Education
Query Complexity of Bayesian Private Learning
How to learn, while ensuring that a spying adversary does not learn?
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum
Much of everyday human thinking and learning can be understood in terms of program induction: constructing a procedure that maps inputs to desired outputs, based on observing example input-output pairs.
Deep Reinforcement Learning of Marked Temporal Point Processes
Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez
In doing so, we define the agent's policy using the intensity and
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Crucially, the neural model uses miniKanren's
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, Alexandre Lacoste
In the current work we aim to close this gap.
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
In this paper, we consider online F-measure optimization (OFO). Unlike traditional performance metrics (e.g., classification error rate), F-measure is non-decomposable over training examples and is a non-convex function of model parameters, making it much more difficult to be optimized in an online fashion. Most existing results of OFO usually suffer from high memory/computational costs and/or lack statistical consistency guarantee for optimizing F-measure at the population level.
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny
The complementarity between SGD and EA is worth investigating.
Distributed Stochastic Optimization via Adaptive SGD
Ashok Cutkosky, Róbert Busa-Fekete
Stochastic convex optimization algorithms are the most popular way to train machine learning models on large-scale data.
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
We aim at using a large observational sample and a possibly much smaller experimental sample.
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Luis Haug, Sebastian Tschiatschek, Adish Singla
Neural Information Processing Systems http://nips.cc/