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.
New optimizer MARS-M combines variance reduction with Muon for faster LLM training.
MarS simulates financial markets using generative models.
Novel estimation methods improve MAR model accuracy for high-dimensional time series.
MARS optimizes large model training by reducing variance, outperforming AdamW.
MARS model outperforms others in stock price prediction across sectors.
Improves MARS for nonparametric multivariate regression with dimension reduction.
SMART combines decision trees and MARS for better regression modeling.
Proposes a lasso variant of MARS for nonparametric regression.
Latent Variable Models (LVMs) are a large family of machine learning models providing a principled and effective way to extract underlying patterns, structure and knowledge from observed data. Due to the dramatic growth of volume and complexity of data, several new challenges have emerged and cannot be effectively addr…
MARS-Gym framework for marketplaces to train and evaluate recommender systems.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
Paper proposes a new MAR model for global economic forecasting.
Improved flow-based models capture dependencies better with multi-scale autoregressive priors.
Optimizes variational autoencoder for detecting missing data in Mars rover transmissions.
FLOWGEM generates complete datasets from incomplete data with non-monotone MAR missingness.
A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the…
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable compar…
Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first …
A new unsupervised method removes CT metal artifacts using beta-CycleGAN and attention.
A probabilistic query may not be estimable from observed data corrupted by missing values if the data are not missing at random (MAR). It is therefore of theoretical interest and practical importance to determine in principle whether a probabilistic query is estimable from missing data or not when the data are not MAR.…
New approach to meaningful and robust algorithmic recourse.
Proposes a method to combine datasets with missing values using Gaussian process latent variables.
The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The re…
Proposes a framework to handle missing data in traffic forecasting with sensor blackouts.
Improves BN graph learning with splines for scalability.
Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a us…
A new method for semi-supervised learning with missing data using GMM and margin confidence.
Finding an energy minimum in the Ising model is an exemplar objective, associated with many combinatorial optimization problems, that is computationally hard in general, but occurs in all areas of modern science. There are several numerical methods, providing solution for the medium size Ising spin systems. However, th…
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
We provide a model to understand how adverse weather conditions modify traffic flow dynamic. We first prove that the microscopic Free Flow Speed of the vehicles is changed and then provide a rule to model this change. For this, we consider a thresholded linear model, corresponding to an application of a MARS model to r…
Efficiently clusters incomplete data without imputation or full EM, faster and more accurate.
Planetary exploration missions with Mars rovers are complicated, which generally require elaborated task planning by human experts, from the path to take to the images to capture. NASA has been using this process to acquire over 22 million images from the planet Mars. In order to improve the degree of automation and th…
Kernel ridge regression for causal inference with missing data.
On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to …
Deep neural networks (DNNs) generate much richer function spaces than shallow networks. Since the function spaces induced by shallow networks have several approximation theoretic drawbacks, this explains, however, not necessarily the success of deep networks. In this article we take another route by comparing the expre…
New method tackles MNAR missingness in domain adaptation.
In this paper, using blow-up analysis, we prove a quantization result for an elliptic equation with critical exponential growth on compact Riemannian surface without boundary. Similar results for Euclidean space were obtained by Adimurthi-Struwe \cite{Adi-Stru}, Druet \cite{Druet}, Lamm-Robert-Struwe \cite{L-R-S}, Mart…
MARS meta-learns function scores for improved predictive accuracy and uncertainty.
Fed-MIWAE improves federated learning by imputing missing data.
Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the…
New method for estimating mean in SS inference with selection bias and decaying overlap.
Study compares traditional and machine learning methods for handling missing data in longitudinal studies.
Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computatio…
Missing data are ubiquitous in many domains including healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process. Consequently, simply applying existin…
New taxonomy for structured missingness in large-scale databases.
We propose an actor-critic, model-free, and online Reinforcement Learning (RL) framework for continuous-state continuous-action Markov Decision Processes (MDPs) when the reward is highly sparse but encompasses a high-level temporal structure. We represent this temporal structure by a finite-state machine and construct …
Study improves feature acquisition for static settings in AFAPE.
We provide the first solution for model-free reinforcement learning of ω-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almost-sure satisfaction of ω-regular objectives to an almost- sure reachability problem and extend this technique to learning how to control an …