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.
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used …
Study resource allocation strategies in sequential decisions with unknown rewards.
Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behav…
New algorithms for batch decision-making with high-dimensional user data.
Protects user privacy in models using optional personal data.
VisRuler simplifies decision extraction from bagged and boosted trees.
When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting, or a factory robot selects a worker to deliver a part. Demonstrating fairness in decision making is essential for such systems to be broad…
The paper proposes a survival model to optimize mobile notification delivery times.
Explainable AI improves human decision accuracy but does not enhance it significantly.
We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user…
New algorithms improve decision-making with limited offline data.
Interactive framework improves understanding of deep neural networks.
We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, wh…
Paper quarantines unreliable Yelp users by detecting review spam.
DECE visualizes machine learning decisions with counterfactual explanations.
Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over …
Summarizes financial news for better investment decisions.
Automated decision making is used routinely throughout our everyday life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. M…
Paper uses LightGBM for mobile user credit assessment.
Bridges uplift modeling and sequential decision-making with online budget allocation.
Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In settings where multiple robots act in separate environments, a single human operator can manage a fleet of robots by identifying and teleoperati…
A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users' experiences and cause irreversible damage. A ty…
Online purchase decisions in organizations can go through a complex journey with multiple agents involved in the decision making process. Depending on the product being purchased, and the organizational structure, the process may involve employees who first conduct market research, and then influence decision makers wh…
User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled sta…
User strategization undermines algorithmic trustworthiness.
A deep RL framework optimizes resource allocation in wireless networks.
Linguistic calibration improves long-form text confidence.
In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over t…
Study assesses whether RL algorithm personalizes treatment sequences.
Study finds users mostly use recent market and decision information to guess market direction.
We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression…
User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based …
Optimizes user marketing campaigns to balance cost and effectiveness.
Develops a measure for subjective explainability of ML predictions.
Enhanced recommender system using ensemble learning and graph embedding.
Educational game on crypto investment helps students grasp macroeconomics.
Paper uses ML to predict utility in APS dialogue outcomes.
Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI d…
New framework uses user feedback in CB problems for better decision-making.
OLPA optimizes online user-centric selection with probing, achieving near-optimal regret bounds.
IntelligentPooling improves treatment decisions in mHealth.
New approach to disentangle utility from impulse in recommendation systems.
It is oftentimes impossible to understand how machine learning models reach a decision. While recent research has proposed various technical approaches to provide some clues as to how a learning model makes individual decisions, they cannot provide users with ability to inspect a learning model as a complete entity. In…
Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural …
PinnerSage creates multi-modal user embeddings for better Pinterest recommendations.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
The paper analyzes regret in online recommendation systems with constraints.
Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pe…