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no code implementations • 23 Feb 2021 • Eric Balkanski, Sharon Qian, Yaron Singer

A major question is therefore how to measure the performance of an algorithm in comparison to an optimal solution on instances we encounter in practice.

no code implementations • NeurIPS 2020 • Ron Kupfer, Sharon Qian, Eric Balkanski, Yaron Singer

Both the upper and lower bounds are under the assumption that queries are only on feasible sets (i. e., of size at most k).

no code implementations • NeurIPS 2020 • Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber

As a case study, we apply this methodology to analyzing gender bias in pre-trained Transformer language models.

no code implementations • 22 Oct 2020 • Eric Balkanski, Harrison Chase, Kojin Oshiba, Alexander Rilee, Yaron Singer, Richard Wang

Nevertheless, we generalize SCAR to design attacks that fool state-of-the-art check processing systems using unnoticeable perturbations that lead to misclassification of deposit amounts.

no code implementations • NeurIPS 2020 • Avinatan Hassidim, Ron Kupfer, Yaron Singer

We consider the classic problem of $(\epsilon,\delta)$-PAC learning a best arm where the goal is to identify with confidence $1-\delta$ an arm whose mean is an $\epsilon$-approximation to that of the highest mean arm in a multi-armed bandit setting.

1 code implementation • 26 Apr 2020 • Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, Stuart Shieber

Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both.

no code implementations • 21 Feb 2020 • Sharon Qian, Dimitris Kalimeris, Gal Kaplun, Yaron Singer

Despite the vast success of Deep Neural Networks in numerous application domains, it has been shown that such models are not robust i. e., they are vulnerable to small adversarial perturbations of the input.

1 code implementation • ICML 2020 • Adam Breuer, Eric Balkanski, Yaron Singer

Recent algorithms have comparable guarantees in terms of asymptotic worst case analysis, but their actual number of rounds and query complexity depend on very large constants and polynomials in terms of precision and confidence, making them impractical for large data sets.

no code implementations • ICML 2020 • Nir Rosenfeld, Kojin Oshiba, Yaron Singer

Providing users with alternatives to choose from is an essential component in many online platforms, making the accurate prediction of choice vital to their success.

1 code implementation • 6 Jun 2019 • Juan C. Perdomo, Yaron Singer

We address the challenge of designing optimal adversarial noise algorithms for settings where a learner has access to multiple classifiers.

no code implementations • ICLR 2019 • Juan C. Perdomo, Yaron Singer

The main technical challenge we consider is the design of best response oracles that can be implemented in a Multiplicative Weight Updates framework to find equilibrium strategies in the zero-sum game.

no code implementations • 9 Mar 2019 • Dimitris Kalimeris, Gal Kaplun, Yaron Singer

A recent surging research direction in influence maximization focuses on the case where the edge probabilities on the graph are not arbitrary but are generated as a function of the features of the users and a global hyperparameter.

1 code implementation • NeurIPS 2019 • Sharon Qian, Yaron Singer

Recently, there has been a surge of interest in a parallel optimization technique called adaptive sampling which produces solutions with desirable approximation guarantees for submodular maximization in exponentially faster parallel runtime.

no code implementations • NeurIPS 2018 • Yaron Singer, Avinatan Hassidim

We consider the problem of maximizing a submodular function when given access to its approximate version.

no code implementations • 27 Nov 2018 • Suproteem K. Sarkar, Kojin Oshiba, Daniel Giebisch, Yaron Singer

To the best of our knowledge, this is the first study of adversarial attacks and defenses for deep learning in financial services.

no code implementations • 12 Aug 2018 • Eric Balkanski, Yaron Singer

For the problem of minimizing a non-smooth convex function $f:[0, 1]^n\to \mathbb{R}$ over the unit Euclidean ball, we give a tight lower bound that shows that even when $\texttt{poly}(n)$ queries can be executed in parallel, there is no randomized algorithm with $\tilde{o}(n^{1/3})$ rounds of adaptivity that has convergence rate that is better than those achievable with a one-query-per-round algorithm.

no code implementations • ICML 2018 • Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg

Despite this obstacle, we can shrink the best-known sample complexity bound for learning IC by a factor of |E|/d where |E| is the number of edges in the graph and d is the dimension of the hyperparameter.

no code implementations • ICML 2018 • Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer

Submodular functions have become a ubiquitous tool in machine learning.

no code implementations • ICML 2018 • Eric Balkanski, Yaron Singer

In particular, we show that under very mild conditions of curvature of a function, adaptive sampling techniques achieve an approximation arbitrarily close to 1/2 while maintaining their low adaptivity.

no code implementations • NeurIPS 2017 • Eric Balkanski, Yaron Singer

In this paper we consider the problem of minimizing a submodular function from training data.

no code implementations • ICML 2017 • Avinatan Hassidim, Yaron Singer

In this paper we analyze the robustness of stochastic variants of the greedy algorithm for submodular maximization.

no code implementations • NeurIPS 2017 • Robert Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis

We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions.

no code implementations • NeurIPS 2016 • Thibaut Horel, Yaron Singer

We study the problem of maximizing a function that is approximately submodular under a cardinality constraint.

no code implementations • NeurIPS 2016 • Eric Balkanski, Aviad Rubinstein, Yaron Singer

In this paper we show that for any monotone submodular function with curvature c there is a (1 - c)/(1 + c - c^2) approximation algorithm for maximization under cardinality constraints when polynomially-many samples are drawn from the uniform distribution over feasible sets.

no code implementations • 12 Jan 2016 • Avinatan Hassidim, Yaron Singer

We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function.

no code implementations • 19 Dec 2015 • Eric Balkanski, Aviad Rubinstein, Yaron Singer

In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution.

no code implementations • NeurIPS 2015 • Harikrishna Narasimhan, David C. Parkes, Yaron Singer

We establish PAC learnability of influence functions for three common influence models, namely, the Linear Threshold (LT), Independent Cascade (IC) and Voter models, and present concrete sample complexity results in each case.

no code implementations • NeurIPS 2015 • Yaron Singer, Jan Vondrak

We consider the problem of optimizing convex and concave functions with access to an erroneous zeroth-order oracle.

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