Search Results for author:
Found 10 papers, 3 papers with code
Towards stable real-world equation discovery with assessing differentiating quality influence
This paper explores the critical role of differentiation approaches for data-driven differential equation discovery.
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Affine correction (AC) is a generalization of IPS that corrects for position bias and trust bias.
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training.
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS.
Safe Exploration for Optimizing Contextual Bandits
Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel.
ViTOR: Learning to Rank Webpages Based on Visual Features
The visual appearance of a webpage carries valuable information about its quality and can be used to improve the performance of learning to rank (LTR).
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists.
Conversational Exploratory Search via Interactive Storytelling
In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface.
