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Found 16 papers, 3 papers with code
Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis
In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients.
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation
Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues.
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy.
Superhuman Fairness
The fairness of machine learning-based decisions has become an increasingly important focus in the design of supervised machine learning methods.
Fairness for Robust Learning to Rank
While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race.
Distributionally Robust Imitation Learning
We consider the imitation learning problem of learning a policy in a Markov Decision Process (MDP) setting where the reward function is not given, but demonstrations from experts are available.
Feedback in Imitation Learning: The Three Regimes of Covariate Shift
The learner often comes to rely on features that are strongly predictive of decisions, but are subject to strong covariate shift.
Robust Fairness under Covariate Shift
We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.
Fairness for Robust Log Loss Classification
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications.
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
What policy should be employed in a Markov decision process with uncertain parameters?
Efficient and Consistent Adversarial Bipartite Matching
Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations.
Adversarial Surrogate Losses for Ordinal Regression
Ordinal regression seeks class label predictions when the penalty incurred for mistakes increases according to an ordering over the labels.
Adversarial Multiclass Classification: A Risk Minimization Perspective
Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses.
Softstar: Heuristic-Guided Probabilistic Inference
Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself.
Adversarial Prediction Games for Multivariate Losses
Multivariate loss functions are used to assess performance in many modern prediction tasks, including information retrieval and ranking applications.
Robust Classification Under Sample Selection Bias
In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions.
