Join Zoom Meeting
https://zoom.us/j/99715655535?pwd=L2F1MVVvcmJBakpyK1N3NHNyeG03UT09

Meeting ID: 997 1565 5535
Passcode: jls-22

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Abstract:

In parallel with the ongoing digitization, computer security has become an
increasingly important and urgent challenge. In particular, the sound and
robust implementation of complex software systems is still not well understood
in practice, as evidenced by the steady stream of successful attacks observed
in the wild. The current state of the art in software security consists of
solutions that are often technically sound, but do not provide operational
security in practice.

In this talk, I will give an overview of our recent work towards resilient and
sustainable software security, which is also the focus of my upcoming ERC
Consolidator project. On the one hand, the system must be resilient against
entire classes of attack vectors. On the other hand, the system must be
sustainable, i.e., it must be able to maintain its security at least over its
design lifetime and possibly even adapt over time. In the talk, I will discuss
some of the methods we have developed to achieve this goal, with a specific
focus on novel software testing strategies that enable accurate and efficient
vulnerability discovery. I will conclude the talk with a brief overview of
future research directions.

Presenter:

Thorsten Holz: https://cispa.de/de/people/c01thho

https://infolab.cs.uni-saarland.de/2022/03/20/schuelerwettbewerb-zur-make-it-saarland/

Abstract
Point sets and sequences are fundamental geometric objects that arise in any application that considers movement data, geometric shapes, and many more.
A crucial task on these objects is to measure their similarity.
Therefore, this thesis presents results on algorithms, complexity lower bounds, and algorithm engineering of the most important point set and sequence similarity measures like the Fréchet distance, the Fréchet distance under translation, and the Hausdorff distance under translation.
As an extension to the mere computation of similarity, also the approximate near neighbor problem for the continuous Fréchet distance on time series is considered and matching upper and lower bounds are shown.

The zoom link will be distributed via the mailing list. You can also write the speaker to obtain it.

http://domino.mpi-inf.mpg.de/internet/events.nsf/0/237B85B47B3D74DFC12588380031B256

Abstract: Quantum algorithms are useful for a variety of problems in search and
optimization. This line of work started with Grover's quantum search algorithm
which achieved a quadratic speedup over naive exhaustive search but has now
developed far beyond it.

In this talk, we describe three recent results in this area:

1. We show that, for any classical algorithm that uses a random walk to find an
object with some property (by walking until the random walker reaches such an
object), there is an almost quadratically faster quantum algorithm
(https://arxiv.org/abs/1903.07493).

2. We show that the best known exponential time algorithms for solving several
NP-complete problems (such as Travelling Salesman Problem or TSP) can be
improved quantumly (https://arxiv.org/abs/1807.05209). For example, for the
TSP, the best known classical algorithm needs time O(2^n) but our quantum
algorithm solves the problem in time O(1.728...^n).

3. We show a almost quadratic quantum speedup for a number of geometric problems
such as finding three points that are on the same line
(https://arxiv.org/abs/2004.08949).

Location https://zoom.us/j/94577321297?pwd=N3l5K1ZtZ3E1aytnWlBkL1FUazNXZz09
Meeting ID 945 7732 1297
Passcode 205903

Information about the series and recordings of previous talks can be found at
https://www.mpi-inf.mpg.de/departments/algorithms-complexity/quantum-lecture-series

Hosts: Ignacio Ciraq (MPIQ) and Kurt Mehlhorn (MPI-INF).

https://explainable-intelligent.systems/lectures/

Abstract
As machine learning (ML) is increasingly used for decision making in scenarios that impact humans, there is a growing awareness of its potential for unfairness. A large body of recent work has focused on proposing formal notions of fairness in ML, as well as approaches to mitigate unfairness. However, there is a disconnect between the ML fairness literature and the needs to operationalize fairness in practice.

This dissertation brings ML fairness closer to real-world applications by developing new models and methods that address key challenges in operationalizing fairness in practice.

*First, we tackle a key assumption in the group fairness literature that sensitive demographic attributes such as race and gender are known upfront, and can be readily used in model training to mitigate unfairness. In practice, factors like privacy and regulation often prohibit ML models from collecting or using protected attributes in decision making. To address this challenge we introduce the novel notion of computationally-identifiable errors and propose Adversarially Reweighted Learning (ARL), an optimization method that seeks to improve the worst-case performance over unobserved groups, without requiring access to the protected attributes in the dataset.

*Second, we argue that while group fairness notions are a desirable fairness criterion, they are fundamentally limited as they reduce fairness to an average statistic over pre-identified protected groups. In practice, automated decisions are made at an individual level, and can adversely impact individual people irrespective of the group statistic. We advance the paradigm of individual fairness by proposing iFair (individually fair representations), an optimization approach for learning a low dimensional latent representation of the data with two goals: to encode the data as well as possible, while removing any information about protected attributes in the transformed representation.

*Third, we advance the individual fairness paradigm, which requires that similar individuals receive similar outcomes. However, similarity metrics computed over observed feature space can be brittle, and inherently limited in their ability to accurately capture similarity between individuals. To address this, we introduce a novel notion of fairness graphs to capture nuanced expert knowledge on which individuals should be treated similarly with respect to the ML objective. We cast the problem of individual fairness into graph embedding, and propose PFR (pairwise fair representations), a method to learn a unified pairwise fair representation of the data.

*Fourth, we tackle the challenge that production data after model deployment is constantly evolving. As a consequence, in spite of the best efforts in training a fair model, ML systems can be prone to failure risks due to a variety of unforeseen failure risks. We propose Risk Advisor, a model-agnostic meta-learner to predict potential failure risks and to give guidance on the sources of uncertainty inducing the risks, by leveraging information theoretic notions of aleatoric and epistemic uncertainty.

Extensive experiments on a variety of real-world and synthetic datasets show that our proposed methods are viable in practice.

http://domino.mpi-inf.mpg.de/internet/events.nsf/0/E739258AF6B96B47C125883F004D4670

https://www.uni-saarland.de/page/tdot.html