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An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Neural Information Processing Systems http://nips.cc/
f33ba15effa5c10e873bf3842afb46a6-Paper.pdf
Typically,eachevaluationisexpensive in such applications, and we need to keep the number of evaluations to a minimum.
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors Li-wei H. Lehman
Accurate and reliable algorithms that can distinguish between true vs. false arrhythmia alarms can
Normalization Helps Training of Quantized LSTM
Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin, Tie-Yan Liu
However, existing quantization methods usually have inferior performancewhenusedonLSTMs.
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Dimitris Bertsimas, Christopher McCord
We propose a novel algorithmic framework that is tractable, asymptotically consistent,andsuperiortocomparable methodsonexampleproblems.
StepbaQ: Stepping backward as Correction for Quantized Diffusion Models
Quantization of diffusion models has attracted considerable attention due to its potential to enable various applications on resource-constrained mobile devices.
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
However, such pessimism for out-of-sample data could be too restricted and sample inefficient, as not all out-of-sample(unseen) states are not generalizable [20].