Search Results for author: Alexander Moreno

Found 11 papers, 2 papers with code

Debiased Machine Learning and Network Cohesion for Doubly-Robust Differential Reward Models in Contextual Bandits

no code implementations11 Dec 2023 Easton K. Huch, Jieru Shi, Madeline R. Abbott, Jessica R. Golbus, Alexander Moreno, Walter H. Dempsey

In addition, these approaches did not explicitly model the baseline reward, which limited the ability to precisely estimate the parameters in the differential reward model.

Multi-Armed Bandits Thompson Sampling

REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning

1 code implementation1 Nov 2023 Maxwell A. Xu, Alexander Moreno, Hui Wei, Benjamin M. Marlin, James M. Rehg

The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful information for subsequent downstream tasks.

Contrastive Learning Retrieval +1

KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned Stochastic Optimization

no code implementations30 May 2023 Jonathan Mei, Alexander Moreno, Luke Walters

Second order stochastic optimizers allow parameter update step size and direction to adapt to loss curvature, but have traditionally required too much memory and compute for deep learning.

Stochastic Optimization

SKI to go Faster: Accelerating Toeplitz Neural Networks via Asymmetric Kernels

no code implementations15 May 2023 Alexander Moreno, Jonathan Mei, Luke Walters

For the low rank component, we replace the RPE MLP with linear interpolation and use asymmetric Structured Kernel Interpolation (SKI) (Wilson et.

PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation

1 code implementation14 Dec 2022 Maxwell A. Xu, Alexander Moreno, Supriya Nagesh, V. Burak Aydemir, David W. Wetter, Santosh Kumar, James M. Rehg

The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions.

Imputation

Kernel Deformed Exponential Families for Sparse Continuous Attention

no code implementations1 Nov 2021 Alexander Moreno, Supriya Nagesh, Zhenke Wu, Walter Dempsey, James M. Rehg

Theoretically, we show new existence results for both kernel exponential and deformed exponential families, and that the deformed case has similar approximation capabilities to kernel exponential families.

Transformers for prompt-level EMA non-response prediction

no code implementations1 Nov 2021 Supriya Nagesh, Alexander Moreno, Stephanie M. Carpenter, Jamie Yap, Soujanya Chatterjee, Steven Lloyd Lizotte, Neng Wan, Santosh Kumar, Cho Lam, David W. Wetter, Inbal Nahum-Shani, James M. Rehg

The transformer model achieves a non-response prediction AUC of 0. 77 and is significantly better than classical ML and LSTM-based deep learning models.

iSurvive: An Interpretable, Event-time Prediction Model for mHealth

no code implementations ICML 2017 Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg

We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.

Survival Analysis

Automatic Variational ABC

no code implementations28 Jun 2016 Alexander Moreno, Tameem Adel, Edward Meeds, James M. Rehg, Max Welling

Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models.

Variational Inference

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