most citedProvably Adversarially Robust Nearest Prototype Classifiers

4 citations · 7 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024

An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks

Valentyn Boreiko, Alexander Panfilov, Vaclav Voracek +2

A plethora of jailbreaking attacks have been proposed to obtain harmful responses from safety-tuned LLMs. These methods largely succeed in coercing the target output in their origi…

stat.ML2024

Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness

Vaclav Voracek

Randomized smoothing is a popular certified defense against adversarial attacks. In its essence, we need to solve a problem of statistical estimation which is usually very time-con…

cs.AI2024

Convergence of Some Convex Message Passing Algorithms to a Fixed Point

Vaclav Voracek, Tomas Werner

A popular approach to the MAP inference problem in graphical models is to minimize an upper bound obtained from a dual linear programming or Lagrangian relaxation by (block-)coordi…

math.QA2024★ 2 cited

Stateless Quantum Structures and Extremal Graph Theory

Vaclav Voracek

We study hypergraphs which represent finite quantum event structures. We contribute to results of graph theory, regarding bounds on the number of edges, given the number of vertice…

cs.LG2022★ 4 cited

Provably Adversarially Robust Nearest Prototype Classifiers

Václav Voráček, Matthias Hein

Nearest prototype classifiers (NPCs) assign to each input point the label of the nearest prototype with respect to a chosen distance metric. A direct advantage of NPCs is that the…

cs.LG2022★ 1 cited

Sound Randomized Smoothing in Floating-Point Arithmetics

Václav Voráček, Matthias Hein

Randomized smoothing is sound when using infinite precision. However, we show that randomized smoothing is no longer sound for limited floating-point precision. We present a simple…