Search Results for author: Smitha Milli

Found 14 papers, 3 papers with code

Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems

no code implementations27 May 2023 Smitha Milli, Emma Pierson, Nikhil Garg

Many recommender systems are based on optimizing a linear weighting of different user behaviors, such as clicks, likes, shares, etc.

Recommendation Systems

Causal Inference Struggles with Agency on Online Platforms

no code implementations19 Jul 2021 Smitha Milli, Luca Belli, Moritz Hardt

Our results suggest that observational studies derived from user self-selection are a poor alternative to randomized experimentation on online platforms.

Causal Inference

From Optimizing Engagement to Measuring Value

no code implementations21 Aug 2020 Smitha Milli, Luca Belli, Moritz Hardt

Most recommendation engines today are based on predicting user engagement, e. g. predicting whether a user will click on an item or not.

Reward-rational (implicit) choice: A unifying formalism for reward learning

no code implementations NeurIPS 2020 Hong Jun Jeon, Smitha Milli, Anca D. Dragan

It is often difficult to hand-specify what the correct reward function is for a task, so researchers have instead aimed to learn reward functions from human behavior or feedback.

Value-laden Disciplinary Shifts in Machine Learning

no code implementations3 Dec 2019 Ravit Dotan, Smitha Milli

As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values.

BIG-bench Machine Learning Decision Making +1

Strategic Classification is Causal Modeling in Disguise

no code implementations ICML 2020 John Miller, Smitha Milli, Moritz Hardt

Moreover, we show a similar result holds for designing cost functions that satisfy the requirements of previous work.

Causal Inference Classification +2

Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

no code implementations9 Mar 2019 Smitha Milli, Anca D. Dragan

In this work, we focus on misspecification: we argue that robots might not know whether people are being pedagogic or literal and that it is important to ask which assumption is safer to make.

The Social Cost of Strategic Classification

no code implementations25 Aug 2018 Smitha Milli, John Miller, Anca D. Dragan, Moritz Hardt

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule.

Classification Decision Making +2

Model Reconstruction from Model Explanations

no code implementations13 Jul 2018 Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt

We show through theory and experiment that gradient-based explanations of a model quickly reveal the model itself.

Inverse Reward Design

1 code implementation NeurIPS 2017 Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca Dragan

When designing the reward, we might think of some specific training scenarios, and make sure that the reward will lead to the right behavior in those scenarios.

Interpretable and Pedagogical Examples

no code implementations ICLR 2018 Smitha Milli, Pieter Abbeel, Igor Mordatch

Teachers intentionally pick the most informative examples to show their students.

Should Robots be Obedient?

1 code implementation28 May 2017 Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell

We show that when a human is not perfectly rational then a robot that tries to infer and act according to the human's underlying preferences can always perform better than a robot that simply follows the human's literal order.

Cannot find the paper you are looking for? You can Submit a new open access paper.