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.
Logit dynamics formula reveals self-regulation in softmax policy gradient methods.
Analyzes how inclusion/exclusion from STOXX Europe 600 Index affects company prices.
Logit regularization induces logit clustering, affecting classifier performance.
Paper presents a privacy-preserving method for dynamic assortment selection.
Improves adversarial robustness by constraining logits with a bounded function.
New algorithm reduces regret in dynamic assortment selection.
Study optimizes dynamic product selection and pricing using censored preference feedback.
Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.
A new logit model derived from the Weibull manifold.
We study a stylized dynamic assortment planning problem during a selling season of finite length . At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the selle…
Proposes SOVR loss to improve adversarial robustness by increasing logit margins.
Study dynamic assortment and positioning of products with varying display effects.
Logit-link models reveal socio-temporal effects on microfinance delinquency.
Generative classifiers have been shown promising to detect illegal inputs including adversarial examples and out-of-distribution samples. Supervised Deep Infomax~(SDIM) is a scalable end-to-end framework to learn generative classifiers. In this paper, we propose a modification of SDIM termed SDIM-\emph{logit}. Instead …
In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question (Athalye et …
Dynamic assortment problem on two-sided platform with unknown parameters
Proposes a convex model for mixed logit to handle individual heterogeneity.
Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods they propose - Clean Logit Pairing (CLP) and Logit Squeezing (LSQ) - just make the gradient-based optimization problem of crafting adversarial …
MANO normalizes logits to estimate test accuracy without labels.
New method uses low logit rank to simplify complex language models.
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional …
Logit distance bounds representational similarity of models.
Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a -cardinality subset from candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution…
This paper explains how low-precision arithmetic causes loss spikes in deep learning models.
We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Since all the utility parameters of MNL are unknown, the selle…
We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is a binary classification task of determining whether the given example belongs to a specific class, where the class of interest can be different each time the classifier is applied. For instance, …
LAWN normalizes logits to improve deep network adaptability and generalization.
We study the dynamical behavior of high-frequency data from the Korean Stock Price Index (KOSPI) using the movement of returns in Korean financial markets. The dynamical behavior for a binarized series of our models is not completely random. The conditional probability is numerically estimated from a return series of K…
New algorithm reduces switching costs in multinomial logit bandit problems.
Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers' preferences through offering recommendations, which may contain existing products and new prod…
We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models…
ELM improves neural model embeddings for long-tail learning.
New method distills cloud models into edge-friendly ones.
Annealing Double-Head calibrates deep neural networks during training.
Paper tackles long-tailed labels in classification problems.
In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model. We prove a tight lower bound on the accumulated regret that matches existing regret upper bounds for all parameters (time horizon , number of items and maximum assortment capacity )…
Deep learning classifiers are known to be vulnerable to adversarial examples. A recent paper presented at ICML 2019 proposed a statistical test detection method based on the observation that logits of noisy adversarial examples are biased toward the true class. The method is evaluated on CIFAR-10 dataset and is shown t…
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel. To address this non-conjugacy concern, we propose the application of Pólygamma data a…
The paper proposes an efficient method to scale Bayesian inference for mixed multinomial logit models to very large datasets.
Proposes a new model for context-dependent decision-making.
Logit correction improves model performance by correcting spurious correlations.
This article presents a proof of the existence of Bertrand-Nash equilibrium prices with multi-product firms and under the Logit model of demand that does not rely on restrictive assumptions on product characteristics, firm homogeneity or symmetry, product costs, or linearity of the utility function. The proof is based …
We consider the dynamic assortment optimization problem under the multinomial logit model (MNL) with unknown utility parameters. The main question investigated in this paper is model mis-specification under the -contamination model, which is a fundamental model in robust statistics and machine learning. In…
The paper models network formation using mixed logit models.
The paper calibrates uncertainty in dropout variational inference models.
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven…
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
A new network model combines features of DCBM, LSM, and β-model, using a cancellation trick for parameter estimation.