framework to modularize sharing into sub-networks with sparse connections and gating.
result Demonstrates consistent improvement over single-task and typical multi-task methods while maintaining efficiency.
SEEK algorithm selects minimal state in reinforcement learning for better policy learning.
problem Challenges in obtaining a state representation that is parsimonious and satisfies the Markov property.
method SEEK algorithm estimates the minimal sufficient state in reinforcement learning.
result The SEEK algorithm achieves selection consistency in large samples.
Proposes a transfer learning method for PCA studies.
problem Enhancing PCA estimation accuracy across multiple studies.
method Two-step algorithm integrating shared subspace information via Grassmannian barycenter.
result Knowledge transfer improves PCA estimation accuracy through enlarged eigenvalue gap.
Training-free source selection for LLM families with shared vocabularies
problem Source selection for LLM families with shared vocabularies
method Fisher alignment at vocabulary scale
result Fisher alignment is a cosine between kernel mean embeddings in the joint activation-error space
Bayesian model transfers knowledge across different engineering fleets.
problem Data sparsity in predictive models for engineering infrastructure.
method Hierarchical Bayesian approach with multitask learning.
result Improves survival analysis and power prediction in truck fleets and wind farms.
We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systemscs.LG
New architecture separates object state and behavior for better game dynamics.
problem Lack of systematicity in black-box models for dynamic environments.
method Factorizes declarative and procedural knowledge into object files and schemata.
result Improves generalization on environments with multiple object tokens.
AFS-BM improves model accuracy by dynamically selecting features.
problem Feature selection challenges in ML, especially scalability and adaptability.
method Joint optimization for feature selection and model training with binary masking.
result AFS-BM achieves significant improvements in model accuracy and computational efficiency.
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
problem Domain adaptation challenges in heterogeneous networks with shared and private node types.
method Generalized Domain Adaptive model across HINs (GDA-HIN) that aligns identical-type nodes and edges while utilizing different-type nodes and edges.
result GDA-HIN outperforms state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.
L2F learns to forget, improving few-shot learning performance.
problem Few-shot learning challenges in adapting to unseen tasks.
method Task-and-layer-wise attenuation on compromised initialization to reduce prior knowledge influence.
result Faster adaptation and improved performance demonstrated experimentally.
This paper proposes a new VoI analysis framework for complex decision problems.
problem Optimizing resource allocation for information collection in decision-making under uncertainty.
method Surrogate-based framework for Value of Information analysis, integrating knowledge sharing and adaptive training.
result Accurate and robust estimates of VoI with fewer model evaluations compared to state-of-the-art methods.
A new algorithm FS-PeSOA extracts features from large datasets.
problem Extracting useful knowledge from large datasets.
method Adaptive Penguin Search Optimization Algorithm (FS-PeSOA) based on penguin hunting strategy.
result FS-PeSOA finds global optimum solution for feature subset selection.
Paper proposes mimic learning to share intrusion detection models without private data.
problem Difficulty in obtaining labelled training data for intrusion detection models due to privacy concerns.
method Use of mimic learning to transfer knowledge from a teacher model trained on private data to a student model.
result Student model mimics teacher model without access to private data.
This paper reviews feature selection in KGs for improved ML model performance.
problem Improving feature selection in KGs for better machine learning model efficacy.
method Comprehensive review of feature selection methodologies in KGs.
result Advancement in scalability, accuracy, and interpretability of feature selection techniques.
This paper improves model selection with cross-validation using domain knowledge.
problem Improving model selection with cross-validation risk estimation.
method Establishes distribution-free deviation bounds using VC dimension, formalizes Learning Spaces based on domain knowledge.
result Enhanced generalization through selection of candidate models based on domain knowledge.
A framework isolates and learns approximately shared features for better domain adaptation.
problem Reducing performance degradation in unseen domains using machine learning models.
method Statistical framework distinguishing feature utilities based on correlation variance across domains. Learning approximately shared features from source tasks and fine-tuning on target tasks.
result Improved population risk compared to previous results on both source and target tasks, resolving the paradox of feature selection.
