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Found 47 papers, 10 papers with code
Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics
We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses.
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts
We present the TherapyView, a demonstration system to help therapists visualize the dynamic contents of past treatment sessions, enabled by the state-of-the-art neural topic modeling techniques to analyze the topical tendencies of various psychiatric conditions and deep learning-based image generation engine to provide a visual summary.
A Survey on Compositional Generalization in Applications
The field of compositional generalization is currently experiencing a renaissance in AI, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical compositional generalization problem.
Dynamic Bandits with an Auto-Regressive Temporal Structure
We formalize this by characterizing a per-round regret lower bound, where the regret is measured against a strong (dynamic) benchmark.
Working Alliance Transformer for Psychotherapy Dialogue Classification
As a predictive measure of the treatment outcome in psychotherapy, the working alliance measures the agreement of the patient and the therapist in terms of their bond, task and goal.
Survey on Applications of Neurosymbolic Artificial Intelligence
In recent years, the Neurosymbolic framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance.
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time.
Targeted Advertising on Social Networks Using Online Variational Tensor Regression
The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner.
Neural Topic Modeling of Psychotherapy Sessions
In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings.
Deep Annotation of Therapeutic Working Alliance in Psychotherapy
The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment.
Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget
In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
Reinforcement Learning with Algorithms from Probabilistic Structure Estimation
In some cases, the environment is not influenced by the actions of the RL agent, in which case the problem can be modeled as a contextual multi-armed bandit and lightweight myopic algorithms can be employed.
Etat de l'art sur l'application des bandits multi-bras
The Multi-armed bandit offer the advantage to learn and exploit the already learnt knowledge at the same time.
Predicting human decision making in psychological tasks with recurrent neural networks
Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions, and theory of mind, i. e., what others are thinking.
Double-Linear Thompson Sampling for Context-Attentive Bandits
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to choose which variables to observe.
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations
The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and image data sources.
Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling
Spectral clustering has shown a superior performance in analyzing the cluster structure.
Computing the Dirichlet-Multinomial Log-Likelihood Function
Dirichlet-multinomial (DMN) distribution is commonly used to model over-dispersion in count data.
Contextual Bandit with Missing Rewards
We consider a novel variant of the contextual bandit problem (i. e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the reward associated with each context-based decision may not always be observed("missing rewards").
Online learning with Corrupted context: Corrupted Contextual Bandits
We consider a novel variant of the contextual bandit problem (i. e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the context used at each decision may be corrupted ("useless context").
Solving Constrained CASH Problems with ADMM
The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.
Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior
As an important psychological and social experiment, the Iterated Prisoner's Dilemma (IPD) treats the choice to cooperate or defect as an atomic action.
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL
Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an environment to maximize some notion of cumulative reward.
Hyper-parameter Tuning for the Contextual Bandit
We study here the problem of learning the exploration exploitation trade-off in the contextual bandit problem with linear reward function setting.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry
Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing.
Split Q Learning: Reinforcement Learning with Two-Stream Rewards
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit
We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms.
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.
A Survey on Practical Applications of Multi-Armed and Contextual Bandits
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback.
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society.
Multi-Objective Reinforcement Learning
reinforcement-learning
Incorporating Behavioral Constraints in Online AI Systems
To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints.
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function.
Contextual Bandit with Adaptive Feature Extraction
Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.
Scalable Recollections for Continual Lifelong Learning
Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings.
Bandit Models of Human Behavior: Reward Processing in Mental Disorders
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Context Attentive Bandits: Contextual Bandit with Restricted Context
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration.
Multi-armed Bandit Problem with Known Trend
We consider a variant of the multi-armed bandit model, which we call multi-armed bandit problem with known trend, where the gambler knows the shape of the reward function of each arm but not its distribution.
A Neural Networks Committee for the Contextual Bandit Problem
This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards.
Freshness-Aware Thompson Sampling
In this sense, we propose to study the freshness of the user's content in CARS through the bandit problem.
Exponentiated Gradient Exploration for Active Learning
Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model.
R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems
Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration).
Recommandation mobile, sensible au contexte de contenus évolutifs: Contextuel-E-Greedy
We introduce in this paper an algorithm named Contextuel-E-Greedy that tackles the dynamicity of the user's content.
Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits
We present Exponentiated Gradient LINUCB, an algorithm for con-textual multi-armed bandits.
Hybrid Q-Learning Applied to Ubiquitous recommender system
Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users.
