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 model for discrete data with conditional transformations.
In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors. We propose a semi-parametric algorithm based on the ordering of the responses which is invariant to the functional form of the transformation function. We prove t…
Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. These issues can attributed to reasons including (1) short-range model architectures that capture limited temporal dependencies, (2) limitations of the maximum likel…
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts the …
Gonogo offers tools for sensitivity experiments in R.
Extends RRR to capture nonlinear interactions in multi-response regression.
Transformers are explained as infinite-dimensional kernel machines.
A new image completion method inspired by brain cells.
The aim of this paper is to construct and analyze solutions to a class of Hamilton-Jacobi-Bellman equations with range bounds on the optimal response variable. Using the Riccati transformation we derive and analyze a fully nonlinear parabolic partial differential equation for the optimal response function. We construct…
VED framework learns low-dimensional latent representations of physical systems.
In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors. To relax such stringent assumptions made by parametric linear models, additive models consider the response to be a summation of unknown transformations applied on the predict…
Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology a…
Current multi-view factorization methods make assumptions that are not acceptable for many kinds of data, and in particular, for graphical data with hierarchical structure. At the same time, current hierarchical methods work only in the single-view setting. We generalize the Treelet Transform to the Multi-View Treelet …
We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification cost…
Feature interactions can contribute to a large proportion of variation in many prediction models. In the era of big data, the coexistence of high dimensionality in both responses and covariates poses unprecedented challenges in identifying important interactions. In this paper, we suggest a two-stage interaction identi…
New analysis shows how cross-entropy training shapes attention in transformers.
Study presents MMC model for better fitting multiple choice data.
Kernel models learn low-dimensional predictive subspaces from input data.
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…
Neural network predicts functional responses from scalar inputs.
Study develops ensemble machine learning framework for predicting groundwater heavy metal pollution.
Theoretical analysis shows LLMs can self-correct responses through in-context learning.
FCOC framework improves financial volatility forecasting.
New method selects causal features from diverse data types.
SKI speeds up Toeplitz Neural Networks by avoiding explicit decay bias and using frequency response.
This paper introduces a novel recalibration method for multivariate forecasts.
SAMformer improves transformer performance in time series forecasting.
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest…
Novel Bayesian model improves EEG-based BCI character selection.
Paper proposes linear transformers for efficient in-context learning without context length limitations.
RLGP model improves robustness and accuracy for discontinuous response surfaces.
Adaptive TFTs improve cryptocurrency price prediction accuracy.
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding. Due to the spars…
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural network solutions? For linear networks, it has been shown that different instances of the same network architecture encode the same representatio…
Study proposes adaptive RL for dynamic portfolio optimization.
Transformers improve with Fourier integral attentions.
Proposes a method to create shorter, more accurate prediction intervals.
CSHT predicts financial returns from news using a novel transformer model on a sphere.
New Performer model tackles long-sequence protein modeling.
We propose methods for estimating correspondence between two point sets under the presence of outliers in both the source and target sets. The proposed algorithms expand upon the theory of the regression without correspondence problem to estimate transformation coefficients using unordered multisets of covariates and r…
The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are cru…
Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize inc…
OptFormer learns universal HPO from diverse datasets.
CogFormer trains a transformer to estimate parameters across various cognitive models.
A new method for unfolding histograms without matrix inversion.
To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binar…
New method identifies shared topics in LLM inputs and outputs for better detection of hallucinations.
This paper continues a series of studies devoted to analysis of the bivariate probability distribution P(x,y) of two consecutive price increments x (push) and y (response) at intraday timescales for a group of stocks. Besides the asymmetry properties of P(x,y) such as Market Mill dependence patterns described in preced…