32 citations · 67 across the 9 of their papers we have counts for
13 papers
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem
Clayton Sanford, Vaggos Chatziafratis
Given a target function , how large must a neural network be in order to approximate ? Recent works examine this basic question on neural network \textit{expressivity} from t…
Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT
Vaggos Chatziafratis, Mohammad Mahdian, Sara Ahmadian
In this paper, we study a number of well-known combinatorial optimization problems that fit in the following paradigm: the input is a collection of (potentially inconsistent) local…
Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering
Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky +3
Recently, Hierarchical Clustering (HC) has been considered through the lens of optimization. In particular, two maximization objectives have been defined. Moseley and Wang defined…
Inapproximability for Local Correlation Clustering and Dissimilarity Hierarchical Clustering
Vaggos Chatziafratis, Neha Gupta, Euiwoong Lee
We present hardness of approximation results for Correlation Clustering with local objectives and for Hierarchical Clustering with dissimilarity information. For the former, we stu…
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Ines Chami, Albert Gu, Vaggos Chatziafratis +1
Similarity-based Hierarchical Clustering (HC) is a classical unsupervised machine learning algorithm that has traditionally been solved with heuristic algorithms like Average-Linka…
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems
Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas
The expressivity of neural networks as a function of their depth, width and type of activation units has been an important question in deep learning theory. Recently, depth separat…