arXiv research

LISBET automates social behavior analysis using machine learning.

Study uses contrastive learning to analyze market order behavior.

Paper develops a framework for learning interpretable representations of sequential decision behavior.

Method uses DNNs to approximate functions with specific asymptotic behavior.

Behavior modification improves prediction accuracy by nudging user behavior.

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. …

The paper introduces new metrics for evaluating generative models of behavior.

BL learns interpretable optimization structures from data.

New method learns behavioral representations from mobility data.

Deep learning analyzes healthcare provider actions and patient outcomes.

We introduce a new approach for comparing reinforcement learning policies, using Wasserstein distances (WDs) in a newly defined latent behavioral space. We show that by utilizing the dual formulation of the WD, we can learn score functions over policy behaviors that can in turn be used to lead policy optimization towar…

This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.

DETECT clusters mobility behaviors from trajectories using deep learning.

Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors. Understanding the relation between an individual's behavioral patterns and psychological states can help identify strategies to improve quality of life. One challenge in analyzing physiological d…

This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for th…

Model predicts human food choices based on demographics.

Paper uses RL for high-level character control in 3D environments.

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging…

This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-known that techniques based on Learning from Demonstrations, for example behavior cloning, can lead to proficient policies given limited data…

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of age…

The article presents methods to select models from behavioral learning data, with applications to contextual bandits.

Reinforcement learning mimics expert behavior.

Dual behavior policy improves reinforcement learning across various environments.

To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we empl…

Study uses ML to analyze financial behavior in big data.

Introduces LoCA regret to evaluate model-based RL methods.

Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a …

A major bottleneck for developing general reinforcement learning agents is determining rewards that will yield desirable behaviors under various circumstances. We introduce a general mechanism for automatically specifying meaningful behaviors from raw pixels. In particular, we train a generative adversarial network to …

The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…

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 …

A test measures artificial agents' human-like behavior in video games.

Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment. With that in mind, maintaining a population of agents is an attractive method, as it allows data be collected with a diverse set of behaviors. This behavioral diversity is often boosted via mul…

SafeMIL learns safer policies by avoiding risky behavior from non-preferred trajectories.

We propose a novel approach to train a multi-modal policy from mixed demonstrations without their behavior labels. We develop a method to discover the latent factors of variation in the demonstrations. Specifically, our method is based on the variational autoencoder with a categorical latent variable. The encoder infer…

PWIL learns agent behavior from expert using Wasserstein distance.

Model assesses credit risk using behavioral data from Experian and Bank of Italy.

Method learns evolving policies in healthcare contexts.

We study the problem of controllable generation of long-term sequential behaviors, where the goal is to calibrate to multiple behavior styles simultaneously. In contrast to the well-studied areas of controllable generation of images, text, and speech, there are two questions that pose significant challenges when genera…

Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of th…

Study optimizes health incentives to balance efficiency and fairness.

Study uses RL to simulate realistic market behavior.

Behavior Transfer improves reinforcement learning by leveraging pre-trained policies.

We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed …

We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers. Each group of users is modeled as a rationally inattentive Bayesian agent which solves a contextual bandit problem. Our methodology integrates t…

The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.

Study uses deep learning to predict stock trends with superior performance.

RL agent learns to avoid market spoofing.