Adaptive source selection for positive transfer in linear models improves target dataset performance.
problem Limited task-specific labeled data in business settings.
method Greedily decides from which sources and how many samples to incorporate into the target dataset using an accept/reject rule based on a data-dependent estimate of the transfer gain.
result Consistent gains over classical and recent strong baselines while avoiding negative transfer.
Paper develops TLoc framework to improve Telco outdoor position recovery.
problem High data collection cost and poor accuracy in Telco outdoor position recovery.
method Transfer learning applied to Telco outdoor position recovery.
result TLoc framework improves accuracy by 27.58% and 26.12% on 2G GSM and 4G LTE MR datasets.
This paper tackles negative transfer in multi-task learning by introducing class-wise weights.
problem Negative transfer hampers function from achieving optimality in multi-task learning.
method Introduces class-wise weights to drive positive transfer and suppress negative transfer.
result Demonstrates improved performance in multi-task learning by reducing negative transfer.
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.
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of ar…
Proves Riemannian positive mass theorem with singularities.
problem Proves Riemannian positive mass theorem for specific types of singular manifolds.
method Uses initial data sets with a second fundamental form to transfer convexity between different singularity components.
result Proves the theorem for manifolds with some mean-concave components and others mean-convex.
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.
The paper analyzes phase transitions in transfer learning for perceptrons.
problem Understanding when transfer learning from a source task to a target task is beneficial.
method Theoretical analysis of a pair of related perceptron learning tasks.
result Reveals a phase transition from negative to positive transfer as task similarity changes.
The paper proves a category of dg manifolds with finite positive amplitude.
problem Understanding the structure of dg manifolds with finite positive amplitude.
method Using path spaces and homotopy transfer theorem for curved L∞[1]-algebras. result Proves that dg manifolds of finite positive amplitude form a category of fibrant objects.
We propose to apply deep transfer learning from computer vision to static malware classification. In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection. As a result, training time of deep neural networks is accelerated while high c…
Transfer learning has been proven effective when within-target labeled data is scarce. A lot of works have developed successful algorithms and empirically observed positive transfer effect that improves target generalization error using source knowledge. However, theoretical analysis of transfer learning is more challe…
Study on neural networks' performance in sequential task learning.
problem Understanding the performance of neural networks in sequential task learning.
method Theoretical analysis of generalization performance in continual learning using statistical mechanical analysis of kernel ridge-less regression.
result Characteristic transitions from positive to negative transfer observed in neural networks.
A novel transfer learning framework combines multiple data sources for PU learning.
problem Challenges in PU learning due to lack of negative labels and data scarcity.
method Model averaging of heterogeneous data sources, including binary labeled, semi-supervised, and PU data.
result Method outperforms other methods in predictive accuracy and robustness, especially under limited labeled data.
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
Agent learns to trade currency pairs with improved risk management.
problem Improving systematic FX trading performance with online transfer learning.
method Online inductive transfer learning using feature representation from Gaussian mixture model to a reinforcement learning agent.
result Annualized portfolio information ratio of 0.52, compound return of 9.3%.
Neural networks learn patterns in random data, improving downstream performance.
problem Understanding what deep networks learn with random labels.
method Analytical and empirical study of convolutional and fully connected networks pre-trained on random labels.
result Pre-trained networks on random labels transfer faster to real datasets, despite specialization effects.
Study improves hypothesis transfer learning for functional linear models.
problem Incompatible TL techniques for high-dimensional FLR methods due to infinite-dimensional nature of functional data.
method Proposes two algorithms for hypothesis transfer learning in RKHS framework, leveraging RKHS distance and aggregation techniques.
result Establishes asymptotic lower bounds and matching upper bounds for the proposed algorithms, demonstrating their effectiveness.
Effective multilingual search with instance-based transfer learning.
problem Search in multilingual setting, especially next-sentence prediction and inverse cloze.
method Instance-based transfer learning, analyzing vocabulary overlaps and transitive overlaps.
result Positive transfer on all 35 target languages and two tasks, even with no vocabulary overlap.
New method transfers word embeddings from large to small datasets efficiently.
problem Challenges in learning word embeddings from new domains with limited data.
method Group-sparse matrix factorization for transfer learning.
result Efficiently learns domain-specific word embeddings with less data.
Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data. Learning reliable networks from limited data is difficult, therefore transfer learning can improve the robustness …
Sharing knowledge between tasks is vital for efficient learning in a multi-task setting. However, most research so far has focused on the easier case where knowledge transfer is not harmful, i.e., where knowledge from one task cannot negatively impact the performance on another task. In contrast, we present an approach…
CLS measures dataset similarity through decision rule performance.
problem Measuring dataset similarity in machine learning, especially for transfer learning and domain adaptation.
method Cross-Learning Score (CLS) measures similarity through bidirectional generalization performance of decision rules, linking to cosine similarity under canonical linear models.
result CLS effectively measures dataset similarity and transferability, validated on synthetic and real-world datasets.
Distilled teacher model transfers knowledge to student model on new datasets.
problem Improving model quality using unlabeled data.
method Knowledge distillation with an unlabeled teacher model.
result Teacher model knowledge transfers to student model on out-of-distribution datasets.
