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 framework improves adversarial robustness in one-stage L2D.
Paper proposes ARB-Loss to improve classification precision in imbalanced datasets.
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional i…
Unified model for prediction and deferral selects top-k entities efficiently.
This paper proposes a Residual Convolutional Neural Network (ResNet) based on speech features and trained under Focal Loss to recognize emotion in speech. Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients (MFCCs) have shown the ability to characterize emotion better than just plain text. Furth…
Simpler algorithm learns shallow networks faster.
Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of curren…
New model uses pretrained biochemical language models to generate drug compounds.
End-to-end kernel learning using generative RFFs for improved performance.
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single generator to perform one-stage enhancement mapping. In this work, we propose to use multiple generators that are chained to perf…
FROST speeds up and stabilizes one-shot semi-supervised learning.
Enhanced privacy, utility, and efficiency through MUST subsampling.
We present the Integrated Size and Price Optimization Problem (ISPO) for a fashion discounter with many branches. Based on a two-stage stochastic programming model with recourse, we develop an exact algorithm and a production-compliant heuristic that produces small optimality gaps. In a field study we show that a distr…
Object detection models shipped with camera-equipped edge devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized object detection system that many applications would rely on. In this paper, we present an efficient y…
Unified framework for deferring queries to top-k experts, improving accuracy-cost trade-offs.
Empirical risk minimization is the main tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are autom…
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how h…
A machine learning method for short-maturity options with jumps and stochastic volatility.
The recent application of deep learning in various areas of medical image analysis has brought excellent performance gains. In particular, technologies based on deep learning in medical image registration can outperform traditional optimisation-based registration algorithms both in registration time and accuracy. Howev…
Improves classifier performance in multi-stage selection processes.
Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine-grain analysis, users' ratings are explained from multiple perspectiv…
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To addre…
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 …
Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a c…
RLHF uses human feedback to train AI models, posing statistical challenges.
Improves classifier performance in multi-stage processes with adversarial autoencoders and multi-task learning.
Principal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage constructs a regression model whose explanatory variables are replaced by principal components obtained by the first stage. Since PCA is performed by using only explanatory var…
The paper introduces new Bayesian network classifiers for better classification accuracy.
This work extends reduction processes for nonholonomic discrete mechanical systems.
New classifiers account for context-specific independences.
In this paper, we study the assortment optimization problem faced by many online retailers such as Amazon. We develop a \emph{cascade multinomial logit model}, based on the classic multinomial logit model, to capture the consumers' purchasing behavior across multiple stages. Different from existing studies, our model a…
Proposes Contrastive Clustering for improved clustering performance.
Bayesian optimization tackles expensive cascade processes.
Paper develops a new method for distribution regression with indefinite kernels.
The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learnin…
A framework tackles model uncertainty in ALM, providing robust investment strategies.
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are regarded as new explanatory variables in the model. Note that PCA is based only on the explanatory variables, so the principal components a…
New RL algorithm reduces policy switching cost to loglog(T) with similar regret.
End-to-end model for time series classification with missing data.
Paper proposes online optimization for uncertain systems using machine learning and DRO.
Language models fail to execute simple steps, showing gating and binding errors.
A new multi-view clustering method that is fast, scalable, and easy to use.
A new framework integrates credit scoring into profit scoring for better P2P lending investments.
New algorithm achieves strong consistency in binary non-uniform hypergraph classification.
We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning and point estimation problems without analytical solution (such as hyperparameter or entropy es…
Proposes ML methods for robust price-sensitivity estimation in dynamic pricing.
Self-training outperforms pre-training on COCO object detection and segmentation datasets.
Un-trained neural networks outperform trained methods in MRI reconstruction.