arXiv research

New method for interpreting non-linear models using forward marginal effects.

The paper proposes effective margin regularization to improve adversarial robustness in deep neural networks.

This paper identifies and bounds ICE central moments using PO marginal central moments.

We develop an HMC algorithm to easily marginalize random effects in LMMs.

fmeffects package interprets non-linear models in plain language.

Proposes a tabular transformer model to maintain feature effect intelligibility.

In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, colle…

Study evaluates margin parameter effects on knowledge embedding quality.

MACQ method explains deep learning models by analyzing feature contributions across prediction levels.

New method bounds causal effects using local consistency of marginals.

Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass of information about the entire margin distribution, which is crucial …

New method resolves nonidentifiability in mixture models.

Frugal Flows learn complex data and infer marginal causal effects.

Neural models improve GLMMs for complex data.

Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe. Various authors have described multiple metrics to evaluate the capacity of deep architectures. In this paper, we allude to the radius margin bounds described for a support vector machine (SVM) with hinge loss, apply the…

Improved exploration in RL with latent state marginalization.

In this paper, we reformulate the forest representation learning approach as an additive model which boosts the augmented feature instead of the prediction. We substantially improve the upper bound of generalization gap from $\mathcal{O}(\sqrt\frac{\ln m}{m})$ to $\mathcal{O}(\frac{\ln m}{m})$, while $λ$ - the margin r…

Boosting and other ensemble methods combine a large number of weak classifiers through weighted voting to produce stronger predictive models. To explain the successful performance of boosting algorithms, Schapire et al. (1998) showed that AdaBoost is especially effective at increasing the margins of the training data. …

Relief based algorithms have often been claimed to uncover feature interactions. However, it is still unclear whether and how interaction terms will be differentiated from marginal effects. In this paper, we propose IMMIGRATE algorithm by including and training weights for interaction terms. Besides applying the large …

Warm starts improve Gaussian process regression by up to 16x.

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…

Max-min margin Markov networks improve consistency in structured prediction.

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…

Margin enlargement over training data has been an important strategy since perceptrons in machine learning for the purpose of boosting the robustness of classifiers toward a good generalization ability. Yet Breiman (1999) showed a dilemma that a uniform improvement on margin distribution does NOT necessarily reduces ge…

ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.

Improves survival prediction model calibration for better individual decision-making.

New insights show Medicaid impacts on ED use vary widely, with some groups seeing significant increases.

MSBM extends SB for multi-marginal trajectory inference.

Active learning can't improve over passive in certain settings.

New framework for learning from imbalanced data with theoretical guarantees.

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation. However, addressing this problem has two critical challenges: (i) Measuring the multi-marginal distance amon…

New method for efficient marginalization of discrete latent variables in neural networks.

The paper examines how heavy-tailed risks behave under Gaussian copula models.

This work introduces a noise-adaptive conformal inference method for better prediction sets in noisy data.

We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the margin-adapted dimension, which is a simple function of the second order statistics of the data distribution, and show distribution-specific upper and lower bounds on…

PredDiff measures prediction changes while marginalizing features, offering new insights into interaction effects.

GADGET framework decomposes global feature effects using recursive partitioning.

New method estimates causal effects with multi-valued, time-varying treatments.

Framework identifies causal direction from single data setting.

Bayesian approach sparsifies neural networks efficiently.

We give polynomial-time algorithms for the exact computation of lowest-energy (ground) states, worst margin violators, log partition functions, and marginal edge probabilities in certain binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it …

In unsupervised domain adaptation, it is widely known that the target domain error can be provably reduced by having a shared input representation that makes the source and target domains indistinguishable from each other. Very recently it has been studied that not just matching the marginal input distributions, but th…

DAIS improves AIS for differentiable marginal likelihood estimation.

As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should…

New framework for estimating treatment effects in observational studies.

New method controls error in low-dimensional marginals of spatial models.

Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning. However, Monte Carlo sampli…

RaSE screens variables via random subspaces, identifying joint effects.