A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method learns output embeddings for structured prediction.
The paper presents a novel approach to multi-output regression using probabilistic circuits.
Gradient flow in parameters equals linear interpolation in outputs.
E2M predicts metric space outputs using deep learning.
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an int…
State-space systems generate probabilistic dependencies between inputs and outputs.
We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) problem using operator-valued kernels. We show that some of the existing formulations of this problem are special cases of our framework. We then propose a c…
A new method quantizes output space for multi-target regression.
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…
We consider a framework for structured prediction based on search in the space of complete structured outputs. Given a structured input, an output is produced by running a time-bounded search procedure guided by a learned cost function, and then returning the least cost output uncovered during the search. This framewor…
A new conformal prediction framework for graph-valued outputs using Z-Gromov-Wasserstein distances.
The report analyzes infinite-dimensional output space regression.
Operator-Valued Kernels (OVKs) and associated vector-valued Reproducing Kernel Hilbert Spaces provide an elegant way to extend scalar kernel methods when the output space is a Hilbert space. Although primarily used in finite dimension for problems like multi-task regression, the ability of this framework to deal with i…
A new method reduces both input and output dimensions for better goal-oriented analysis.
Study efficient neural operator learning using variation spaces.
Sig-PCA integrates model outputs and observations to correct model biases.
Deep neural networks for structured prediction using kernel-induced losses.
Combines neural networks with variational inference for better uncertainty quantification.
We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output spac…
Study efficient interactive learning for structured outputs with reliable computation.
When learning a mapping from an input space to an output space, the assumption that the sample distribution of the training data is the same as that of the test data is often violated. Unsupervised domain shift methods adapt the learned function in order to correct for this shift. Previous work has focused on utilizing…
We propose and analyze a novel theoretical and algorithmic framework for structured prediction. While so far the term has referred to discrete output spaces, here we consider more general settings, such as manifolds or spaces of probability measures. We define structured prediction as a problem where the output space l…
FGBoost boosts gradient boosting for complex data.
We consider the problem of retrieving the most relevant labels for a given input when the size of the output space is very large. Retrieval methods are modeled as set-valued classifiers which output a small set of classes for each input, and a mistake is made if the label is not in the output set. Despite its practical…
This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces. For classification, a common approach is to define prototypes as the mean output vector over training examples per class. Here, we propose to use hyperspheres as output spa…
Bayesian kernel regression improves functional output prediction.
Predicting structured outputs can be computationally onerous due to the combinatorially large output spaces. In this paper, we focus on reducing the prediction time of a trained black-box structured classifier without losing accuracy. To do so, we train a speedup classifier that learns to mimic a black-box classifier u…
Optimal estimator derived for partially observable LTI systems.
Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested questions, the testing nodes, leading to a set of predictions, th…
Contrast uses normalizing flows to create precise prediction regions for multi-dimensional outputs.
Unified approach to structured prediction combining entropy regularization and neuro-symbolic logic.
We study algebraic varieties of ReLU networks to understand their representable functions.
We consider a neural network architecture with randomized features, a sign-splitter, followed by rectified linear units (ReLU). We prove that our architecture exhibits robustness to the input perturbation: the output feature of the neural network exhibits a Lipschitz continuity in terms of the input perturbation. We fu…
JES optimizes expensive functions by considering joint entropy over input and output spaces.
Efficiently approximates uncertainty in classification models using Dirichlet distributions.
Paper connects sampling and labeling biases in large-output spaces.
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…
New recursive algorithm estimates conditional kernel mean embeddings in Hilbert space.
Paper introduces vector-valued variation spaces for multi-output neural networks.
Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.
A new method reduces high-dimensional parameter spaces for faster numerical tasks.
A method constructs a stochastic surrogate from dimensionality reduction results for high-dimensional uncertainty quantification.
Deep single-index Fréchet regression for metric space-valued outputs
Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the o…
We propose a general approach for supervised learning with structured output spaces, such as combinatorial and polyhedral sets, that is based on minimizing estimated conditional risk functions. Given a loss function defined over pairs of output labels, we first estimate the conditional risk function by solving a (possi…
Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through probabilistic inference in a graphical model with constrained transitions. Recently, it has become common to locally parametrize these models…
Random feature model approximates PDE solutions efficiently.