Past events

Function estimation on a large graph using Bayesian Laplacian regularization ---- Alice Kirichenko

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Algorithms for Climate Informatics: Learning from spatiotemporal data with both spatial and temporal non-stationarity ---- Claire Monteleoni

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Adaptive signal denoising with provable guarantees via first-order algorithms ---- Dmitrii Ostrovskii

Dmitrii is visiting the CWI ML these two weeks. He is giving a talk about his recent work.

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Past and Future Dependencies in Meta-Analysis: Flexible Statistics for Reducing Health Research Waste -- Judith ter Schure

This session will be a 10-minute practice talk with feedback. April 20th Judith gives a 10-minute talk at the NRIN Research Conference in the parallel session '2.1 Biases and solutions'<https://www.nrin.nl/agenda/nrin-research-conference-2018/>. Because the talk is so short, a practice round might help to get the focus right. Although the abstract involves all research topics related to meta-analysis, the talk will mainly discuss a newly defined phenomenon: 'Accumulation Bias'. Accumulation Bias is the bias in meta-analysis that occurs because clinical trials are, and ought to be, a dependent sequence; whether additional trials are performed and how many depends on previous trial results. See title and abstract below.

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The Matrix-F Prior for Estimating and Testing Covariance Matrices ---- Joris Mulder

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The paradox of German high-level cryptography in WWII ---- Sandy Zabell

This is a joint ML-group/RISC colloquium. Sandy Zabell is with the Departments of Mathematics and Statistics, Northwestern University

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Generalized/safe Bayes, NML and learning theory ---- Peter Grünwald

Talk about Peter's recent work with Nishant (CWI postdoc alumnus)

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Yuri Gurevich | Impugning Alleged Randomness

Yuri Gurevich from Microsoft Research will talk about individual sequence randomness in a legal context.

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Marc Volkert | Confidence Bound Algorithms in Game Trees

Marc's will speak about his MSc research project on Game Tree Search in preparation for his Leiden MSc thesis defence.

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Alexander Ly | “Jeffreys’s” Bayes factors

Conceptual basics and construction of Bayes factors. This will be an open session with discussion. Starting point will be Alexander Ly's PhD. thesis.

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Judith ter Schure | Brownian Motion in Sequential Analysis

This is the first Fall 2017 regular CWI Machine Learning group meeting. Judith will give an overview of the book "Statistical Monitoring of Clinical Trials" by Proschan, Lan and Turk Wittes, which is a manual for medical ethics boards. The presentation will include technical details (Brownian motion in clinical trials) and the practicalities of optional stopping in double-blind studies.

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Rianne de Heide | Sequential nonparametric testing with the law of the iterated logarithm - Balsubramani, Ramdas

https://arxiv.org/pdf/1405.2639v1.pdf and http://auai.org/uai2016/proceedings/supp/270_supp.pdf

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Jonas Teuwen | Predicting survival in lung cancer with neural networks

CWI Machine Learning Seminar by Jonas Teuwen from Netherlands Cancer Institute & Radboudumc

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Rianne de Heide | Sequential nonparametric testing with the law of the iterated logarithm - Balsubramani, Ramdas

NB Rescheduled because of the annual meeting of the VVS-OR on March 23rd. In nonparametric testing we would like to make a decision between a null hypothesis (H_0) and an alternative hypothesis (H_1) without making assumptions about underlying distribution(s) of the data being analyzed. The test should maximize the power (minimize false negatives) while maintaining the level of type I errors (false positives), and in addition we focus on a sequential testing framework that accomodates data sets that are extremely large or high-dimensional, or data that arrive sequentially. http://auai.org/uai2016/proceedings/supp/270_supp.pdf

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Sjoerd Dirksen | Dimensionality reduction with Johnson-Lindenstrauss embeddings

Machine Learning Seminar by Sjoerd Dirksen from RWTH Aachen. Dirksen will talk about dimensionality reduction using random projections.

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Peter Grunwald | "Bousquet's Version of Talagrand's Inequality": What it is, How to Prove It, and Why It Matters

"Bousquet's Version of Talagrand's Inequality": What it is, How to Prove It, and Why It Matters (from the book "Concentration Inequalities. A Nonasymptotic Theory of Independence. Stephane Boucheron, Gabor Lugosi, and Pascal Massart")

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Vivek Farias | Learning Preferences with Side Information

This is an interesting seminar hosted by Bert Zwart at CWI

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Wouter Koolen | Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression ---- Dieuleveut, Flammarion, Bach

This will be the first Spring 2017 meeting of the CWI ML reading group. This paper is about first order algorithms (no inverse of no matrix) for stochastic least-squares regression that achieve the correct rate in terms of noise (d/n) and initial conditions (n^-2).

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National NIPS Debriefing

National NIPS debriefing in Leiden, where PhD students and/or senior researchers will briefly present the paper they found the most interesting at NIPS 2016.

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Wouter M. Koolen | MetaGrad: Multiple Learning Rates in Online Learning

This is a NIPS oral sneak preview / practice talk. It will follow the NIPS format of 15 minutes presentation plus 5 minutes questions. Your feedback will be very welcome.

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Emilie Kaufmann | Revisiting the Exploration/Exploitation trade-off in bandit models

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James Ridgway | On the properties of variational approximations of Gibbs posteriors

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Gleb Polevoy | The Game of Reciprocation Habits

Gleb Polevoy from Delft Technical University will tell us about his game theoretic results on reciprocation.

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Nishant Mehta | Sample compression schemes for VC classes

Sample compression schemes for VC classes Shay Moran and Amir Yehudayoff (Journal of the ACM, 2016)

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Rianne de Heide | Bayesian regression with sparse priors

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Wouter Koolen | MetaGrad: Online Convex Optimization in Individual-Sequence and Stochastic Settings

This talk will be about MetaGrad, a new algorithm for Online Convex Optimization.

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Steve Homer | Automatically Scalable Computation

This is a talk organised by Ronald de Wolf that may be of interest to the ML group.

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Hannu Reittu | A graph compression method inspired by Szemerédi's Regularity Lemma

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Wouter Koolen | BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ---- Rakhlin and Sridharan

This is the latest paper in the "Relaxation" n-logy.

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Haipeng Luo | Variance-Reduced and Projection-Free Stochastic Optimization

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