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.
IMMIGRATE selects features with interaction terms using margin-based weights.
The key issue of few-shot learning is learning to generalize. This paper proposes a large margin principle to improve the generalization capacity of metric based methods for few-shot learning. To realize it, we develop a unified framework to learn a more discriminative metric space by augmenting the classification loss…
Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models for both continuous and categorical response var…
A new method for unsupervised domain adaptation using Gaussian processes.
We embed KKT points in neural networks of different sizes.
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive princi…
Improves Gaussian process regression without bias.
New insights into using IPF for inferring dynamic networks from marginals.
We consider an optimal stopping problem where a constraint is placed on the distribution of the stopping time. Reformulating the problem in terms of so-called measure-valued martingales allows us to transform the marginal constraint into an initial condition and view the problem as a stochastic control problem; we esta…
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov style regularization where the regularization penalty articulates the way in which the marginal density should constrain otherwise unrestricted co…
New algorithm optimizes margin distribution in binary classifiers.
New framework using Jensen-Shannon divergence improves domain adaptation theory.
Christoffel function characterizes the corruption a bounded-degree certificate cannot remove in robust halfspace learning.
Algorithm improves SVM classification in non-Euclidean spaces.
Paper proposes a new uncertainty measure for active learning in neural networks.
Paper proposes adaptive margin loss to improve few-shot learning.
New method resolves nonidentifiability in mixture models.
Paper reinterprets marginal productivity theory using vectorial products, challenging traditional ethical interpretations.
In many real-world applications, data is not collected as one batch, but sequentially over time, and often it is not possible or desirable to wait until the data is completely gathered before analyzing it. Thus, we propose a framework to sequentially update a maximum margin classifier by taking advantage of the Maximum…
Framework identifies causal direction from single data setting.
Maximizing margins leads to lossless compression of training data.
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-lik…
Unified framework for removing unwanted information from machine learning models.
This work improves adversarial robustness by boosting model ensembles with margin maximization.
In this paper, we develop a new mathematical technique which allows us to express the joint distribution of a Markov process and its running maximum (or minimum) through the marginal distribution of the process itself. This technique is an extension of the classical reflection principle for Brownian motion, and it is o…
Proposes an online metric learning method for multi-label classification.
New margin-based regularization and selective sampling improve deep neural network performance.
Proposes learning invariances in neural networks using a weight-space approach.
Identifying components and estimating mixing weights in unlabeled finite mixtures under marginal independence.
ELM improves neural model embeddings for long-tail learning.
Link prediction is a fundamental task in statistical network analysis. Recent advances have been made on learning flexible nonparametric Bayesian latent feature models for link prediction. In this paper, we present a max-margin learning method for such nonparametric latent feature relational models. Our approach attemp…
Given a task of predicting from , a loss function , and a set of probability distributions on , what is the optimal decision rule minimizing the worst-case expected loss over ? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying t…
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, it is relatively insufficient to empower the discriminative ability of DGMs on making accurate predictions. This paper presents max-ma…
This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex transport constraints in addition to having given initial and terminal marginals. Sev…
A new principle for extrapolating regression outside training data.
Generative models often fail to preserve joint structure despite matching marginals.
Paper studies apparent horizon dynamics and introduces a null comparison principle.
This paper connects masked pre-training to Bayesian model selection.
We introduce a natural generalization of marginally outer trapped surfaces, called immersed marginally outer trapped surfaces, and prove that three dimensional asymptotically flat initial data sets either contain such surfaces or are diffeomorphic to R^3. We establish a generalization of the Penrose singularity theorem…
New study reveals a polynomial penalty for adapting to unknown margin parameters in batched nonparametric bandits.
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize…
Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pe…
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the ma…
New methods improve deep learning on imbalanced datasets.
New method for efficient conditional sampling from diffusion models.
Bayesian principles improve neural additive models for better feature selection and uncertainty.
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chos…