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.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
Transformer models improve arithmetic accuracy with number decomposition.
Framework fine-tunes foundation models with semi-supervised learning for downstream tasks and latent spaces.
The paper uses tensor decompositions to improve neural network models for tree data.
Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization and MAP tasks, for which a large class of efficient and convergent variational algorithms, such as dual decomposition, exist. In this work,…
New RL approach speeds up training across tasks.
RID framework quantifies and regularizes task-relevant knowledge in distillation.
Paper proposes a novel MTL framework for personalized modeling of diverse individuals.
Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing. We study orthogonal outer product decompositions where the factors in the summands in the decomposition are required to be orthogonal across summands, by relating this orthogonal decomposition to the…
Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achie…
Paper introduces a new principle for fair redistribution of insurance surplus.
While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number o…
This work introduces a bias-variance decomposition for proper scores, improving uncertainty estimation in predictive models.
Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks. By reusing the corresponding sub-policies within and between tasks, they provide training data for each policy from different high-level tasks and compose them to perform novel ones. E…
This paper finds a new method for decomposing insurer profits and losses.
This work introduces a method to decompose uncertainty in in-context learning for large language models.
Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches. Decomposition of an environment into interacting controllable and non-controllable objects allows supervised learning for non-controllable objects and universal value function approximator learning for controllable obje…
New method uses random decompositions for high-dimensional Bayesian optimization.
In many areas of machine learning, it becomes necessary to find the eigenvector decompositions of large matrices. We discuss two methods for reducing the computational burden of spectral decompositions: the more venerable Nystom extension and a newly introduced algorithm based on random projections. Previous work has c…
VDA improves disentanglement of latent representations in complex signals.
OneShotSTL efficiently decomposes time series online, improving speed and accuracy.
Adaptive tensor modeling preserves continuity in multidimensional data.
This work is devoted to elaboration on the idea to use block term decomposition for group data analysis and to raise the possibility of modelling group activity with (Lr, 1) and Tucker blocks. A new generalization of block tensor decomposition was considered in application to group data analysis. Suggested approach was…
New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.
We consider the problem of decomposing a multivariate polynomial as the difference of two convex polynomials. We introduce algebraic techniques which reduce this task to linear, second order cone, and semidefinite programming. This allows us to optimize over subsets of valid difference of convex decompositions (dcds) a…
We present an algorithm, Decision-Directed Data Decomposition (D4), which decomposes a dataset into two components. The first contains most of the useful information for a specified supervised learning task. The second orthogonal component contains little information about the task but retains associations and informat…
In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferable for tasks such as …
SWoTTeD discovers hidden temporal patterns in EHR data.
A new method uses CPD to efficiently model feature interactions in non-sequential data.
Proposes a method to predict responses from covariates over time.
We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces hand-designed decompositions…
Unified methods for fast column selection in various applications.
We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes sequences with variable lengthed output units as a function of both the input sequence and the output sequence. We present a training algorithm which samples valid extensions and an approximate decoding algorithm. We experiment with the Wall …
Adaptive algorithm learns tensor network structures from data.
New method for uncertainty analysis in TabPFN, a state-of-the-art tabular transformer.
KEDformer improves long-term time series forecasting with seasonal-trend decomposition.
Tensor train (TT) decomposition provides a space-efficient representation for higher-order tensors. Despite its advantage, we face two crucial limitations when we apply the TT decomposition to machine learning problems: the lack of statistical theory and of scalable algorithms. In this paper, we address the limitations…
Paper predicts multiple types of miRNA-disease associations using tensor decomposition.
Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena. How these models are able to perform these remarkable feats so well, however, is still an open question. To gain more insight into what information LSTMs base their decisions on, we pro…
Paper introduces Modular Jets for diagnosing model decompositions in pipelines.
New Fourier analysis method for non-uniform Boolean hypercube.
The paper tackles scalability issues in Graph Representation Learning.
Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to specific data. This approach has proven to be very effective in many image processing tasks. Traditionally, the di…
A common machine learning task is to discriminate between normal and anomalous data points. In practice, it is not always sufficient to reach high accuracy at this task, one also would like to understand why a given data point has been predicted in a certain way. We present a new principled approach for one-class SVMs …
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather ch…
This work adapts RDT for mental program construction, showing benefits and costs.
This paper evaluates how well neural models can solve complex tasks by breaking them into simpler ones.
Revisits orbital minimization for neural operator decomposition.