Search Results for author: Ivan Vendrov

Found 6 papers, 3 papers with code

Opportunities and Risks of LLMs for Scalable Deliberation with Polis

no code implementations20 Jun 2023 Christopher T. Small, Ivan Vendrov, Esin Durmus, Hadjar Homaei, Elizabeth Barry, Julien Cornebise, Ted Suzman, Deep Ganguli, Colin Megill

In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements.

Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors

2 code implementations6 Feb 2022 Christina Göpfert, Alex Haig, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Hubert Pham, Mohammad Ghavamzadeh, Craig Boutilier

Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).

Recommendation Systems

What are you optimizing for? Aligning Recommender Systems with Human Values

no code implementations22 Jul 2021 Jonathan Stray, Ivan Vendrov, Jeremy Nixon, Steven Adler, Dylan Hadfield-Menell

We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy.

Fairness Recommendation Systems

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

1 code implementation14 Mar 2021 Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e. g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years.

counterfactual Probabilistic Programming +1

Gradient-based Optimization for Bayesian Preference Elicitation

no code implementations20 Nov 2019 Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational.

Recommendation Systems

Order-Embeddings of Images and Language

2 code implementations19 Nov 2015 Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun

Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images.

Cross-Modal Retrieval Image Captioning +2

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