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.
The paper proposes a method to stabilize predictions by identifying causal variables using a seed variable.
Stable health predictions need deconfounding test set features.
SFB uses stable features to adapt unstable ones for better performance.
Retraining stabilizes model influence on data.
Proposes BSSP to stabilize predictions in biased data.
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the de…
Model predicts stable molecules with AI and physics constraints.
Stable long-term predictions for fluid flows using neural networks.
New algorithms improve policy evaluation in reinforcement learning.
Proposes a method to learn stable invariant sets in dynamical systems.
Shifts in environment between development and deployment cause classical supervised learning to produce models that fail to generalize well to new target distributions. Recently, many solutions which find invariant predictive distributions have been developed. Among these, graph-based approaches do not require data fro…
We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset. Specifically, we consider the notions of uniform stability (Bousquet and Elisseeff, 2001) and prediction privacy (Dwork and Feldman, 2018). Previous work on these notions shows how…
Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions…
Spectral gradient methods outperform Euclidean in certain deep learning scenarios.
LOO-StabCP speeds up CP for multiple predictions.
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is a strong limitation for applications involving critical decisions, typica…
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stab…
Method predicts LFSM increments from past observations using codifference.
Bayesian Invariant Prediction models stable features from multi-environment data.
Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce ch…
Study compares MoE and RNN models for stock price prediction across volatility profiles.
The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice. However, the theoretical rate at which past CFR-based algorithms converge to the Nash equilibrium is on the order of , where is the number of iterations. In contrast, first-order methods can be used to …
A cubing strategy identifies stable hyperparameter regions for uncertainty quantification in spatial deep learning.
Predictive coding networks are shown to be stable, robust, and converge faster than backpropagation.
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations…
Study homotopy types of free racks and quandles, proving analogs of Milnor's theorem.
FIRES framework selects stable features from online data.
SIRUS creates interpretable rules from random forests for regression.
The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to ge…
Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, the…
Online algorithms stabilize in feedback loops of performative prediction.
We consider the problem of online prediction in a marginally stable linear dynamical system subject to bounded adversarial or (non-isotropic) stochastic perturbations. This poses two challenges. Firstly, the system is in general unidentifiable, so recent and classical results on parameter recovery do not apply. Secondl…
Random forests are stable and provide reliable prediction intervals.
We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretica…
This paper optimizes performative risk by focusing on convex properties and developing efficient algorithms.
This paper introduces libconform v0.1.0, a Python library for the conformal prediction framework, licensed under the MIT-license. libconform is not yet stable. This paper describes the main algorithms implemented and documents the API of libconform. Also some details about the implementation and changes in future versi…
Algorithm learns dynamics from past observations.
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
FlowMM models stable crystal structures efficiently.
We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algori…
Study improves stock price prediction using adaptive Mixture of Experts framework.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrica…
Improved online prediction with guaranteed coverage.
New algorithm learns stable LDSs with lower error and better control performance.
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs). Despite the predictive power of these systems, it has been difficult to make formal claims about the basic properties of the learned systems. In …
Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferen…