A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Equilibrium found for multi-agent trading with transaction costs.
Paper improves bike-sharing demand prediction by adapting to changing patterns.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
Enhances generative model accuracy through knowledge transfer.
Transfer learning aims to faciliate learning tasks in a label-scarce target domain by leveraging knowledge from a related source domain with plenty of labeled data. Often times we may have multiple domains with little or no labeled data as targets waiting to be solved. Most existing efforts tackle target domains separa…
A framework isolates and learns approximately shared features for better domain adaptation.
Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in a…
Study on reliability of latent reuse in diffusion models under distribution shift.
UCB-TQL learns from multiple tasks with shared dynamics and adapts to task-specific variations.
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…
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…
Unified analysis of generalization and sample complexity for semi-supervised domain adaptation.
DAF uses attention sharing to adapt forecasts from abundant to scarce data.
State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the mos…
Paper tackles open set domain adaptation by detecting unknown classes.
New approach improves cross-domain recommendation for sparse target domains.
Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sub-linearly with the number of possible classes. This implies that features …
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions.…
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
A new method for handling missing values in data.
A new method quantizes output space for multi-target regression.
Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered learning where Profit Sharing works to plan a prey position in the higher layer and Q-learning method trains the state-actions to the target in the lower layer. In this paper, we expanded HMRL to multi-target problem to take the distance between …
New method combines regional HIV prevention trial data without sharing individual patient info.
Model improves fraud detection for new scenes with limited data.
Many tasks in natural language understanding require learning relationships between two sequences for various tasks such as natural language inference, paraphrasing and entailment. These aforementioned tasks are similar in nature, yet they are often modeled individually. Knowledge transfer can be effective for closely …
Unified multitask learning framework for mixed-type outcomes.
Study how transaction costs impact stock returns and holdings in equilibrium.
Proposes a robust FMR model for handling sample heterogeneity.
Study examines CSO algorithm for 3D swarming and tracking multiple targets.
The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning. Transfer learning is often based on the assumption that objects in both target and source domains share some common feature and/or data space. In this p…
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than , of the variance of the target variables. To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian ap…
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
Training-free source selection for LLM families with shared vocabularies
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have …
SMART transfers knowledge across related studies for multi-task learning.
Unified framework for response-adaptive targeting in multi-treatment experiments
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase lo…
K-FAC speeds up training of modern neural networks with linear weight-sharing.
Transthoracic echo is one of the most common means of cardiac studies in the clinical routines. During the echo exam, the sonographer captures a set of standard cross sections (echo views) of the heart. Each 2D echo view cuts through the 3D cardiac geometry via a unique plane. Consequently, different views share some l…
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of…
New method uses limited labeled data and multiple starts to adapt models across domains.
Trans-Glasso uses transfer learning to estimate precision matrices from related studies.
This paper introduces a cross adversarial source separation (CASS) framework via autoencoder, a new model that aims at separating an input signal consisting of a mixture of multiple components into individual components defined via adversarial learning and autoencoder fitting. CASS unifies popular generative networks l…
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning. The underlying premises are that there exist correlations among input parts and among output targets, and the …
Proposes a method to adapt to new classes in a domain shift.
Transfer learning assumes classifiers of similar tasks share certain parameter structures. Unfortunately, modern classifiers uses sophisticated feature representations with huge parameter spaces which lead to costly transfer. Under the impression that changes from one classifier to another should be ``simple'', an effi…
Graph embedding leaks sensitive graph properties and subgraphs.
This study assesses how share capital affects financial growth of non-financial firms listed at NSE.