Communication Complexity of Collision

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Confidence Matters: Applications to Semantic Segmentation

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Unambiguous DNFs and Alon-Saks-Seymour

Mika Tapani Göös, Siddhartha Jain

We exhibit an unambiguous k-DNF formula that requires CNF width (Omega) over tilde (k(2)), which is optimal up to logarithmic factors. As a consequence, we get a near-optimal solution to the Alon-Saks-Seymour problem in graph theory (posed in 1991), which ...

IEEE COMPUTER SOC2022

Proofs, Circuits, and Communication

Mika Tapani Göös

We survey lower-bound results in complexity theory that have been obtained via newfound interconnections between propositional proof complexity, boolean circuit complexity, and query/communication complexity. We advocate for the theory of total search prob ...

2022

As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensus is easy!

Rachid Guerraoui, Jovan Komatovic, Pierre Philippe Civit, Vincent Gramoli, Seth Gilbert

It is known that the agreement property of the Byzantine consensus problem among n processes can be violated in a non-synchronous system if the number of faulty processes exceeds t0 = ┌n/3┐ − 1 [10], [19]. In this paper, we investigate the accountable Byza ...

IEEE2022

Minimum Cost Intervention Design for Causal Effect Identification

Negar Kiyavash, Sina Akbari, Seyed Jalal Etesami

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...

PMLR2022

Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

Lower Bounds for Unambiguous Automata via Communication Complexity

Mika Tapani Göös, Weiqiang Yuan

We use results from communication complexity, both new and old ones, to prove lower bounds for unambiguous finite automata (UFAs). We show three results. 1) Complement: There is a language L recognised by an n-state UFA such that the complement language ̅L ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2022

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Thomas Jean Debarre

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Overcoming decision paralysis—A digital twin for decision making in energy system design

François Maréchal, Julia Granacher, Tuong-Van Nguyen

The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of ...

2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

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