Search Results for author: Alina Beygelzimer

Found 17 papers, 1 papers with code

Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment

no code implementations5 Jun 2023 Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan

We present the NeurIPS 2021 consistency experiment, a larger-scale variant of the 2014 NeurIPS experiment in which 10% of conference submissions were reviewed by two independent committees to quantify the randomness in the review process.

How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?

no code implementations22 Nov 2022 Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah

In a top-tier computer science conference (NeurIPS 2021) with more than 23, 000 submitting authors and 9, 000 submitted papers, we survey the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews.

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

no code implementations27 Mar 2020 Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily Fox, Hugo Larochelle

Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings.

BIG-bench Machine Learning

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

no code implementations6 Feb 2019 Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang

Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$.

Classification General Classification

Contextual Memory Trees

no code implementations17 Jul 2018 Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro

We design and study a Contextual Memory Tree (CMT), a learning memory controller that inserts new memories into an experience store of unbounded size.

General Classification Image Captioning +2

Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret

no code implementations25 Feb 2017 Alina Beygelzimer, Francesco Orabona, Chicheng Zhang

The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm of the competitor.

Search Improves Label for Active Learning

no code implementations NeurIPS 2016 Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang

We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not).

Active Learning

Online Gradient Boosting

no code implementations NeurIPS 2015 Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo

We extend the theory of boosting for regression problems to the online learning setting.


Optimal and Adaptive Algorithms for Online Boosting

no code implementations9 Feb 2015 Alina Beygelzimer, Satyen Kale, Haipeng Luo

We study online boosting, the task of converting any weak online learner into a strong online learner.

Learning Reductions that Really Work

no code implementations9 Feb 2015 Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro

We provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of problems, and show that this approach to solving machine learning problems can be broadly useful.

BIG-bench Machine Learning

Conditional Probability Tree Estimation Analysis and Algorithms

no code implementations9 Aug 2014 Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alexander L. Strehl

We consider the problem of estimating the conditional probability of a label in time O(log n), where n is the number of possible labels.


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