6 citations · 26 across the 22 of their papers we have counts for
22 papers
TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Ability
Vincent Cohen-Addad, Dimitris Paparas, Ernest van Wijland +13
We introduce TCS-Bench, a benchmark for evaluating Large Language Models (LLMs) on research-level Theoretical Computer Science (TCS) proof generation. TCS-Bench consists of theorem…
Towards Automating Scientific Review with Google's Paper Assistant Tool
Rajesh Jayaram, Drew Tyler, David Woodruff +4
Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acc…
Accelerating Scientific Research with Gemini: Case Studies and Common Techniques
David P. Woodruff, Vincent Cohen-Addad, Lalit Jain +33
Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks,…
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
Approximating High-Dimensional Earth Mover's Distance as Fast as Closest Pair
Lorenzo Beretta, Vincent Cohen-Addad, Rajesh Jayaram +1
We give a reduction from -approximate Earth Mover's Distance (EMD) to -approximate Closest Pair (CP). As a consequence, we improve the fastest kno…
Scalable contribution bounding to achieve privacy
Vincent Cohen-Addad, Alessandro Epasto, Jason Lee +1
In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bou…
Private Training & Data Generation by Clustering Embeddings
Felix Zhou, Samson Zhou, Vahab Mirrokni +2
Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this proc…
Efficient Data Selection at Scale via Influence Distillation
Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh +1
Effective data selection is critical for efficient training of modern Large Language Models (LLMs). This paper introduces Influence Distillation, a novel, mathematically-justified…
Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto +1
In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to…
Dynamic Correlation Clustering in Sublinear Update Time
Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori +1
We study the classic problem of correlation clustering in dynamic node streams. In this setting, nodes are either added or randomly deleted over time, and each node pair is connect…
Multi-View Stochastic Block Models
Vincent Cohen-Addad, Tommaso d'Orsi, Silvio Lattanzi +1
Graph clustering is a central topic in unsupervised learning with a multitude of practical applications. In recent years, multi-view graph clustering has gained a lot of attention…
Perturb-and-Project: Differentially Private Similarities and Marginals
Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto +2
We revisit the input perturbations framework for differential privacy where noise is added to the input and the result is then projected back to the space of adm…
A Scalable Algorithm for Individually Fair K-means Clustering
MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto +1
We present a scalable algorithm for the individually fair (, )-clustering problem introduced by Jung et al. and Mahabadi et al. Given points in a metric space, let $δ…
Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams
Ainesh Bakshi, Vincent Cohen-Addad, Samuel B. Hopkins +2
Metric embeddings are a widely used method in algorithm design, where generally a ``complex'' metric is embedded into a simpler, lower-dimensional one. Historically, the theoretica…
Multi-Swap -Means++
Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi +1
The -means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular -means clustering objective and is know…
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian +2
Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approxi…
Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi +4
We introduce general tools for designing efficient private estimation algorithms, in the high-dimensional settings, whose statistical guarantees almost match those of the best know…
Scalable Differentially Private Clustering via Hierarchically Separated Trees
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi +5
We study the private -median and -means clustering problem in dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algor…
Improved Approximations for Euclidean -means and -median, via Nested Quasi-Independent Sets
Vincent Cohen-Addad, Hossein Esfandiari, Vahab Mirrokni +1
Motivated by data analysis and machine learning applications, we consider the popular high-dimensional Euclidean -median and -means problems. We propose a new primal-dual alg…
Near-Optimal Correlation Clustering with Privacy
Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi +4
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more. In the…
Correlation Clustering in Constant Many Parallel Rounds
Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović +3
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining. In correlation clustering, one receives as input a signed graph an…
Fast and Accurate -means++ via Rejection Sampling
Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard +2
-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance. Despite its wide a…