Unified Bayesian framework for clustered federated learning improves model performance.
problem Handling non-IID client data in federated learning.
method A unified Bayesian framework for clustered federated learning that associates clients to clusters and proposes practical algorithms for data associations.
result The proposed framework increases model performance by circumventing the need for unique client-cluster associations.
Proposes a method to integrate learner models robustly against misspecifications.
problem Misspecifications in learner models and parameter sharing patterns degrade prediction accuracy.
method Sequentially incorporates additional learners based on user-specified parameter sharing patterns.
result Data-adaptively selects the most suitable way of parameter sharing to enhance predictive performance.
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasksstat.ML
This paper shows how diverse tasks can make inefficient exploration in MTRL efficient.
problem The challenge of efficient exploration in Multitask Reinforcement Learning.
method A generic policy-sharing algorithm with myopic exploration design trained on diverse tasks.
result A generic policy-sharing algorithm with myopic exploration design can be sample-efficient in MTRL.
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propose a novel algorithm for unsupervised representation learning from piece-wise stationary visual data: Variational Autoencoder with Shared Emb…
The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. These models represent each data view via shared and individual components, and have been successfully applied for exploratory dimension reduct…
The study extracts market direction from transaction data.
problem Extracting market direction from transaction data.
method Dynamic equation with time scale selection from past transactions.
result Automatic determination of time scale for price calculation.
This paper proposes a method to train multiple neural networks with shared parameters using a reconstruction loss.
problem Training multiple neural networks for correlated tasks separately is inefficient.
method Introduces a novel approach with a reconstruction loss to encourage shared features across multiple tasks.
result The proposed method achieves efficient transfer learning with competitive performance.
The paper analyzes how shared priors affect Bayesian data fusion performance.
problem Effect of shared priors on Bayesian data fusion performance.
method Theoretical analysis using two divergences common in Bayesian inference.
result Theoretical analysis and experimental validation of performance behavior.
SPEQ improves quantized neural networks by stochastic precision sharing and cosine similarity loss.
problem Improving quantized deep neural networks for edge devices.
method SPEQ combines stochastic precision sharing and cosine similarity loss for knowledge distillation.
result SPEQ outperforms existing methods in various tasks.
Unified multitask learning framework for mixed-type outcomes.
problem Difficulty in formulating a unified objective for tasks with different outcomes.
method Multitask transformation framework with shared sparsity, using deep neural networks and rank-based optimization.
result Improved prediction and variable selection across continuous, binary, and mixed outcomes.
The paper optimizes private data sharing by selecting statistics and using MCMC for Bayesian inference.
problem Optimizing private data sharing by selecting statistics and performing Bayesian inference.
method Promotes Fisher information for statistic selection and proposes MCMC algorithms for inference.
result The Fisher information of the privatized statistic predicts the relative performance of the statistic in Bayesian estimation.
Deep RL solves combinatorial selection problems with large item spaces.
problem Solving MDPs with large state and action spaces, especially for combinatorial selection.
method Convert S-MDP to IS-MDP, use weight-shared Q-networks to manage state space explosion.
result Our approach effectively handles large item spaces and scales to diverse environments.
Unsupervised domain adaptation (UDA) aims to learn the unlabeled target domain by transferring the knowledge of the labeled source domain. To date, most of the existing works focus on the scenario of one source domain and one target domain (1S1T), and just a few works concern the scenario of multiple source domains and…
Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…
A new method for multi-task learning by allocating parameters.
problem Sharing parameters between unrelated tasks can hurt performance.
method Learned binary variables to allocate components to tasks, encouraging sharing between related tasks.
result Achieves a 17% relative reduction of the error rate on Omniglot benchmark.
LLM-Lasso uses LLMs to improve feature selection in Lasso regression.
problem Improving feature selection in Lasso regression with domain-specific knowledge.
method Combines LLMs with Lasso regularization to generate feature weights.
result Outperforms standard Lasso and feature selection baselines in biomedical studies.
We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a …
Paper proposes MMVFL for multi-class VFL with multiple participants.
problem Privacy-preserving multi-class VFL with multiple participants.
method Extends multi-view learning to enable label sharing among multiple VFL participants.
result MMVFL effectively shares label information among multiple VFL participants and matches multi-class classification performance.