arXiv research

A scalable MOGP model with stochastic variational inference for many outputs.

DeepICMGP surrogate models multiple outputs efficiently.

Sig-PCA integrates model outputs and observations to correct model biases.

Unified framework for output analysis using Monte Carlo sampling.

The paper presents a novel approach to multi-output regression using probabilistic circuits.

Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other. A primary issue that arises in the context of MOD learning is that for any given input pattern there can be multiple correct output patterns. This changes the…

RePULSe improves language model alignment by reducing undesired outputs without sacrificing overall performance.

Enhances robustness of MOGP regression for multiple correlated outputs.

Proposes GPLFR for predicting high-dimensional outputs with few data.

Models such as Sequence-to-Sequence and Image-to-Sequence are widely used in real world applications. While the ability of these neural architectures to produce variable-length outputs makes them extremely effective for problems like Machine Translation and Image Captioning, it also leaves them vulnerable to failures o…

New model predicts multiple outputs with missing labels.

Auto-regressive sequence-to-sequence models with attention mechanism have achieved state-of-the-art performance in many tasks such as machine translation and speech synthesis. These models can be difficult to train. The standard approach, teacher forcing, guides a model with reference output history during training. Th…

Enhances fairness in multi-output models using optimal transport.

Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for …

Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regr…

Proposes SHORE model for efficient MOR with sparsity and scalability.

Wide Boosting improves GB's performance on multivariate output tasks.

This paper introduces a multi-output Gaussian process for censored data.

Reverse sensitivity analysis for risk models under various stresses.

We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valued Gaussian process prior to jointly model the parameters in all likelihoods as latent functions. Our multi-output Gaussian process uses a c…

Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making in real-world applications. Inspired by big data, the 4Vs characteristics of multi-output imposes a set of challenges to multi-output learn…

Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued rando…

This study compares multivariate vs univariate machine learning for multi-output regression.

Proves bounds on copyright risk for generative models.

Proposes a deep ordinal regression framework using optimal transport loss and unimodal output probabilities.

Conformal Alignment ensures trustworthy outputs from foundation models.

Bayesian kernel regression improves functional output prediction.

Surrogate model construction for vector-valued outputs

Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, classification, and modeling motion capture data. While much progress has been made in training non-conditional RBMs, these algorithms are not a…

Study shows limitations and possibilities of learning quantum circuit output distributions.

For many important problems the quantity of interest is an unknown function of the parameters, which is a random vector with known statistics. Since the dependence of the output on this random vector is unknown, the challenge is to identify its statistics, using the minimum number of function evaluations. This problem …

In many applications of supervised learning, multiple classification or regression outputs have to be predicted jointly. We consider several extensions of gradient boosting to address such problems. We first propose a straightforward adaptation of gradient boosting exploiting multiple output regression trees as base le…

Recurrent neural networks and sequence to sequence models require a predetermined length for prediction output length. Our model addresses this by allowing the network to predict a variable length output in inference. A new loss function with a tailored gradient computation is developed that trades off prediction accur…

In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output. The proposed method provides new acquisition functions for minimizing the error between the desired structured-output and the prediction of a Gaussian process model, by effect…

MO-GP models fill gaps in biophysical data with across-domain info transfer.

The paper explores the identifiability and interpretability of Gaussian process models using different kernel structures.

This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initial…

Study efficient interactive learning for structured outputs with reliable computation.

Graph-to-Tree Neural Networks improve structured input-output translation in tasks like semantic parsing and math word problems.

EPFGNN models graph connections for better node classification.

Proposes an online model for LLM cascading with adaptive API selection.

Automatically identifies geometric flat outputs for robotic systems.

Unified study of nine multi-output conformal methods with generalized scores.

For the task of generating complex outputs such as source code, editing existing outputs can be easier than generating complex outputs from scratch. With this motivation, we propose an approach that first retrieves a training example based on the input (e.g., natural language description) and then edits it to the desir…

LLMs produce volatile sentence-level sentiment classifications that affect financial decision-making.

New framework learns physics from output measurements only.

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new in…

Safe active learning for multi-output Gaussian processes reduces data acquisition costs and ensures safety.