1 code implementation • 16 Jan 2024 • Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi
In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.
1 code implementation • NeurIPS 2023 • Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
Distributed representations provide a vector space that captures meaningful relationships between data instances.
2 code implementations • 17 Oct 2023 • Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.
no code implementations • 15 Sep 2022 • Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
no code implementations • 24 Feb 2022 • Sharib Ali, Noha Ghatwary, Debesh Jha, Ece Isik-Polat, Gorkem Polat, Chen Yang, Wuyang Li, Adrian Galdran, Miguel-Ángel González Ballester, Vajira Thambawita, Steven Hicks, Sahadev Poudel, Sang-Woong Lee, Ziyi Jin, Tianyuan Gan, Chenghui Yu, Jiangpeng Yan, Doyeob Yeo, Hyunseok Lee, Nikhil Kumar Tomar, Mahmood Haithmi, Amr Ahmed, Michael A. Riegler, Christian Daul, Pål Halvorsen, Jens Rittscher, Osama E. Salem, Dominique Lamarque, Renato Cannizzaro, Stefano Realdon, Thomas de Lange, James E. East
Polyps are well-known cancer precursors identified by colonoscopy.
no code implementations • 19 Oct 2021 • Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar
In a nutshell, we use the large teacher models to guide the lightweight student models to only make correct predictions on a subset of "easy" examples; for the "hard" examples, we fall-back to the teacher.
no code implementations • 29 Sep 2021 • Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar
In a nutshell, we use the large teacher models to guide the lightweight student models to only make correct predictions on a subset of "easy" examples; for the "hard" examples, we fall-back to the teacher.
no code implementations • 14 Jun 2021 • Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das
Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy.
no code implementations • 14 Apr 2021 • Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
no code implementations • 30 Dec 2020 • Wei Xiang Lim, ZhiYuan Chen, Amr Ahmed, Tissa Chandesa, Iman Liao
Diabetes is a global epidemic and it is increasing at an alarming rate.
no code implementations • 15 Dec 2020 • Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias Seeger, Cédric Archambeau
To democratize access to machine learning systems, it is essential to automate the tuning.
no code implementations • 15 Dec 2020 • Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
no code implementations • 1 Dec 2020 • Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Mohammad Ghavamzadeh, Craig Boutilier
The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models.
2 code implementations • 22 Oct 2020 • Nicholas Monath, Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gokhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, YuAn Wang, Yuchen Wu
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
no code implementations • 15 Sep 2020 • Xinyuan Zhang, Ruiyi Zhang, Manzil Zaheer, Amr Ahmed
High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task.
11 code implementations • NeurIPS 2020 • Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Ranked #1 on Text Classification on Arxiv HEP-TH citation graph
no code implementations • NeurIPS 2020 • Joey Hong, Branislav Kveton, Manzil Zaheer, Yin-Lam Chow, Amr Ahmed, Craig Boutilier
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state.
no code implementations • 15 Jun 2020 • Joey Hong, Branislav Kveton, Manzil Zaheer, Yin-Lam Chow, Amr Ahmed
This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment.
no code implementations • ICLR 2021 • Paul Pu Liang, Manzil Zaheer, Yu-An Wang, Amr Ahmed
In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix.
no code implementations • 25 Sep 2019 • Paul Pu Liang, Manzil Zaheer, YuAn Wang, Amr Ahmed
Learning continuous representations of discrete objects such as text, users, and items lies at the heart of many applications including text and user modeling.
no code implementations • ICLR 2018 • Xun Zheng, Manzil Zaheer, Amr Ahmed, Yu-An Wang, Eric P. Xing, Alexander J. Smola
Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
no code implementations • ICML 2017 • Manzil Zaheer, Amr Ahmed, Alexander J. Smola
Recurrent neural networks, such as long-short term memory (LSTM) networks, are powerful tools for modeling sequential data like user browsing history (Tan et al., 2016; Korpusik et al., 2016) or natural language text (Mikolov et al., 2010).
no code implementations • ICML 2017 • Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola
In this work, we propose Canopy, a sampler based on Cover Trees that is exact, has guaranteed runtime logarithmic in the number of atoms, and is provably polynomial in the inherent dimensionality of the underlying parameter space.
no code implementations • WSDM 2017 • Chao-yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing
Recommender systems traditionally assume that user profiles and movie attributes are static.
no code implementations • 6 Dec 2015 • Chao-yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola
With this novel technique we propose a new Bayesian model for joint collaborative filtering of ratings and text reviews through a sum of simple co-clusterings.
no code implementations • 21 Oct 2015 • Aaron Q. Li, Amr Ahmed, Mu Li, Vanja Josifovski
Latent variable models have accumulated a considerable amount of interest from the industry and academia for their versatility in a wide range of applications.
no code implementations • 31 Dec 2014 • Alex Beutel, Amr Ahmed, Alexander J. Smola
Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data.
no code implementations • NeurIPS 2012 • Amr Ahmed, Sujith Ravi, Alex J. Smola, Shravan M. Narayanamurthy
Clustering is a key component in data analysis toolbox.