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.
Understanding optimal prompts for binary sequence predictors is challenging.
We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering th…
Developed R package for creating nomograms for any ML algorithms.
This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sa…
Paper discusses binary classification with metric space predictors, privacy constraints, and convergence rates.
Efficiency criteria improve conformal predictors' performance.
Study fairness in ordinal regression using threshold models.
Study calibrates high-dimensional binary classifiers using angle between estimator and true weights.
This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems. Venn predictors produce probability-type predictions for the labels of test objects which are guaranteed to be well calibrated under the standard assumption that the observations ar…
New test for SGD in binary classification reduces computation time.
A faster method for optimizing DNA and protein sequences using machine learning.
New method calibrates multi-class predictions efficiently without sacrificing accuracy.
New loss function reduces outage probability in ML-assisted resource allocation.
Study on merging predictors in causal and anticausal directions using CMAXENT.
The study simplifies assessing overlap in logistic regression models using empirical likelihood.
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approx…
Open problem seeks an online learning algorithm for binary classification.
SplitWise enhances stepwise regression by adaptively encoding numeric predictors into binary features.
The problem is sequence prediction in the following setting. A sequence x1,..., xn,... of discrete-valued observations is generated according to some unknown probabilistic law (measure) mu. After observing each outcome, it is required to give the conditional probabilities of the next observation. The measure mu belongs…
Paper explores learning patterns in binary sequences, finding no method consistently outperforms others.
We carefully study how well minimizing convex surrogate loss functions, corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors. In particular, we show that amongst all convex surrogate losses, the hinge loss gives essentially the best possible bound, o…
This research improves binary classification by balancing overfitting and generalization with a novel Bayesian approach.
We consider a problem of data integration. Consider determining which genes affect a disease. The genes, which we call predictor objects, can be measured in different experiments on the same individual. We address the question of finding which genes are predictors of disease by any of the experiments. Our formulation i…
Binary classification improves with a small fraction of corrupted labels.
The paper analyzes network models with binary values and sub-Gamma noise, deriving asymptotic properties.
This paper investigates the problem of determining a binary-valued function through a sequence of strategically selected queries. The focus is an algorithm called Generalized Binary Search (GBS). GBS is a well-known greedy algorithm for determining a binary-valued function through a sequence of strategically selected q…
Proposes MELODIC family for simultaneous binary logistic regression.
We work with a generalization of knot theory, in which one diagram is reachable from another via a finite sequence of moves if a fixed condition, regarding the existence of certain morphisms in an associated category, is satisfied for every move of the sequence. This conditional setting leads to a possibility of irreve…
Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods. Recently, hard-attention-based methods have been proposed to prevent fatal alignment errors, but their sampling method of discrete alignment is poorly investigated. This research investigates various combinations of sampling methods…
This paper applies deep learning to ordinal regression, modeling it as a binary search.
Interpretability has arisen as a key desideratum of machine learning models alongside performance. Approaches so far have been primarily concerned with fixed dimensional inputs emphasizing feature relevance or selection. In contrast, we focus on temporal modeling and the problem of tailoring the predictor, functionally…
Introduces alternators for modeling sequences, outperforming baselines.
The book explores universal time-series forecasting using mixture predictors.
Paper bounds convergence rate of adversarial surrogate risk.
BELIEF framework interprets GLMs using binary linear models.
To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized linear/logistic regressio…
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an …
In order to study large variations or fluctuations of finite or infinite sequences (time series), we bring to light an 1868 paper of Crofton and the (Cauchy-)Crofton theorem. After surveying occurrences of this result in the literature, we introduce the inconstancy of a sequence and we show why it seems more pertinent …
New method evaluates LLMs fairness in universal prediction.
Adaptive correlated MC improves sequence generation stability.
Memory-based models can learn to approximate Bayes-optimal predictors for non-stationary data.
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributi…
Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of th…
Contradiction graphs reveal VC dimension threshold.
During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which …
The paper derives a new theorem for predicting batches of data.
Study compares various calibration methods for binary classification tasks.
This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem. Unlike most previous work, we give results that are general enough to include cases in which no minimum exists, as occurs typically, for in…