Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Vassilvitskii, Sergei"'
Differential privacy is the gold standard for statistical data release. Used by governments, companies, and academics, its mathematically rigorous guarantees and worst-case assumptions on the strength and knowledge of attackers make it a robust and c
Externí odkaz:
http://arxiv.org/abs/2408.07614
Autor:
Amin, Kareem, Bie, Alex, Kong, Weiwei, Kurakin, Alexey, Ponomareva, Natalia, Syed, Umar, Terzis, Andreas, Vassilvitskii, Sergei
We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction. In the private prediction framework, we only require the output synthetic data to satisfy differential privacy gua
Externí odkaz:
http://arxiv.org/abs/2407.12108
Push-Relabel is one of the most celebrated network flow algorithms. Maintaining a pre-flow that saturates a cut, it enjoys better theoretical and empirical running time than other flow algorithms, such as Ford-Fulkerson. In practice, Push-Relabel is
Externí odkaz:
http://arxiv.org/abs/2405.18568
Any social choice function (e.g the efficient allocation) can be implemented using different payment rules: first price, second price, all-pay, etc. All of these payment rules are guaranteed to have the same expected revenue by the revenue equivalenc
Externí odkaz:
http://arxiv.org/abs/2403.04856
Autor:
Isik, Berivan, Ponomareva, Natalia, Hazimeh, Hussein, Paparas, Dimitris, Vassilvitskii, Sergei, Koyejo, Sanmi
Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are
Externí odkaz:
http://arxiv.org/abs/2402.04177
We study the Densest Subgraph (DSG) problem under the additional constraint of differential privacy. DSG is a fundamental theoretical question which plays a central role in graph analytics, and so privacy is a natural requirement. All known private a
Externí odkaz:
http://arxiv.org/abs/2308.10316
The classical ski-rental problem admits a textbook 2-competitive deterministic algorithm, and a simple randomized algorithm that is $\frac{e}{e-1}$-competitive in expectation. The randomized algorithm, while optimal in expectation, has a large varian
Externí odkaz:
http://arxiv.org/abs/2308.05067
Autor:
Cummings, Rachel, Desfontaines, Damien, Evans, David, Geambasu, Roxana, Huang, Yangsibo, Jagielski, Matthew, Kairouz, Peter, Kamath, Gautam, Oh, Sewoong, Ohrimenko, Olga, Papernot, Nicolas, Rogers, Ryan, Shen, Milan, Song, Shuang, Su, Weijie, Terzis, Andreas, Thakurta, Abhradeep, Vassilvitskii, Sergei, Wang, Yu-Xiang, Xiong, Li, Yekhanin, Sergey, Yu, Da, Zhang, Huanyu, Zhang, Wanrong
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's deployment in real-world applications. Key points and high-level contents
Externí odkaz:
http://arxiv.org/abs/2304.06929
Autor:
Carey, CJ, Dick, Travis, Epasto, Alessandro, Javanmard, Adel, Karlin, Josh, Kumar, Shankar, Medina, Andres Munoz, Mirrokni, Vahab, Nunes, Gabriel Henrique, Vassilvitskii, Sergei, Zhong, Peilin
Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis t
Externí odkaz:
http://arxiv.org/abs/2304.07210
Recent work has shown that leveraging learned predictions can improve the running time of algorithms for bipartite matching and similar combinatorial problems. In this work, we build on this idea to improve the performance of the widely used Ford-Ful
Externí odkaz:
http://arxiv.org/abs/2303.00837