Search Results for author:
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
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?
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)
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.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$.
Contextual Memory Trees
We design and study a Contextual Memory Tree (CMT), a learning memory controller that inserts new memories into an experience store of unbounded size.
A Reductions Approach to Fair Classification
We present a systematic approach for achieving fairness in a binary classification setting.
Efficient Online Bandit Multiclass Learning with O(sqrt{T}) Regret
An efficient bandit algorithm for $\sqrt{T}$-regret in online multiclass prediction?
Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret
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
We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not).
Online Gradient Boosting
We extend the theory of boosting for regression problems to the online learning setting.
Optimal and Adaptive Algorithms for Online Boosting
We study online boosting, the task of converting any weak online learner into a strong online learner.
Learning Reductions that Really Work
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.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable to linear learning?
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable to linear learning?
Conditional Probability Tree Estimation Analysis and Algorithms
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.
Agnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works without keeping a version space.
The Offset Tree for Learning with Partial Labels
We show that the Offset Tree is an optimal reduction to binary classification.
