b'Daniel Russo'

research

∙ 08/30/2023

Pandemic Pedagogy: Evaluating Remote Education Strategies during COVID-19

The COVID-19 pandemic precipitated an abrupt shift in the educational la...

0 Daniel Russo, et al. ∙

research

∙ 08/29/2023

Benchmarking the Generation of Fact Checking Explanations

Fighting misinformation is a challenging, yet crucial, task. Despite the...

0 Daniel Russo, et al. ∙

research

∙ 07/19/2023

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Recommender systems are a ubiquitous feature of online platforms. Increa...

0 Thomas M. McDonald, et al. ∙

research

∙ 07/12/2023

Navigating the Complexity of Generative AI Adoption in Software Engineering

In this paper, the adoption patterns of Generative Artificial Intelligen...

0 Daniel Russo, et al. ∙

research

∙ 06/20/2023

Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization

Inventory management offers unique opportunities for reliably evaluating...

0 Matias Alvo, et al. ∙

research

∙ 02/09/2023

An Information-Theoretic Analysis of Nonstationary Bandit Learning

In nonstationary bandit learning problems, the decision-maker must conti...

0 Seungki Min, et al. ∙

research

∙ 02/07/2023

Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective

We study the problem of optimizing a recommender system for outcomes tha...

0 Lucas Maystre, et al. ∙

research

∙ 01/30/2023

On the Statistical Benefits of Temporal Difference Learning

Given a dataset on actions and resulting long-term rewards, a direct est...

0 David Cheikhi, et al. ∙

research

∙ 01/30/2023

The Double-Edged Sword of Diversity: How Diversity, Conflict, and Psychological Safety Impact Agile Software Teams

Team diversity can be seen as a double-edged sword. It brings additional...

0 Christiaan Verwijs, et al. ∙

research

∙ 03/28/2022

Recruiting Software Engineers on Prolific

Recruiting participants for software engineering research has been a pri...

0 Daniel Russo, et al. ∙

research

∙ 02/18/2022

Adaptivity and Confounding in Multi-Armed Bandit Experiments

We explore a new model of bandit experiments where a potentially nonstat...

0 Chao Qin, et al. ∙

research

∙ 12/13/2021

From Anecdote to Evidence: The Relationship Between Personality and Need for Cognition of Developers

There is considerable anecdotal evidence suggesting that software engine...

0 Daniel Russo, et al. ∙

research

∙ 11/19/2021

Understanding Developers Well-Being and Productivity: A Longitudinal Analysis of the COVID-19 Pandemic

COVID-19 has likely been the most disruptive event at a global scale the...

0 Daniel Russo, et al. ∙

research

∙ 07/16/2021

Developers Task Satisfaction and Performance during the COVID-19 Pandemic

Following the onset of the COVID-19 pandemic and subsequent lockdowns, s...

0 Daniel Russo, et al. ∙

research

∙ 07/13/2021

The Impact of Working From Home on the Success of Scrum Projects: A Multi-Method Study

The number of companies opting for remote working has been increasing ov...

0 Adrian-Alexandru Cucolas, et al. ∙

research

∙ 05/26/2021

A Theory of Scrum Team Effectiveness

Scrum teams are the most important drivers to lead an Agile project to i...

0 Christiaan Verwijs, et al. ∙

research

∙ 02/19/2021

Learning to Stop with Surprisingly Few Samples

We consider a discounted infinite horizon optimal stopping problem. If t...

0 Daniel Russo, et al. ∙

research

∙ 01/12/2021

The Daily Life of Software Engineers during the COVID-19 Pandemic

Following the onset of the COVID-19 pandemic and subsequent lockdowns, s...

0 Daniel Russo, et al. ∙

research

∙ 10/07/2020

Empirical Standards for Software Engineering Research

Empirical Standards are natural-language models of a scientific communit...

0 Paul Ralph, et al. ∙

research

∙ 07/24/2020

Predictors of Well-being and Productivity among Software Professionals during the COVID-19 Pandemic – A Longitudinal Study

The COVID-19 pandemic has forced governments worldwide to impose movemen...

0 Daniel Russo, et al. ∙

research

∙ 07/22/2020

Approximation Benefits of Policy Gradient Methods with Aggregated States

Folklore suggests that policy gradient can be more robust to misspecific...

0 Daniel Russo, et al. ∙

research

∙ 07/21/2020

A Note on the Linear Convergence of Policy Gradient Methods

We revisit the finite time analysis of policy gradient methods in the si...

0 Jalaj Bhandari, et al. ∙

research

∙ 09/04/2019

SQuAP-Ont: an Ontology of Software Quality Relational Factors from Financial Systems

Quality, architecture, and process are considered the keystones of softw...

0 Paolo Ciancarini, et al. ∙

research

∙ 06/07/2019

Worst-Case Regret Bounds for Exploration via Randomized Value Functions

This paper studies a recent proposal to use randomized value functions t...

0 Daniel Russo, et al. ∙

research

∙ 06/05/2019

Global Optimality Guarantees For Policy Gradient Methods

Policy gradients methods are perhaps the most widely used class of reinf...

0 Jalaj Bhandari, et al. ∙

research

∙ 04/09/2019

A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents

This note gives a short, self-contained, proof of a sharp connection bet...

0 Daniel Russo, et al. ∙

research

∙ 06/06/2018

A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

Temporal difference learning (TD) is a simple iterative algorithm used t...

0 Jalaj Bhandari, et al. ∙

research

∙ 03/07/2018

Satisficing in Time-Sensitive Bandit Learning

Much of the recent literature on bandit learning focuses on algorithms t...

0 Daniel Russo, et al. ∙

research

∙ 05/29/2017

Improving the Expected Improvement Algorithm

The expected improvement (EI) algorithm is a popular strategy for inform...

0 Chao Qin, et al. ∙

research

∙ 03/22/2017

Deep Exploration via Randomized Value Functions

We study the use of randomized value functions to guide deep exploration...

0 Ian Osband, et al. ∙

research

∙ 11/16/2015

How much does your data exploration overfit? Controlling bias via information usage

Modern data is messy and high-dimensional, and it is often not clear a p...

0 Daniel Russo, et al. ∙

research

∙ 06/04/2013

(More) Efficient Reinforcement Learning via Posterior Sampling

Most provably-efficient learning algorithms introduce optimism about poo...

0 Ian Osband, et al. ∙

Daniel Russo

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