arXiv research

Improved object classification using neural networks with known and unknown features.

New method tackles unknown unknowns in machine learning.

New algorithm reduces online learning error for unknown feature distributions.

Improves machine learning models by incorporating physical laws into feature maps.

New algorithm for truncated linear regression without knowing the survival set.

In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the de…

In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features …

A new algorithm optimizes unknown functions with noisy data and unmatched features.

New method models unknown systems with hidden parameters using neural networks.

KCRL learns stable policies for nonlinear systems with formal guarantees.

TIPRDC anonymizes data features to protect privacy while retaining useful information.

Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential …

Improves estimation under model misspecification with fake features.

We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features a…

FML uses neural networks to model unknown systems accurately.

Study online linear regression with paid noise reduction.

Method preserves Hamiltonian structure for unknown systems from noisy data.

CGDL improves open set recognition by learning conditional Gaussian distributions.

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in ch…

We consider a firm that sells a large number of products to its customers in an online fashion. Each product is described by a high dimensional feature vector, and the market value of a product is assumed to be linear in the values of its features. Parameters of the valuation model are unknown and can change over time.…

Paper tackles RL in real environments using model ensembles.

Financial markets, with their vast range of different investment opportunities, can be seen as a system of many different simultaneous games with diverse and often unknown levels of risk and reward. We introduce generalizations to the classic Kelly investment game [Kelly (1956)] that incorporates these features, and us…

Paper tackles RCA in complex networks with unknown interdependencies.

Pattern recognition is a central topic in Learning Theory with numerous applications such as voice and text recognition, image analysis, computer diagnosis. The statistical set-up in classification is the following: we are given an i.i.d. training set $(X_{1},Y_{1}),... (X_{n},Y_{n})$ where $X_{i}$ represents a feature…

Study agnostic feature-based dynamic pricing models with linear policies and noisy valuations.

Paper proposes a loss extension for neural networks to improve OSR performance.

Paper introduces privacy-preserving inventory policy learning for feature-based newsvendor with unknown demand.

We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to …

We present a max-margin nonparametric latent feature model, which unites the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction and automatically infer the unknown latent social dimension. By minimizing a hinge-loss using the linear expectation operat…

We present a method for finding high density, low-dimensional structures in noisy point clouds. These structures are sets with zero Lebesgue measure with respect to the $D$-dimensional ambient space and belong to a $d<D$ dimensional space. We call them "singular features." Hunting for singular features corresponds to f…

Improves sparse recovery with non-linear Fourier features.

Proposes a new framework for open set recognition using conditional probabilistic generative models.

In dictionary learning, also known as sparse coding, the algorithm is given samples of the form $y = Ax$ where $x\in \mathbb{R}^m$ is an unknown random sparse vector and $A$ is an unknown dictionary matrix in $\mathbb{R}^{n\times m}$ (usually $m > n$, which is the overcomplete case). The goal is to learn $A$ and $x$. T…

New DL approach reveals feature construction in dense samples.

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

The paper finds non-Gaussian directions in high-dimensional data using Wasserstein distance.

Consider a two-class clustering problem where we observe $X_i = \ell_i μ+ Z_i$, $Z_i \stackrel{iid}{\sim} N(0, I_p)$, $1 \leq i \leq n$. The feature vector $μ\in R^p$ is unknown but is presumably sparse. The class labels $\ell_i\in\{-1, 1\}$ are also unknown and the main interest is to estimate them. We are interested …

A new exploration strategy for contextual bandits reduces regret and is computationally efficient.

Unified framework for feature-based explanations using ANOVA and game theory.

Proposes a framework for extracting consistent physiological features across users.

Study builds a classifier for diffusions with unknown diffusion but known drifts.

Estimates system parameters from a single observation using kernel-based score.

We tackle linear bandits with partially observable features, achieving sublinear regret.

Paper studies vertex correspondence recovery in correlated graphs with node features.

Study uncovers statistical optimality of nonconvex tensor completion methods.

New method estimates treatment effects in complex interference settings.

We investigate the problem of classification in the presence of unknown class-conditional label noise in which the labels observed by the learner have been corrupted with some unknown class dependent probability. In order to obtain finite sample rates, previous approaches to classification with unknown class-conditiona…

End-to-end framework learns new classes dynamically.