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.
Introduces joint exclusivity (JE), a new form of negative dependence.
The paper explores the relationship between joint mixability and negative dependence structures.
Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution and returns a hypothesis chosen from a fixed class with small loss . In the parametric setting, depending upon $(\ell…
Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm. We provide a new interpretation of mixability via convex analysis that highlights the role of the Kullback-Leibler divergence in its definit…
In this paper we extend the setting of the online prediction with expert advice to function-valued forecasts. At each step of the online game several experts predict a function, and the learner has to efficiently aggregate these functional forecasts into a single forecast. We adapt basic mixable (and exponentially conc…
Optimal algorithms for mixable losses in dynamic environments with reduced redundancy.
Near-logarithmic regret per switch achieved for mixable/exp-concave losses.
The goal of online prediction with expert advice is to find a decision strategy which will perform almost as well as the best expert in a given pool of experts, on any sequence of outcomes. This problem has been widely studied and and regret bounds can be achieved for convex losses (\cite{zin…
The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditi…
Improved multiclass logistic regression with lower computational complexity.
The paper examines risk aggregation under mixtures of marginals, finding that more homogeneous distributions lead to larger uncertainty.
Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). Popular example of scoring rule for continuous outcomes is the …
Bayesian framework reduces online optimization regret.
Gaptron algorithm reduces mistakes in online multiclass classification.
We discuss the general properties of the theory of joint invariants of a smooth Lie group action in a manifold. Many of the known results about differential invariants, including Lie's finiteness theorem, have simpler versions in the context of joint invariants. We explore the relation between joint and differential in…
FJS method improves multinomial classification accuracy.
Paper proposes a new method to evaluate joint risk under uncertainty.
Introduces joint Shapley values to measure feature importance in models.
Study proposes a new model for joint survival annuity valuation.
Estimates joint causal effects using single-variable interventions on nonlinear models.
Study joint invariants on symplectic spaces, extending group and space variations.
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
Proposes joint LCA for multiview data to identify shared and view-specific components.
Joint diffusion models improve data representation for both generation and prediction.
The Neural Testbed evaluates joint predictions of neural agents, revealing their limitations.
We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition.…
New Bayesian method for joint sparse parameter inference.
We discuss possible extensions of the classical Chern-Weil formalism to an infinite dimensional setup. This is based on joint work with Steven Rosenberg, joint work with Simon Scott and joint work with Jouko Mickelsson.
Unified theorem for deep and shallow joint-equivariant machines.
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i…
Paper uses non-Euclidean analysis to classify brain structure variations.
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We…
We employ the language of Cartan's geometry to present a model for studying vector spaces of Killing two-tensors defined in pseudo-Riemannian spaces of constant curvature under the action of the corresponding isometry group. We also discuss geometric properties of joint invariants of Killing two-tensors defined in the …
This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional dist…
Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the condi…
Proposes a new method for handling domain shift in samples with biases in both covariates and labels.
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
Analyzing a comprehensive news dataset, we document that joint news coverage triggers attention contagion, causing temporarily inflated valuations for affected stocks. Tracing SEC EDGAR visits from unique IPs, we provide direct evidence of attention spillovers between stocks. Stocks with greater joint news coverage exh…
Develops a new framework for estimating joint probability distributions.
Researchers derived formulas for joint moments of elliptical distributions.
Surveying joint Gaussian graphical models to identify shared structures across domains.
Better signal detection in undersampled data using joint and cross covariances.
New method combines score lists using joint CDFs, improving computation.
Learning linear predictors with the logistic loss---both in stochastic and online settings---is a fundamental task in machine learning and statistics, with direct connections to classification and boosting. Existing "fast rates" for this setting exhibit exponential dependence on the predictor norm, and Hazan et al. (20…
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work l…
The paper presents a method to estimate joint interventional distributions from marginal interventional data.