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.
MFAI uses gradient boosted trees to leverage auxiliary info for scalable Bayesian matrix factorization.
Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…
Enhances normal mean estimation with side info using NIT approach.
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of -sparse signals b…
We characterize the performance of sequential information guided sensing, Info-Greedy Sensing, when there is a mismatch between the true signal model and the assumed model, which may be a sample estimate. In particular, we consider a setup where the signal is low-rank Gaussian and the measurements are taken in the dire…
Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like height or depth of the characters. Moreover, actions in such games typically involve particular sequential action orders, which also makes the…
New models learn stable latent clusters without side info.
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific a…
Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consi…
Estimates non-parametric logistic model using case-control data and external summary info.
AuxiLearn combines auxiliary tasks into a single loss function.
New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.
In-N-Out improves model robustness to out-of-distribution data.
Aux-NAS uses auxiliary labels to improve primary task performance without extra inference cost.
Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiri…
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
Researchers identify valid auxiliary functions for extreme value distributions and their max-domains of attraction.
Study achieves fairness without demographic info, improving regression tasks.
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
The paper improves high-dimensional linear regression prediction and estimation using auxiliary samples.
Method reweights auxiliary tasks to reduce data need for main task.
This paper reviews dMTL and methods for selecting auxiliary tasks.
Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…
Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on th…
Paper tackles circularity issues in machine learning predictions.
Proves Bochner's identity on graphs using a new auxiliary graph.
ScoreFusion fuses multiple diffusion models to enhance generative modeling of a target population.
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…
Proposes a method to improve graph neural networks on heterogeneous graphs using meta-paths.
NeurT-FDR controls FDR by incorporating auxiliary covariates in deep learning.
We introduce a new family of MCMC samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density. Our approach permits the marginalisation over the auxiliary variables yielding marginal samplers, or the augmentation of the auxiliary variables, yielding auxiliary samplers. The well…
Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged b…
REGAIN learns optimal auxiliary directions for forecast reconciliation.
LRSAO uses RL to dynamically select and unlearn auxiliary objectives for EA optimization.
We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent vari- ables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally determini…
Enhances machine learning with background knowledge through feature generation.
Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into ac…
Learn dynamics of a system using auxiliary data from similar systems.
Detecting and aggregating sentiments toward people, organizations, and events expressed in unstructured social media have become critical text mining operations. Early systems detected sentiments over whole passages, whereas more recently, target-specific sentiments have been of greater interest. In this paper, we pres…
A framework for using auxiliary data to improve few-shot learning.
Proto-value networks improve deep reinforcement learning representations using auxiliary tasks.
Auxiliary equations improve bounds in symplectic geometry.
One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivial to know if an auxiliary task will be helpful for the main task and when it could start hurting. We propose to use the cosine similarity be…
Improves deep learning models by blending gradients from training loss and auxiliary objective.
This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machi…
GEAR uses auxiliary data to estimate optimal decisions in studies with limited primary outcomes.
New method extracts factors of variation from data without much supervision.
We show that the auxiliary variable method (Møller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation method for likelihood estimation.