2018 2015 2013 2012
8 February 2018

As an optimization problem, clustering exhibits a striking phenomenon: It is generally regarded as easy in practice, while theory classifies it among the computationally intractable problems. To address this dichotomy, research has identified a number of conditions a data set must satisfy for a clustering to be (1) easily computable and (2) meaningful. In this talk we show that all previously proposed notions of struturedness of a data set are fundamentally local properties, i.e. the global optimum is in well defined sense close to a local optimum. As a corollary, this implies that the Local Search heuristic has strong performance guarantees for both the tasks of recovering the underlying optimal clustering and obtaining a clustering of small cost.
Room 222 - Galleria 1 @12:00

22 February 2018

The study of online algorithms and competitive analysis provide a solid foundation for studying the quality of irrevocable decision making when the data arrives in an online manner. While in some scenarios the decisions are indeed irrevocable, there are many practical situations when changing a previous decision is not impossible, but simply expensive. In this work we formalize this notion and introduce the consistent k-clustering problem. With points arriving online, the goal is to maintain a constant approximate solution, while minimizing the number of reclusterings necessary. We prove a lower bound, showing that Ω(k log n) changes are necessary in the worst case, for a wide range of objective functions. On the positive side, we give an algorithm that needs only O(k^2 log^4 n) changes to maintain a constant competitive solution. This is an exponential improvement on the naive solution of reclustering at every time step. Finally, we show experimentally that our approach performs much better than the theoretical bound, with the number of changes growing approximately as O(log n). Joint work with Sergei Vassilvitskii.
Manno, Galleria 1, 2nd floor, room G1-204 @12:00

7 March 2018 - 7 March 2018

Many systems in the world are vast networks consisting of autonomous nodes that communicate with each other in order to jointly solve a task. One common feature of these complex networks is that due to their size it is impossible for an individual node to have a global view. Instead, nodes have to base their decisions on local information about nearby nodes only. Despite this intrinsic locality, the network as a whole is supposed to arrive at a global solution. Understanding capabilities and limitations of such local algorithms is the key challenge of distributed graph algorithms. The LOCAL model---the standard synchronous message-passing model of distributed computing introduced by Linial in 1987---is designed to abstract the pure concept of locality. In this talk, we discuss the maximal matching problem in the LOCAL model. In particular, we introduce a new rounding technique that leads to a deterministic O(log^2 Delta log n)-round algorithm on any n-node graph with maximum degree Delta. This is the first improvement in almost 20 years over the celebrated O(log^4 n)-round algorithm by Hanckowiak, Karonski, and Panconesi.
Galleria 1 - 2nd floor, room G1-204

21 March 2018

Recently, machine learning methods have started to enter the field of photonics, ranging from quantum mechanics, nanophotonics, optical communication and optical networks. Though machine learning offers many powerful techniques, linking it to optical communication may not be trivial, due to the inherent peculiarities of optical technologies. In this talk, we will discuss open challenges and benefits that machine learning methods can bring to optical communications, especially in the field of optical networks design and management.
Galleria 1, 2nd floor, room G1-204

28 March 2018 - 28 March 2018

A Bayesian network (BN) is a versatile and well-known probabilistic graphical model with applications in a variety of fields. Their graphical nature makes BNs excellent models for representing the complex probabilistic relationships existing in many real problems ranging from bioinformatics to law, from image processing to economic risk analysis. In this talk, we will present the difficult task of learning their dependency graph, also known as their structure, from data.
Manno, Galleria 1, 2nd floor, room G1-204 @12:00

