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.
Bottom-up algorithms outperform top-down in hierarchical community detection at intermediate levels.
Paper introduces algorithms for private decision tree learning.
Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees, and formulate learning as posterior inference given data---have been shown to produce competitive performance. Unlike c…
Attention mechanism combines bottom-up and top-down signals in neural networks.
The paper tackles hierarchical clustering with structural constraints, providing approximation guarantees and improving upon current techniques.
CNT leverages noisy targets to guide model learning.
Efficient algorithm for evaluating hierarchical classification methods at multiple operating points.
Efficient unsupervised training and inference in deep generative models remains a challenging problem. One basic approach, called Helmholtz machine, involves training a top-down directed generative model together with a bottom-up auxiliary model used for approximate inference. Recent results indicate that better genera…
We derive a convex optimization problem for the task of segmenting sequential data, which explicitly treats presence of outliers. We describe two algorithms for solving this problem, one exact and one a top-down novel approach, and we derive a consistency results for the case of two segments and no outliers. Robustness…
Extends PCVM for multi-class classification with improved accuracy.
We propose a top-down model for cash CLO. This model can consistently price cash CLO tranches both within the same deal and across different deals. Meaningful risk measures for cash CLO tranches can also be defined and computed. This method is self-consistent, easy to implement and computationally efficient. It has the…
In the top-down approach to multi-name credit modeling, calculation of singe name sensitivities appears possible, at least in principle, within the so-called random thinning (RT) procedure which dissects the portfolio risk into individual contributions. We make an attempt to construct a practical RT framework that enab…
DAGGER controls FDR on DAGs in sequential testing.
A new network controls task execution in a single vision system.
Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Networks (CNNs). This work seeks to explore a path by which top-down information can have a direct impact within current deep networks. We explore …
A new method to learn EBM in latent space for better data modeling.
The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibl…
End-to-end deep model for coherent probabilistic forecasts in hierarchical time series.
Large-scale classification of data where classes are structurally organized in a hierarchy is an important area of research. Top-down approaches that exploit the hierarchy during the learning and prediction phase are efficient for large scale hierarchical classification. However, accuracy of top-down approaches is poor…
Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…
New method for summarizing ranking distributions using consensus ranking distributions.
We show that a generative random field model, which we call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multi-category classification and assuming one of the categories is a base category generated by a reference distribution. If we further assume that the…
Study improves forecasting of aggregated curves in electricity markets.
Paper uses LLMs for sector allocation, showing better returns.
Unified determinants via a single equation.
NFM improves deep learning by selectively processing hidden states.
A clustering algorithm for natural hierarchical clusters with near-linear time complexity.
Evolutionary algorithms improve decision tree ensembles.
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions, so that a top-down sensing procedure can identify these partitions, and hence the activa…
Reduces high granularity and dimensionality in hierarchical categorical variables.
A new method for feature fusion in U-Net decoders using difference-based gating.
We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constra…
Study explores robust Orlicz spaces in finance, showing separability implications.
The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo…
Iterative models improve inference efficiency in deep latent variable models.
Develops a new method to recover large latent tree models efficiently.
New learning rules from information bottleneck improve deep learning without precise labels.
A new method learns latent space normalizing flow for approximate inference in generator models.
A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of th…
A neural network solves dictionary learning problems efficiently.
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on …
Generalized Earley parser predicts future events from sequence data.
Proposes a new algorithm for accurate tree-based models with guaranteed recourse actions.
A new multi-phase approach improves supply chain forecasting accuracy.
RLHC uses multiple critics at different levels to enhance RL performance.
A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic {\em unpooling} is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-…
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, i…
Study finds almost contact structures in thermal QCD-like theories at intermediate coupling.