no code implementations • ICML 2020 • Pierre Perrault, Zheng Wen, Michal Valko, Jennifer Healey
We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set.
1 code implementation • EMNLP 2021 • Victor Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, Larry Birnbaum
Conversations aimed at determining good recommendations are iterative in nature.
no code implementations • 23 Apr 2024 • Wanrong Zhu, Jennifer Healey, Ruiyi Zhang, William Yang Wang, Tong Sun
Recent advancements in instruction-following models have made user interactions with models more user-friendly and efficient, broadening their applicability.
no code implementations • 5 Dec 2021 • Victor S. Bursztyn, Jennifer Healey, Vishwa Vinay
Based on recent advances in realistic language modeling (GPT-3) and cross-modal representations (CLIP), Gaud\'i was developed to help designers search for inspirational images using natural language.
no code implementations • 19 Sep 2021 • Sridhar Mahadevan, Anup Rao, Georgios Theocharous, Jennifer Healey
Many real-world applications require aligning two temporal sequences, including bioinformatics, handwriting recognition, activity recognition, and human-robot coordination.
1 code implementation • 15 Sep 2021 • Victor S. Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, Larry Birnbaum
Conversations aimed at determining good recommendations are iterative in nature.
no code implementations • 13 Apr 2021 • Victor S. Bursztyn, Jennifer Healey, Eunyee Koh, Nedim Lipka, Larry Birnbaum
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.
no code implementations • 5 Jan 2021 • Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different kind of approximation guarantee -- weaker than the classical one -- for the difference of submodular (DS) optimization problem, and vice versa.
Data Structures and Algorithms