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Found 9 papers, 0 papers with code
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a pre-specified time window.
Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation
Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control).
Learning Optimal Policies from Observational Data
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others.
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features.
Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks
We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits.
Data-Driven Online Decision Making with Costly Information Acquisition
In most real-world settings such as recommender systems, finance, and healthcare, collecting useful information is costly and requires an active choice on the part of the decision maker.
Predicting Grades
We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years.
Global Bandits
In the case in which rewards of all arms are deterministic functions of a single unknown parameter, we construct a greedy policy that achieves {\em bounded regret}, with a bound that depends on the single true parameter of the problem.
Global Bandits with Holder Continuity
Specifically, we prove that the parameter-free (worst-case) regret is sublinear in time, and decreases with the informativeness of the arms.
