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What if we had built a prediction model with a survival super learner instead of a Cox model 10 years ago?

Autor: Chatton, Arthur, Pilote, Émilie, Feugo, Kevin Assob, Cardinal, Héloïse, Platt, Robert W., Schnitzer, Mireille E

Objective: This study sought to compare the drop in predictive performance over time according to the modeling approach (regression versus machine learning) used to build a kidney transplant failure prediction model with a time-to-event outcome. Stud

Externí odkaz: http://arxiv.org/abs/2412.10252

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Regression trees for nonparametric diagnostics of sequential positivity violations in longitudinal causal inference

Autor: Chatton, Arthur, Schomaker, Michael, Luque-Fernandez, Miguel-Angel, Platt, Robert W., Schnitzer, Mireille E.

Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it usually relies on multiple parametric prop

Externí odkaz: http://arxiv.org/abs/2412.10245

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Towards a uniqueness theorem for static black holes in Kaluza-Klein theory with small circle size

Autor: Albertini, Emma, Platt, Daniel, Wiseman, Toby

Kaluza-Klein theory, by which we mean vacuum gravity in 5-dimensions, with asymptotics that are a product of a circle with Minkowski spacetime, has a variety of different static black hole solutions; localized black holes and the homogeneous and inho

Externí odkaz: http://arxiv.org/abs/2410.20967

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On-Robot Reinforcement Learning with Goal-Contrastive Rewards

Autor: Biza, Ondrej, Weng, Thomas, Sun, Lingfeng, Schmeckpeper, Karl, Kelestemur, Tarik, Ma, Yecheng Jason, Platt, Robert, van de Meent, Jan-Willem, Wong, Lawson L. S.

Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-robot runtime, due to inefficient exploration when learning from a spar

Externí odkaz: http://arxiv.org/abs/2410.19989

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Multi-view biomedical foundation models for molecule-target and property prediction

Autor: Suryanarayanan, Parthasarathy, Qiu, Yunguang, Sethi, Shreyans, Mahajan, Diwakar, Li, Hongyang, Yang, Yuxin, Eyigoz, Elif, Saenz, Aldo Guzman, Platt, Daniel E., Rumbell, Timothy H., Ng, Kenney, Dey, Sanjoy, Burch, Myson, Kwon, Bum Chul, Meyer, Pablo, Cheng, Feixiong, Hu, Jianying, Morrone, Joseph A.

Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we d

Externí odkaz: http://arxiv.org/abs/2410.19704

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Low Energy Backgrounds and Excess Noise in a Two-Channel Low-Threshold Calorimeter

We describe observations of low energy excess (LEE) events (background events observed in all light dark matter direct detection calorimeters) and noise in a two-channel silicon athermal phonon detector with 375 meV baseline energy resolution. We mea

Externí odkaz: http://arxiv.org/abs/2410.16510

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Design Studies Of A Pulsed Quasimonoenergetic 2-keV Neutron Source For Calibration Of Low Threshold Dark Matter Detectors

We describe design studies for a pulsed quasi-monoenergetic 2-keV neutron source for calibration of sub-keV nuclear recoils. Such a calibration is required for detectors sensitive to sub-GeV dark matter and also the coherent elastic scattering of rea

Externí odkaz: http://arxiv.org/abs/2410.14722

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MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies

Autor: Huang, Haojie, Liu, Haotian, Wang, Dian, Walters, Robin, Platt, Robert

Many manipulation tasks require the robot to rearrange objects relative to one another. Such tasks can be described as a sequence of relative poses between parts of a set of rigid bodies. In this work, we propose MATCH POLICY, a simple but novel pipe

Externí odkaz: http://arxiv.org/abs/2409.15517

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Equivariant Reinforcement Learning under Partial Observability

Autor: Nguyen, Hai, Baisero, Andrea, Klee, David, Wang, Dian, Platt, Robert, Amato, Christopher

Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient l

Externí odkaz: http://arxiv.org/abs/2408.14336

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