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.
This paper improves volatility forecasting using dynamic subset selection in genetic programming.
Support vector machine (SVM) training is an active research area since the dawn of the method. In recent years there has been increasing interest in specialized solvers for the important case of linear models. The algorithm presented by Hsieh et al., probably best known under the name of the "liblinear" implementation,…
Adaptive cascade submodular maximization tackles sequential selection under uncertainty.
Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our selection rules are based on the dual residual or the primal-du…
LOFT separates subspace rotation and transformation for orthogonal fine-tuning.
SASE improves attributed graph clustering for large graphs with linear time and space complexity.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical analysis of optimal transport in domain adaptation and show that it can directly suggest a feature selection procedure leveraging the shift …
This paper analyzes MaskGIT sampler and introduces a moment sampler for faster masked diffusion sampling.
Roy's `Safety First' criterion for selecting one risky asset from many is adapted to the case of non-normal returns, via Cornish Fisher expansion. The resulting investment objective is consistent with first order stochastic dominance, and is equal to the Sharpe ratio for the case of normal returns. An investor selectin…
Adaptive learning model forecasts financial prices using order book data.
The anomaly detection of time series is a hotspot of time series data mining. The own characteristics of different anomaly detectors determine the abnormal data that they are good at. There is no detector can be optimizing in all types of anomalies. Moreover, it still has difficulties in industrial production due to pr…
In this paper, we present a machine learning approach for estimating the number of incident wavefronts in a direction of arrival scenario. In contrast to previous works, a multilayer neural network with a cross-entropy objective is trained. Furthermore, we investigate an online training procedure that allows an adaptio…
SIREN protocol corrects optimistic winner's scores in LLM evaluation.
Algorithm detects causal change points quickly with adaptive interventions.
A new algorithm selects models for contextual bandits, reducing regret.
We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation sc…
We introduce a new criterion to determine the order of an autoregressive model fitted to time series data. It has the benefits of the two well-known model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the data is generated from a finite order autoregression, the Bay…
Balances the regret of different algorithms in bandit and RL problems.
Selecting diverse and important items, called landmarks, from a large set is a problem of interest in machine learning. As a specific example, in order to deal with large training sets, kernel methods often rely on low rank matrix Nyström approximations based on the selection or sampling of landmarks. In this context, …
AdaSub optimizes with second-order info in low-dims subspace.
We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Ma…
An efficient algorithm selects the correct number of latent dimensions in multidimensional probit models.
Cost-effective feature selection improves network model choice.
Paper proposes a new method combining random forests and Lasso selection.
Paper proposes adaptive parameter selection for KGD algorithms.
We propose an adaptive optimization method for deep learning that dynamically adjusts batch size.
Paper proposes ClipSMT algorithm for better ATE estimation.
AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.
EASE optimizes exemplar selection for ICL in LLMs efficiently.
An online learning framework for survival analysis with real-time adaptation.
Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult …
This paper optimizes PCE for efficient surrogate modeling in engineering.
New method handles robust and adaptive control of linear systems with non-convex costs.
Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancel…
ARL-GEN adapts to the smallest model class in nested families for RL with improved regret.
In today world of enormous amounts of data, it is very important to extract useful knowledge from it. This can be accomplished by feature subset selection. Feature subset selection is a method of selecting a minimum number of features with the help of which our machine can learn and predict which class a particular dat…
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range. We fit saturating splines to data using a convex optimization problem over a space of measures, which we solve using an efficient algorithm based on the condi…
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promisin…
Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backw…
New method reduces memory usage for high-dimensional variable selection.
We study the convergence of the predictive surface of regression trees and forests. To support our analysis we introduce a notion of adaptive concentration for regression trees. This approach breaks tree training into a model selection phase in which we pick the tree splits, followed by a model fitting phase where we f…
New approach to adaptively select bandwidths in nonparametric regression.
AGS-CL selectively updates penalties based on node importance for continual learning.
ACS is an interactive framework for model-free selection with guaranteed error control.
Proposes a new sampling policy for ranking and selection problems.
New model selects uncorrelated and discriminative features for unsupervised feature selection.
Paper introduces slow kill for efficient large-scale variable screening.