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.
Bayesian nonparametric LABS model adapts to function smoothness in Besov spaces.
New method for adaptive estimation and inference in econometric models without knowing smoothness.
We propose an adaptive smoothing algorithm based on Nesterov's smoothing technique in \cite{Nesterov2005c} for solving "fully" nonsmooth composite convex optimization problems. Our method combines both Nesterov's accelerated proximal gradient scheme and a new homotopy strategy for smoothness parameter. By an appropriat…
New adaptive methods for constrained convex optimization and variational inequalities.
Adaptive algorithms improve performance in non-convex optimization across various scenarios.
Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in non-stati…
We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stochastic line-search procedure to gradually increase the learning rate, then automatically switches to a statistical method to decrease the le…
Efficient algorithms for contextual bandits with smooth regret in continuous action spaces.
DoWG optimizer automatically adapts to convex and nonsmooth problems without tuning.
Learn to automatically plug domain-specific modules into a common network.
Paper presents a Transformer model for automatic domain adaptation.
A standard way to obtain convergence guarantees in stochastic convex optimization is to run an online learning algorithm and then output the average of its iterates: the actual iterates of the online learning algorithm do not come with individual guarantees. We close this gap by introducing a black-box modification to …
D-Adaptation automatically sets optimal learning rates without manual tuning.
We investigate and compare the fundamental performance of several distributed learning methods that have been proposed recently. We do this in the context of a distributed version of the classical signal-in-Gaussian-white-noise model, which serves as a benchmark model for studying performance in this setting. The resul…
Bayesian deep learning with heavy-tailed weights achieves near-optimal performance.
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
Pairwise Label Smoothing improves deep model generalization by reducing overconfidence.
agtboost speeds up gradient tree boosting with automatic complexity adjustment.
Adaptive gradient methods converge faster with over-parameterization and line-search.
Multiple generalized additive models (GAMs) are a type of distributional regression wherein parameters of probability distributions depend on predictors through smooth functions, with selection of the degree of smoothness via regularization. Multiple GAMs allow finer statistical inference by incorporating explana…
Smooth contact maps are always smooth in rigid Carnot groups.
We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance. To address this problem, we present Maximum Variance Total V…
Stochastic Gradient Descent (SGD) has played a central role in machine learning. However, it requires a carefully hand-picked stepsize for fast convergence, which is notoriously tedious and time-consuming to tune. Over the last several years, a plethora of adaptive gradient-based algorithms have emerged to ameliorate t…
In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a -dimensional domain. This method was proposed by Adams, Murray and MacKay (ICML, 2009), who developed a tractable computational approach and show…
AdaAnn optimizes annealing for efficient probability density approximation.
ACL improves DRL by adapting task difficulty to agent's capabilities.
NeAda solves nonconvex minimax optimization by balancing primal and dual variables adaptively.
Paper tackles online learning with interval regret, achieving adaptive bounds.
tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier approximation and a recursive median sieve) could be automatically applied to time series classificatio…
Adapts Hölder smoothness with normalized gradients.
New algorithm tackles nonconvex machine learning problems with adaptive normalization and independent sampling.
Develops a mathematical model for automatic differentiation in machine learning.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and …
Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to …
We study a non-parametric multi-armed bandit problem with stochastic covariates, where a key complexity driver is the smoothness of payoff functions with respect to covariates. Previous studies have focused on deriving minimax-optimal algorithms in cases where it is a priori known how smooth the payoff functions are. I…
The paper proposes a method to compute higher infinitesimals in numerical and symbolic analysis.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
New methods detect objects in industrial settings with little training data.
This paper presents, evaluates, and discusses a new software tool to automatically build Dynamic Bayesian Networks (DBNs) from ordinary differential equations (ODEs) entered by the user. The DBNs generated from ODE models can handle both data uncertainty and model uncertainty in a principled manner. The application, na…
This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture. This construction can be used to transfer weights when growing or pruning a decision tree, allowing non-greedy decision tree algorithms to automatically learn and adapt to the ideal arch…
DoubleAdapt improves stock trend forecasting by adapting models to evolving data.
SATL adapts to varying smoothness in hypothesis transfer learning.
This manuscript addresses the problem of the automatic lesion boundary detection in dermoscopy, using deep neural networks. An approach is based on the adaptation of the U-net convolutional neural network with skip connections for lesion boundary segmentation task. I hope this paper could serve, to some extent, as an e…
Improves numerical solution of ill-conditioned linear systems for machine learning.
Cone structures over minimal products can't be calibrated smoothly.
AutoClip automatically adjusts gradient clipping for better audio separation.
Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received increasing attention in the field of optimization for developing optimization algorithms with fast convergence. However, the studies of EBC in st…