arXiv research

PAC-Bayes with Backprop trains neural nets with competitive error estimates and tighter risk bounds.

Unified framework for estimating random forest prediction errors.

Paper develops an online learning algorithm for functional data models.

New robust estimator improves variable selection and coefficient estimation in linear regression with heavy-tailed errors and outliers.

Optimistic RL algorithms are simplified for deep RL with competitive performance.

Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.

Model predicts US COVID-19 deaths with quantile estimates.

Deep neural networks enforce non-crossing quantile regression curves.

Paper uses DNN-based MoM-GAN to estimate contaminated data distributions.

Improves matrix completion by exploiting biased observation patterns.

The estimation of probabilities of network edges from the observed adjacency matrix has important applications to predicting missing links and network denoising. It has usually been addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but this is ill-defined without strong a…

Study minimax off-policy evaluation in multi-armed bandits with known and unknown behavior policies.

Cross-validation pitfalls in change-point regression are addressed with new approaches.

Proposes a new method for kernel density estimation using stagewise minimization and a simple dictionary.

A neural network method estimates densities from characteristic functions.

Greedy algorithm performs well in online matching despite non-i.i.d. connections.

Paper introduces MoM-KDE for robust density estimation robust to anomalous data.

JADAI optimizes design and inference for parameter estimation.

PITMonitor monitors model calibration over time with formal error guarantees.

We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification problems. The classification tree is constructed from empirical estimates of the Henze-Penrose bounds on t…

New method gives provable error bounds for neural nets under distribution shift.

Confident learning estimates label quality by identifying and correcting noisy data.

Adapts causal inference for high-dimensional treatments like text strings.

An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small step shift and rotation operations upon selected discriminative hyperplan…

Model predicts competition between similar products in sales.

Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector $w^* \in \mathbb{R}^d$ that represents the "qualities" of the $d$ ite…

Machine learning is often used in competitive scenarios: Participants learn and fit static models, and those models compete in a shared platform. The common assumption is that in order to win a competition one has to have the best predictive model, i.e., the model with the smallest out-sample error. Is that necessarily…

New method for estimating covariance with robustness to outliers.

In this paper, we present a novel penalty approach for the numerical solution of continuously controlled HJB equations and HJB obstacle problems. Our results include estimates of the penalisation error for a class of penalty terms, and we show that variations of Newton's method can be used to obtain globally convergent…

New method estimates covariance matrices without restrictive assumptions.

We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows performance that is competitive with recent neural conditional density models and Gaussian processes. The proposed model is based on a novel …

Kernel Density Machines learn probability densities without structural assumptions.

Private density estimation in Wasserstein distance for geographic populations.

New method generates novel samples from closed-form diffusion models.

This paper explores how representation learning can improve design-based causal inference.

We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods are known to achieve complementary bias-variance trade-off properties, w…

Paper proposes an alternative to MLE for GLMs with non-canonical link functions.

Variant of mSSA improves time series prediction error.

Most recent results in matrix completion assume that the matrix under consideration is low-rank or that the columns are in a union of low-rank subspaces. In real-world settings, however, the linear structure underlying these models is distorted by a (typically unknown) nonlinear transformation. This paper addresses the…

First ABAW 2020 Competition analyzes affective behavior tasks.

An algorithm estimates the mean of a process from noisy, online sensor data.

The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full adv…

Robust forecast framework reduces distribution error by 63%.

Estimating symmetric properties of a distribution, e.g. support size, coverage, entropy, distance to uniformity, are among the most fundamental problems in algorithmic statistics. While each of these properties have been studied extensively and separate optimal estimators are known for each, in striking recent work, Ac…

A hybrid ASR system using conformer architecture improves word-error-rate and training speed.

This work improves the Euler method for masked diffusion models, providing tighter convergence guarantees.

causalKANs provides interpretable treatment effect estimates using neural networks.

We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the network and reduce processing time. Each node of the network uses a convex optimization based algorithm that provides a locally optimum soluti…