Machine Learning

Machine Learning, Reinforcement

HOMER: Provable Exploration in Reinforcement Learning

Last week at ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and I had a paper on a new reinforcement learning (RL) algorithm that solves three key problems in RL: (i) global exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. Our ICML poster is here. The paper is a bit mathematically heavy in nature so this …

Conferences, Machine Learning, Organization

Coronavirus and Machine Learning Conferences

I’ve been following the renamed COVID-19 epidemic closely since potential exponentials deserve that kind of attention. The last few days have convinced me it’s a good idea to start making contingency plans for machine learning conferences like ICML. The plausible options happen to be structurally aligned with calls to enable reduced travel to machine learning …

ai, deep-learning, Machine Learning

Thoughts on the BagNet Paper

Some thoughts on the interesting BagNet paper (accepted at ICLR 2019) currently being circulated around the Machine Learning Twitter Community.

Disclaimer: I wasn’t a reviewer of this paper for ICLR. I think it was worthy of acceptance to the c…

ai, finance, Machine Learning, statistics

Uncertainty: a Tutorial

A PDF version of this post can be found here.
Chinese translation by Xiaoyi Yin

Notions of uncertainty are tossed around in conversations around AI safety, risk management, portfolio optimization, scientific measurement, and insurance. Here are a few …

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