Search Results for author: Siddhartha Sen

Found 10 papers, 5 papers with code

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

1 code implementation NeurIPS 2021 Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson

We present a transformer-based approach to behavioral stylometry in the context of chess, where one attempts to identify the player who played a set of games.

Decision Making

Mimetic Models: Ethical Implications of AI that Acts Like You

no code implementations19 Jul 2022 Reid McIlroy-Young, Jon Kleinberg, Siddhartha Sen, Solon Barocas, Ashton Anderson

An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression.

Text Generation

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

1 code implementation20 Jun 2022 JinKun Lin, Anqi Zhang, Mathias Lecuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen

Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.

Causal Inference

Learning Models of Individual Behavior in Chess

1 code implementation23 Aug 2020 Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson

AI systems that can capture human-like behavior are becoming increasingly useful in situations where humans may want to learn from these systems, collaborate with them, or engage with them as partners for an extended duration.

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

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