arXiv research

This work provides safety guarantees for iterative GP predictions.

This work uses QPGPs to improve ILC performance in repetitive tasks.

Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and efficiency, several potential problems have yet to be addressed, particularly the potential for unintentional discrimination. We present an…

Researchers compare different gradient methods for ridge regression, finding conjugate gradients have similar performance.

Self-training in linear models shows a U-shaped test-risk curve due to signal forgetting and denoising.

New iterative methods improve scalability of Gaussian process approximations for large data.

New method predicts and optimizes matrix recovery from noisy measurements.

Efficiently compress pretrained models using RSI for improved predictive accuracy.

The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice. However, the theoretical rate at which past CFR-based algorithms converge to the Nash equilibrium is on the order of $O(T^{-1/2})$, where $T$ is the number of iterations. In contrast, first-order methods can be used to …

XLVINs improve data efficiency in implicit planning by leveraging latent space.

An iterative SE(3)-Transformer model is developed for graph data.

Develops methods for spectral estimation and rare-event prediction in complex systems.

Matrix factorization was used to generate investment recommendations for investors. An iterative conjugate gradient method was used to optimize the regularized squared-error loss function. The number of latent factors, number of iterations, and regularization values were explored. Overfitting can be addressed by either…

New iterative methods improve Vecchia-Laplace approximations for large data sets.

Refines neural network predictions using background knowledge for improved accuracy.

missForestPredict fills missing data for prediction models quickly and accurately.

A reinforcement learning method predicts medical outcomes from masked feature vectors.

New framework predicts AMP behavior in spiked models for finite iterations.

C-IP improves LLMs' query selection for interactive tasks by estimating uncertainty robustly.

Proposes a method to generate multivariate prediction intervals for random forests.

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep a…

New recommendations improve Gaussian process accuracy and stability.

Simplifies neural regression by combining two sub-networks for predictions and uncertainties.

We present the use of the fitted Q iteration in algorithmic trading. We show that the fitted Q iteration helps alleviate the dimension problem that the basic Q-learning algorithm faces in application to trading. Furthermore, we introduce a procedure including model fitting and data simulation to enrich training data as…

Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…

Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative…

A new method calibrates value predictions in offline RL to improve reliability.

Improves predictive algorithm performance with domain adaptation.

OptEx accelerates first-order optimization with parallelized iterations.

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiab…

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learni…

In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N, the n…

Deep learning ensembles improve COVID-19 detection from chest X-rays.

Stochastic algo learns from evolving data, achieving optimal performance.

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this se…

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional p…

New framework calibrates models to control risk under performativity.

Study iterated ERM in active learning, deriving test error bounds.

Improved CTR prediction with XDBoost neural network.

DSVGD improves federated learning with fewer communication rounds.

Paper tackles performative prediction without convexity assumptions.

The Poisson equation is commonly encountered in engineering, for instance in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the incompressibility of the velocity field. In the present work, we propose a novel fully convolutional neural network (CNN) architec…

We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l1-l2 norm regularizer. By systematically treating the non-differentiable regulariz…

The paper tackles performative risk optimization under weak convexity assumptions.

In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number …

IDGL learns better graph structure and embeddings iteratively.

A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of depl…

Optimal ridge regularization computed iteratively from generative parameters.