29 March 2018 - 29 March 2018

Semantic networks are one of the most valuable tools in today's NLP. They power the intelligence behind conversational interfaces, help search engines answer queries, and are one the most used ways to represent the knowledge present in natural language. In this talk, I will present some of my work related to them. Firstly, we will see an example of a simple use case for semantic networks within opinion mining. I will show how we used them to build a model able to detect political disaffection in Twitter messages in Italian language. Semantic networks here helped, e.g., in distinguishing general political disaffection from a sentiment against a specific political party. We applied this model to 35 millions tweets, and – in order to validate the quality of the generated time-series – we compared our results to opinion surveys. Secondly, I will illustrate a theoretical model for semantic networks. In a semantic network, each node is usually tagged with different categories. How does the presence or absence of such categories interplay with the network link structure? I will present a model that is able to describe complex interactions between categories and links, while at the same time being simple enough to derive scalable algorithms. Finally, I will show a practical application for this model on semantic networks, presenting an algorithm to mine surprising links.
Manno, Galleria 1, room G1-204

9 April 2018 - 9 April 2018

This talk concerns the problem of quickly computing distances and shortest paths on distributed networks (the CONGEST model). There have been many developments for this problem in the last few years, resulting in tight approximation schemes. This left widely open whether exact algorithms can perform equally well. In this talk, we will discuss some recent progress in answering this question. The talk will focus on surveying the state of the art, and will discuss some recent algorithms in details as time permits. Based on joint work with Chien-Chung Huang and Thatchaphol Saranurak (FOCS 2017) and with Sebastian Krinninger (work in progress).
Manno, Galleria 1, 2nd floor, room G1-204 @12:00

13 April 2018 - 13 April 2018

This talk will present the current state and the proposed next efforts in the field of natural human-robot interfaces for mobile robotic systems based on body motion. The intuitive mapping system is data-driven from the user's body motion when they are asked to naturally move as they were in control of the machine performing a predefined set of manoeuvres . Previous studies demonstrated that this kind of interface presents a steeper learning curve in terms of teleoperation performance with respect to a classic remote controller. Further developments of the interface will consist in a personalised mapping, adapted ad-hoc on the user, the introduction of a machine learning-based nonlinear regressor to increase the decoding power of the gestural interface, an online adaptation paradigm and a shared control system finalised to the optimisation of estimated trajectories.
IDSIA Meeting Room - Galleria 1 - 11:00 am

23 April 2018 - 23 April 2018

Plagiarism is increasingly becoming a major issue in the academic and educational domains. Automated and effective plagiarism detection systems are direly required to curtail this information breach, especially in tackling idea plagiarism. The proposed approach is aimed to detect such plagiarism cases, where the idea of a third party is adopted and presented intelligently so that at the surface level, plagiarism cannot be unmasked. The work aims to explore syntax-semantic concept extractions with genetic algorithm in detecting cases of idea plagiarism. The work mainly focuses on idea plagiarism where the source ideas are plagiarized and represented in a summarized form. Plagiarism detection is employed at both the document and passage levels by exploiting the document concepts at various structural levels. Initially, the idea embedded within the given source document is captured using sentence level concept extraction with genetic algorithm. Document level detection is facilitated with word-level concepts where syntactic information is extracted and the non-plagiarized documents are pruned. A combined similarity metric that utilizes the semantic level concept extraction is then employed for passage level detection. The proposed approach is tested on PAN 2014 plagiarism corpus for summary obfuscation data, which represents a challenging case of idea plagiarism. The results obtained are found to exhibit significant improvement over the compared systems and hence reflects the potency of the proposed syntax-semantic based concept extractions in detecting idea plagiarism.
IDSIA Meeting Room Galleria 1 @9:30

25 April 2018 - 25 April 2018

In an attempt to explain what Logic (and a logic) is, in this talk, thorough a brief glimpse into the history of the concerned discipline (spanning from Chrysippus of Soli and Aristotle to Gödel and Turing, and beyond), I will provide an overview of some of its fundamental concepts and results underpinning the connections with researches at the Institute. In guise of conclusion, I will present ongoing works related to statistical relational languages and imprecise probabilities.
IDSIA, Room G1-204 - Galleria 1 - 12:00-13:00