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Found 21 papers, 2 papers with code
Uniform Convergence of Rank-weighted Learning
In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning.
Uniform Convergence of Rank-weighted Learning
In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning.
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors.
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework
Large language models (LLMs) can easily generate biased and discriminative responses.
Personalized Language Modeling from Personalized Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is the current dominating framework to fine-tune large language models to better align with human preferences.
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits
While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users.
Off-Policy Risk Assessment in Markov Decision Processes
To mitigate these problems, we incorporate model-based estimation to develop the first doubly robust (DR) estimator for the CDF of returns in MDPs.
Supervised Learning with General Risk Functionals
Standard uniform convergence results bound the generalization gap of the expected loss over a hypothesis class.
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity.
Modeling Attrition in Recommender Systems with Departing Bandits
We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal.
Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19
To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group.
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
Off-Policy Risk Assessment in Contextual Bandits
Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data.
On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk
Because optimizing the coherent risk is difficult in Markov decision processes, recent work tends to focus on the Markov coherent risk (MCR), a time-consistent surrogate.
Median Optimal Treatment Regimes
In this work, we propose a new median optimal treatment regime that instead treats individuals whose conditional median is higher under treatment.
Rebounding Bandits for Modeling Satiation Effects
Psychological research shows that enjoyment of many goods is subject to satiation, with short-term satisfaction declining after repeated exposures to the same item.
Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.
Automated Dependence Plots
To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model.
On Human-Aligned Risk Minimization
The statistical decision theoretic foundations of modern machine learning have largely focused on the minimization of the expectation of some loss function for a given task.
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions.
Sample Complexity of Nonparametric Semi-Supervised Learning
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions.