Paper introduces PTL-SI for statistical inference in TL-HDR, controlling FPR.
problem Quantifying statistical significance in TL-HDR with limited data.
method PTL-SI framework for valid p-values in TL-HDR feature selection. result Valid p-values and controlled FPR in TL-HDR feature selection. Automatic post-disaster damage detection using aerial imagery is crucial for quick assessment of damage caused by disaster and development of a recovery plan. The main problem preventing us from creating an applicable model in practice is that damaged (positive) examples we are trying to detect are much harder to obtai…
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements. We introduce Regularized Hierarchical Policy Optimization (RHPO) to improve data-efficiency for domains with multiple dominant tasks and ultimately reduce require…
Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain which relaxes the fully shared label space assumption across different domains. In this more general and practical scenario, a major challenge is how to select source instances in the shared classes acro…
The study shows how modular learning can adapt to new tasks.
problem Adapting to new tasks in an ever-changing environment.
method Task segmentation, modular learning, memory-based ensembling.
result The system demonstrates robustness to catastrophic forgetting and increasing positive transfer.
The family of admissible positions in a transaction costs model is a random closed set, which is convex in case of proportional transaction costs. However, the convexity fails, e.g. in case of fixed transaction costs or when only a finite number of transfers are possible. The paper presents an approach to measure risks…
Two insurance companies collaborate to maximize the probability of none going bankrupt.
problem Maximizing the probability of no company bankruptcy in a correlated Brownian motion model.
method Analyzing optimal strategies and deriving explicit formulas for minimal ruin probability.
result Maximizing collaboration benefits when Brownian motions are positively correlated.
A new framework for federated continual learning reduces interference and improves performance.
problem Learning from a sequence of tasks with limited data from each client.
method Federated Weighted Inter-client Transfer (FedWeIT) framework.
result FedWeIT significantly outperforms existing methods with reduced communication cost.
This paper develops geometric tools for causal inference using information flow concepts.
problem Developing a geometric interpretation of causal inference from probabilistic measures.
method Introducing a new measure, GeoC_{y
ightarrow x}, based on fractal correlation dimension.
result Avoids boundedness issues in transfer entropy, providing a more robust measure of causal inference.
Improves classifier performance in multi-stage selection processes.
problem Difficulty in training classifiers in multi-stage selection processes due to varying sample sizes and information.
method Multi-Stage Transfer Learning (MSGTL) approach that uses knowledge from simpler classifiers trained in early stages to improve later stages.
result MSGTL outperforms other transfer learning methods in real-world selection process data.
The paper analyzes how combining samples from two tasks can improve performance, especially in high dimensions.
problem Understanding when combining samples from two related tasks outperforms learning with one task alone.
method Applying random matrix theory to high-dimensional linear regression, focusing on proportional sample size increases.
result Precise high-dimensional asymptotics for bias and variance of HPS estimator, showing phase transitions in transfer performance.
Abstract MDPs enable strategic exploration and fast reward transfer in complex environments.
problem Challenging to learn accurate MDPs for high-dimensional states.
method Learn an abstract MDP over low-dimensional coarse states, using an abstraction function.
result Achieves superhuman performance on Pitfall! and higher reward with fewer samples.
Data is one of the essential ingredients to power deep learning research. Small datasets, especially specific to medical institutes, bring challenges to deep learning training stage. This work aims to develop a practical deep multimodal that can classify patients into abnormal and normal categories accurately as well a…
Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between prot…
XL-Editor improves sentence post-editing using XLNet's variable-length insertion probability.
problem Post-editing sentences to refine generated text.
method XL-Editor trains XLNet to estimate variable-length insertion probabilities and apply post-editing operations.
result XL-Editor outperforms XLNet on text insertion and deletion tasks, and achieves significant style transfer improvements.
Transfer learning improves portfolio optimization by identifying transfer risk.
problem Financial portfolio optimization problem.
method Introduces transfer risk concept within transfer learning framework.
result Transfer risk is a significant indicator of transferability and enhances portfolio management efficiency.
Deep learning model detects and flags artefacts in polarimetric images.
problem Artifacts in polarimetric images contaminate areas of interest.
method Convolutional Neural Network (CNN) for automatic artefact detection.
result Model achieves 98% true positive and 97% true negative rates.
Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators a…
Paper analyzes transfer risk in transfer learning for finance.
problem Evaluate transferability of transfer learning in finance.
method Proposes transfer risk concept and applies to stock return prediction and portfolio optimization.
result Transfer risk correlates with transfer learning performance and identifies appropriate source tasks.
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, …
Meta-learning framework for few-shot one-class classification using order-equivariant networks.
problem Few labeled examples for positive class in one-class classification tasks.
method Order-equivariant networks for meta-learning a binary classifier conditioned on positive examples.
result Meta-learning framework outperforms baselines on unseen synthetic streams.
Mathematical framework for transfer learning feasibility and transfer risk.
problem Theoretical analysis of transfer learning.
method Reformulated transfer learning as an optimization problem, introduced transfer risk concept.
result Demonstrated the potential and benefits of incorporating transfer risk in transfer learning evaluation.
Generative Adversarial Network purifies images from steganography without degrading quality.
problem Destruction of image steganography while maintaining visual quality.
method Generative Adversarial Network (GAN) optimized for steganography destruction.
result High rate of steganographic content destruction with minimal visual quality degradation.
Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To dis…
Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora p…