Measuring the Reliability of Reinforcement Learning Algorithms

1 code implementation • ICLR 2020 • Stephanie C. Y. Chan, Samuel Fishman, John Canny, Anoop Korattikara, Sergio Guadarrama

To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a set of metrics that quantitatively measure different aspects of reliability.

reinforcement-learning Reinforcement Learning (RL)

From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

no code implementations • ICLR 2019 • Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama

In this work, we investigate the problem of grounding language commands as reward functions using inverse reinforcement learning, and argue that language-conditioned rewards are more transferable than language-conditioned policies to new environments.

Instruction Following reinforcement-learning +1

Speed/accuracy trade-offs for modern convolutional object detectors

14 code implementations • CVPR 2017 • Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang song, Sergio Guadarrama, Kevin Murphy

On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.

Object object-detection +1

Im2Calories: Towards an Automated Mobile Vision Food Diary

no code implementations • ICCV 2015 • Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

Bayesian Dark Knowledge

1 code implementation • NeurIPS 2015 • Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling

We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e. g., for applications involving bandits or active learning.

Active Learning

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

no code implementations • 5 Mar 2015 • Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling

Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference.

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

no code implementations • 19 Apr 2013 • Anoop Korattikara, Yutian Chen, Max Welling

Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets?

Statistical Tests for Optimization Efficiency

no code implementations • NeurIPS 2011 • Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling

Learning problems such as logistic regression are typically formulated as pure optimization problems defined on some loss function.

regression