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.
Study quantifies performance gap between tensor and matrix-based approaches in nested matrix-tensor model.
Paper proposes a tensor model for clustering noisy multi-view data.
AMP algorithm for matrix tensor product model provides recovery conditions.
Efficiently factorizes coupled matrix tensor data for better accuracy and speed.
Paper finds formulas for mutual information and MMSE in matrix tensor product problems.
Paper proposes C-STM for multimodal neuroimaging data classification.
Graphical notation simplifies tensor operations and decompositions.
Gradient-based algorithms are effective for many machine learning tasks, but despite ample recent effort and some progress, it often remains unclear why they work in practice in optimising high-dimensional non-convex functions and why they find good minima instead of being trapped in spurious ones. Here we present a qu…
In this work we analyse quantitatively the interplay between the loss landscape and performance of descent algorithms in a prototypical inference problem, the spiked matrix-tensor model. We study a loss function that is the negative log-likelihood of the model. We analyse the number of local minima at a fixed distance …
Study reveals efficient recovery of multi-modal signals via Bayesian methods and sequential learning.
Nested model averaging improves high-dimensional linear regression performance.
Develops a method for learning proposals in nested importance samplers.
We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning. The new framework is a hybrid between alternating optimization (AO) and the alternating direction method of multipliers (ADMM): each matrix factor is updated in tur…
Let R be an o-minimal expansion of the real field, and let L(R) be the language consisting of all nested Rolle leaves over R. We call a set nested subpfaffian over R if it is the projection of a boolean combination of definable sets and nested Rolle leaves over R. Assuming that R admits analytic cell decomposition, we …
Nested Slice Sampling accelerates Nested Sampling for GPU acceleration.
Enhances Bayesian model selection for high-dimensional problems.
Nested sampling improved for arbitrary priors.
We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While nesting sub-models layer-wise is straight-forward with deep-supervision as proposed in \cite{xie2015holistically}, channel-wise nesting has…
Study cobordisms of nested manifolds and their invariants.
We develop nested automatic differentiation (AD) algorithms for exact inference and learning in integer latent variable models. Recently, Winner, Sujono, and Sheldon showed how to reduce marginalization in a class of integer latent variable models to evaluating a probability generating function which contains many leve…
NEST optimizes deep learning training by placing devices efficiently across networks and memory.
Nested learning improves model performance on multi-granular tasks.
Paper tackles robust model training with a new stochastic algorithm.
How can we correlate neural activity in the human brain as it responds to words, with behavioral data expressed as answers to questions about these same words? In short, we want to find latent variables, that explain both the brain activity, as well as the behavioral responses. We show that this is an instance of the C…
Quantum algorithm speeds up nested expectation estimation by nearly quadratically.
There is an increasing interest in estimating expectations outside of the classical inference framework, such as for models expressed as probabilistic programs. Many of these contexts call for some form of nested inference to be applied. In this paper, we analyse the behaviour of nested Monte Carlo (NMC) schemes, for w…
Improved nested simulation for financial risk measurement.
Scalable tools for nested optimization in deep learning.
Paper proposes a new estimator for nested expectations with faster convergence.
Unified SGD method improves convergence for nested optimization problems.
Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A tree structure is induced that recursively splits the set of classes into subsets, and a binary classification model learns to discriminate between the two subsets of classes at each node. In …
New methods for estimating complex causal effects in econometrics.
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estimators, such that terms in an outer estimator themselves involve calculation of a separate, nested, estimation. We investigate the statistica…
EENNs improve inference efficiency but need nested prediction sets for reliable uncertainty estimates.
We formalize the notion of nesting probabilistic programming queries and investigate the resulting statistical implications. We demonstrate that while query nesting allows the definition of models which could not otherwise be expressed, such as those involving agents reasoning about other agents, existing systems take …
Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which …
Generative model synthesizes complex data structures with composite and nested types.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
Gradient-guided nested sampling improves posterior inference efficiency.
Paper tackles robust optimization under uncertainty using nested distance.
A new method sorts models to find the best one with minimal risk.
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix-tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as u…
New discrete cobordism category for nested manifolds and relations to algebraic structures.
The data torrent unleashed by current and upcoming astronomical surveys demands scalable analysis methods. Many machine learning approaches scale well, but separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is often an after-th…
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each …
A new method for efficient nested Monte Carlo simulations in financial modeling.
Proposes a method for explaining black-box models with nested feature attributions.
Study of loops in sums of Laplace eigenfunctions on surfaces.