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.
Develops a minimax optimal estimator for system stability under distribution shift.
Cluster stability selection improves feature selection in correlated data.
Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…
New method improves stability of Gaussian process approximations.
Pipeline learns topological features for protein stability prediction.
Recent work has studied the reasons for the remarkable performance of deep neural networks in image classification. We examine batch normalization on the one hand and the dynamical systems view of residual networks on the other hand. Our goal is in understanding the notions of stability and smoothness of the inter-laye…
The paper explores stability and generalization of deep GCNs.
Paper proposes an unsupervised feature selection algorithm with stability guarantees.
We introduce a notion of algorithmic stability of learning algorithms---that we term \emph{argument stability}---that captures stability of the hypothesis output by the learning algorithm in the normed space of functions from which hypotheses are selected. The main result of the paper bounds the generalization error of…
Online algorithms stabilize in feedback loops of performative prediction.
New insights on stability in reservoir computing for better performance.
Survey explores geometric aspects of policy optimization in control systems.
Paper introduces stability in model averaging and proposes a L2-penalty method.
To date, the instability of prognostic predictors in a sparse high dimensional model, which hinders their clinical adoption, has received little attention. Stable prediction is often overlooked in favour of performance. Yet, stability prevails as key when adopting models in critical areas as healthcare. Our study propo…
Paper improves zero-shot protein stability prediction by clarifying free-energy foundations.
WAVE improves stability in reinforcement learning by adaptively weighting critic's loss.
Bayesian algorithm stabilizes unknown continuous-time systems from unstable data.
New algorithm stabilizes RL policy learning through divergence regularization.
The overall performance or expected excess risk of an iterative machine learning algorithm can be decomposed into training error and generalization error. While the former is controlled by its convergence analysis, the latter can be tightly handled by algorithmic stability. The machine learning community has a rich his…
Differentiable architecture search (DARTS) is a prevailing NAS solution to identify architectures. Based on the continuous relaxation of the architecture space, DARTS learns a differentiable architecture weight and largely reduces the search cost. However, its stability has been challenged for yielding deteriorating ar…
We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations i…
New method stabilizes DEQ models by regularizing Jacobian of fixed-point equations.
Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-seri…
Improves test set performance and reduces out-of-sample disappointment for unstable models.
Stability of recurrent models is closely linked with trainability, generalizability and in some applications, safety. Methods that train stable recurrent neural networks, however, do so at a significant cost to expressibility. We propose an implicit model structure that allows for a convex parametrization of stable mod…
Paper proposes a new framework to improve stability-based bounds in deep learning.
Improves neural architecture search methods to be more stable and efficient.
New algorithm improves stability of optimization algorithms by adapting step-size.
In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradient-bas…
Improved MLE for Hawkes Processes stabilizes unstable optimization.
Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy. In t…
Generative adversarial networks (GANs) are effective in generating realistic images but the training is often unstable. There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods. To this end, we present …
Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be g…
The paper analyzes stability and generalization of decentralized SGD.
The correspondence between residual networks and dynamical systems motivates researchers to unravel the physics of ResNets with well-developed tools in numeral methods of ODE systems. The Runge-Kutta-Fehlberg method is an adaptive time stepping that renders a good trade-off between the stability and efficiency. Can we …
OMD and DA perform similarly in static settings but OMD is inferior under dynamic learning rates.
SAM improves generalization by operating near the edge of stability.
New method stabilizes saddle-point optimization with unbounded gradients.
AI threatens financial stability through misuse and stealth adoption.
MOSS optimizes decision rules for accuracy and stability.
New framework replicates private equity performance using AI and liquid strategies.
Noise-robust Koopman operator framework for control with improved stability and performance.
A novel Bayesian computation method using importance weighting improves numerical stability and performance.
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a consequence, learning algorithms are rarely applied on safety-crit…
The paper explores the generalization of quantum neural networks using stability theory.
A new family of penalty functions, adaptive to likelihood, is introduced for model selection in general regression models. It arises naturally through assuming certain types of prior distribution on the regression parameters. To study stability properties of the penalized maximum likelihood estimator, two types of asym…
Proposes a new stability measure for model fitting on similar feature data sets.
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing …