Search Results for author:
Found 14 papers, 3 papers with code
On the Importance of Uncertainty in Decision-Making with Large Language Models
We compare this baseline to LLM bandits that make active use of uncertainty estimation by integrating the uncertainty in a Thompson Sampling policy.
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards.
Fast Interactive Search with a Scale-Free Comparison Oracle
A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?''
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
In this paper, we study the identification and estimation of long-term treatment effects when both experimental and observational data are available.
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective
We study the problem of optimizing a recommender system for outcomes that occur over several weeks or months.
A Strong Baseline for Batch Imitation Learning
Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making.
A User Study of Perceived Carbon Footprint
We propose a statistical model to understand people's perception of their carbon footprint.
Scalable and Efficient Comparison-based Search without Features
We consider the problem of finding a target object $t$ using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair $(i, j)$ is more similar to $t$?"}.
Pairwise Comparisons with Flexible Time-Dynamics
Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics.
Can Who-Edits-What Predict Edit Survival?
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project.
ChoiceRank: Identifying Preferences from Node Traffic in Networks
We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities.
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions
In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models.
Fast and Accurate Inference of Plackett–Luce Models
We show that the maximum-likelihood (ML) estimate of models derived from Luce's choice axiom (e. g., the Plackett-Luce model) can be expressed as the stationary distribution of a Markov chain.
Just Sort It! A Simple and Effective Approach to Active Preference Learning
We address the problem of learning a ranking by using adaptively chosen pairwise comparisons.
