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Found 27 papers, 15 papers with code
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces
Whenever a clinician reflects on the efficacy of a sequence of treatment decisions for a patient, they may try to identify critical time steps where, had they made different decisions, the patient's health would have improved.
Reinforcement Learning Under Algorithmic Triage
Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other.
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time.
Group Testing under Superspreading Dynamics
Testing is recommended for all close contacts of confirmed COVID-19 patients.
Differentiable Learning Under Triage
However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood.
Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively
Our randomized controlled trial also reveals that the learners whose study sessions are optimized using machine learning are $\sim$50% more likely to return to the app within 4-7 days.
Classification Under Human Assistance
Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Decisions, Counterfactual Explanations and Strategic Behavior
In this paper, our goal is to find policies and counterfactual explanations that are optimal in terms of utility in such a strategic setting.
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes
Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions.
Regression Under Human Assistance
In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels.
Can A User Anticipate What Her Followers Want?
As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback.
Optimal Decision Making Under Strategic Behavior
Using this characterization, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time and there are cases in which deterministic policies are suboptimal.
Fair Decisions Despite Imperfect Predictions
In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.
Non-submodular Function Maximization subject to a Matroid Constraint, with Applications
Then, we show that the same greedy algorithm offers a constant approximation factor of $(1 + 1/(1-\alpha))^{-1}$, where $\alpha$ is the generalized curvature of the function.
Stochastic Optimal Control of Epidemic Processes in Networks
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.
Deep Reinforcement Learning of Marked Temporal Point Processes
In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes.
Steering Social Activity: A Stochastic Optimal Control Point Of View
At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users.
On the Complexity of Opinions and Online Discussions
Sports, and the Newsroom app suggest that unidimensional opinion models may often be unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.
NeVAE: A Deep Generative Model for Molecular Graphs
Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates.
Optimizing Human Learning
Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition?
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking.
Uncovering the Dynamics of Crowdlearning and the Value of Knowledge
In this paper, we present a probabilistic modeling framework of crowdlearning, which uncovers the evolution of a user's expertise over time by leveraging other users' assessments of her contributions.
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time.
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.
Smart broadcasting: Do you want to be seen?
Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them.
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
Can we recover the hidden network structures from these observed cascades?
