695 citations · 2.6k across the 40 of their papers we have counts for
43 papers
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement
Igor L. Markov
Reinforcement learning (RL) for physical design of silicon chips in a Google 2021 Nature paper stirred controversy due to poorly documented claims that raised eyebrows and drew cri…
Ever more optimized simulations of fermionic systems on a quantum computer
Qingfeng Wang, Ze-Pei Cian, Ming Li +2
Despite using a novel model of computation, quantum computers break down programs into elementary gates. Among such gates, entangling gates are the most expensive. In the context o…
Scalable End-to-End ML Platforms: from AutoML to Self-serve
Igor L. Markov, Pavlos A. Apostolopoulos, Mia R. Garrard +8
ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of…
Looper: An end-to-end ML platform for product decisions
Igor L. Markov, Hanson Wang, Nitya Kasturi +16
Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems.…
Text Ranking and Classification using Data Compression
Nitya Kasturi, Igor L. Markov
A well-known but rarely used approach to text categorization uses conditional entropy estimates computed using data compression tools. Text affinity scores derived from compressed…
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
Graham Cormode, Igor L. Markov
Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of so…