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.
Gradient boosting predicts promotion efficiency using multiple performance indicators.
Exact solutions reveal how unbalanced initializations promote rapid feature learning in neural networks.
To address three important issues involved in latent variable models (LVMs), including capturing infrequent patterns, achieving small-sized but expressive models and alleviating overfitting, several studies have been devoted to "diversifying" LVMs, which aim at encouraging the components in LVMs to be diverse. Most exi…
Distance metric learning (DML), which learns a distance metric from labeled "similar" and "dissimilar" data pairs, is widely utilized. Recently, several works investigate orthogonality-promoting regularization (OPR), which encourages the projection vectors in DML to be close to being orthogonal, to achieve three effect…
iPromoter-BnCNN identifies and classifies six types of sigma promoters.
Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliab…
New framework promotes reproducible, domain-agnostic reinforcement learning algorithms.
The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its…
Dynamic promotion optimization for e-commerce platforms within financial constraints.
Composite convex optimization problems which include both a nonsmooth term and a low-rank promoting term have important applications in machine learning and signal processing, such as when one wishes to recover an unknown matrix that is simultaneously low-rank and sparse. However, such problems are highly challenging t…
Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influ…
SAM improves deep learning tasks by promoting balancedness, reducing outlier impact.
In this paper we will promote the 3D index of an ideal triangulation T of an oriented cusped 3-manifold M (a collection of q-series with integer coefficients, introduced by Dimofte-Gaiotto-Gukov) to a topological invariant of oriented cusped hyperbolic 3-manifolds. To achieve our goal we show that (a) T admits an index…
Paper develops a method to learn optimal sparsity-promoting regularizers for linear inverse problems.
NARD extends ARD for linear models, promoting sparsity and correlation structure.
A variation of the Minority Game has been applied to study the timing of promotional actions at retailers in the fast moving consumer goods market. The underlying hypotheses for this work are that price promotions are more effective when fewer than average competitors do a promotion, and that a promotion strategy can b…
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble…
Adaptive sampling method improves efficiency in complex target distributions.
Paper proposes LC-Checkpoint for efficient deep learning model checkpoints.
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
MineRL Competition reduced reinforcement learning sample needs.
Paper extends KPCA using dualization for faster, more robust algorithms.
Net-Promoter Score (NPS) is now ubiquitous as an easily-collected market research metric, having displaced many serious market research processes. Unfortunately, this has been its sole success. It possesses few, if any, of the characteristics that might be regarded as highly desirable in a high-level market research me…
Accelerates machine learning algorithms for sparse data.
ParK efficiently solves kernel ridge regression for large datasets.
SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.
Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection fin…
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
This study proposes hidden state curiosity to enhance RL models' resilience against noise.
Paper addresses LSTM stability for thermal systems using infinity-norm.
Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity more strongly than convex regularization, it is also standard practice to employ non-convex optimization. In this paper, we take a third approach. We utilize a no…
We compute the equivariant -homology of the classifying space for proper actions, for compact 3-dimensional hyperbolic reflection groups. This coincides with the topological -theory of the reduced -algebra associated to the group, via the Baum-Connes conjecture. We show that, for any such reflection group…
Prior knowledge on properties of a target model often come as discrete or combinatorial descriptions. This work provides a unified computational framework for defining norms that promote such structures. More specifically, we develop associated tools for optimization involving such norms given only the orthogonal proje…
This study analyzes how colonial rice trade in prewar Japan affected its rice market, considering several government interventions in the two rice futures exchanges in Tokyo and Osaka. We explore the interventions in the futures markets using two procedures. First, we measure the joint degree of efficiency in the marke…
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings. Existing approaches towards finding effic…
Model analyzes RFQ markets using stochastic control to optimize dealer performance and inventory.
IAGAN method improves medical image reconstruction by incorporating adaptive GAN priors.
This paper studies the interrelation between spot and futures prices in the two major rice markets in prewar Japan from the perspective of market efficiency. Applying a non-Bayesian time-varying model approach to the fundamental equation for spot returns and the futures premium, we detect when efficiency reductions in …
Promotes spectral functionals to noncommutative fields and proves a theorem.
Study finds inventory inaccuracies are linked to store activity and product perishability.
SHHK Stock Connect increases A-H share price premium, more for less efficient markets.
This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.
PePR scores assess DL model performance per resource unit, promoting smaller, more efficient models.
We propose a new class of convex penalty functions, called \emph{variational Gram functions} (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space. These functions can serve as regularizers in convex optimization problems arising from hierarchical classification, m…
This work investigates fundamental questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResNet, Inception, DenseNet and MobileNet indicate that weights near the center of a filter are larger than weights on the outside. Current regu…
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also …
Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work w…
In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regulariza- tion approach that encourages the supports of weight vectors in RL models to have small …