Search Results for author: Siddhartha Sen

Found 7 papers, 1 papers with code

Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess

no code implementations NeurIPS 2021 Reid McIlroy-Young, Yu Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson

The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans.

Decision Making

Sayer: Using Implicit Feedback to Optimize System Policies

no code implementations28 Oct 2021 Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins

We develop a methodology, called Sayer, that leverages implicit feedback to evaluate and train new system policies.

Data Augmentation

Learning Personalized Models of Human Behavior in Chess

no code implementations23 Aug 2020 Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson

Even when machine learning systems surpass human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable: humans may want to learn from them, they may need to collaborate with them, or they may expect them to serve as partners in an extended interaction.

Decision Making

Aligning Superhuman AI with Human Behavior: Chess as a Model System

1 code implementation2 Jun 2020 Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg, Ashton Anderson

We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way.

Decision Making

Poor Video Streaming Performance Explained (and Fixed)

no code implementations31 Dec 2018 Matvey Arye, Siddhartha Sen, Michael J. Freedman

We show that the root cause of the problem lies in the data plane, and that even a perfect control plane (ABR) algorithm is not enough to guarantee video flows their fair share of network bandwidth.

Networking and Internet Architecture

Making Contextual Decisions with Low Technical Debt

no code implementations13 Jun 2016 Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins

The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy.

Multi-Armed Bandits online learning

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