Research Seminar

Main content

Would you like to be notified about these presentations via e-mail? Please subscribe here.

Spring Semester 2017

Note: The highlighted event marks the next occurring event and events marked with an asterisk (*) indicate that the time and/or location are different from the usual time and/or location.

Date / Time Speaker Title Location
28 February 2017
Po-Ling Loh
University of Wisconsin-Madison
Influence maximization in stochastic and adversarial settings  HG  G 19.1 
Abstract: We consider the problem of influence maximization in fixed networks, for both stochastic and adversarial contagion models. In the stochastic setting, nodes are infected in waves according to linear threshold or independent cascade models. We establish upper and lower bounds for the influence of a subset of nodes in the network, where the influence is defined as the expected number of infected nodes at the conclusion of the epidemic. We quantify the gap between our upper and lower bounds in the case of the linear threshold model and illustrate the gains of our upper bounds for independent cascade models in relation to existing results. Importantly, our lower bounds are monotonic and submodular, implying that a greedy algorithm for influence maximization is guaranteed to produce a maximizer within a 1-1/e factor of the truth. In the adversarial setting, an adversary is allowed to specify the edges through which contagion may spread, and the player chooses sets of nodes to infect in successive rounds. We establish upper and lower bounds on the pseudo-regret for possibly stochastic strategies of the adversary and player. This is joint work with Justin Khim and Varun Jog.
7 April 2017
Tommaso Proietti
University of Rome, Tor Vergata
Optimal linear prediction of stochastic trends  HG G 19.1 
Abstract: A recent strand of the time series literature has considered the problem of estimating high-dimensional autocovariance matrices, for the purpose of out of sample prediction. For an integrated time series, the Beveridge-Nelson trend is defined as the current value of the series plus the sum of all forecastable future changes. For the optimal linear projection of all future changes into the space spanned by the past of the series, we need to solve a high-dimensional Toeplitz system involving 𝑛 autocovariances, where 𝑛 is the sample size. The paper proposes a non-parametric estimator of the trend that relies on banding, or tapering, the sample partial autocorrelations, by a regularized Durbin-Levinson algorithm. We derive the properties of the estimator and compare it with alternative parametric estimators based on the direct and indirect finite order autoregressive predictors.
10 April 2017
Shahar Mendelson
The Australian National University, Canberra, Australia and The Department of Mathematics, Technion, I.I.T, Haifa, Israel
The small-ball method and the structure of random coordinate projections   HG G 19.2 
Abstract: We study the geometry of the natural function class extension of a random projection of a subset of $R^d$: for a class of functions $F$ defined on the probability space $(\Omega,\mu)$ and an iid sample X_1,...,X_N with each of the $X_i$'s distributed according to $\mu$, the corresponding coordinate projection of $F$ is the set $\{ (f(X_1),....,f(X_N)) : f \in F\} \subset R^N$. We explain how structural information on such random sets can be derived and then used to address various questions in high dimensional statistics (e.g. regression problems), high dimensional probability (e.g., the extremal singular values of certain random matrices) and high dimensional geometry (e.g., Dvoretzky type theorems). Our focus is on results that are (almost) universally true, with minimal assumptions on the class $F$; these results are established using the recently introduced small-ball method.
10 April 2017
Mladen Kolar
The University of Chicago
Title T.B.A. HG G 19.2 
12 May 2017
Walter Distaso
Imperial College
Title T.B.A. HG G 19.1 
29 June 2017
Victor Chernozhukov
Title T.B.A.  HG G 19.1 
Abstract: tba

Archive: SS 17  AS 16  SS 16  AS 15  SS 15  AS 14  SS 14  AS 13  SS 13  AS 12  SS 12  AS 11  SS 11  AS 10  SS 10  AS 09 

Page URL:
© 2017 Eidgenössische Technische Hochschule Zürich