Research Seminar "Machine Learning Theory": Past meetings

Past meetings of the TML graduate seminar

• 18.4.2024 Presentation by Sebastian Bordt: Memorization of LLMs
• 11.4.2024 Paper discussion: Impossibility Theorem for Feature Attributions, PNAS 2024, pdf
• 4.4.2024 Presentation by Robi Bhattacharjee: Auditing explanations
• Feb 22, 2024, 14:00 (paper discussion) Understanding augmentation-based self-supervised representation learning via rkhs approximation and regression, Ravikumar et al, ICLR 2024 link
• Feb 15, 2024, internal project presentations
• Jan 25, 2024, Paper discussion (Robi) Solving olympiad geometry without human demonstrations, Nature Jan 2024, link
• Jan 16, 2024, 11:00, Glassroom 3rd floor: Talk by Karolin Frohnapfel: Community Detection in Large Static and Dynamic Networks: Spectral Clustering and Scalability
• Jan 11, 2024, Paper discussion (Sebastian) Are Emergent Abilities of Large Language Models a Mirage? Rylan Schaeffer, Brando Miranda, Sanmi Koyejo. Neurips 2023 best paper award. pdf
• Jan 18, 2024, Paper discussion Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, Neurips 2023 pdf
• Nov 30, 2023, Symposium AI and law all afternoon
• Nov 23, 2023, Paper discussion: What Algorithms can Transformers Learn? A Study in Length Generalization (Sebastian), Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh Susskind, Samy Bengio, Preetum Nakkiran, 2023. pdf to understand this paper it is very beneficial to know the basics of the transformer architecture
• Nov 16, 2023,paper discussion, Moritz: Feature Learning in Infinite-Width Neural Networks, Yang, Hu, 2020. pdf (if you are really interested, a more gentle, experimental reading about the same effect is this paper)
• Nov 16, 2023,paper discussion, Moritz: Feature Learning in Infinite-Width Neural Networks, Yang, Hu, 2020. pdf (if you are really interested, a more gentle, experimental reading about the same effect is this paper)
• Oct 26, 2023, Paper discussion: Learning with Explanation Constraints, Neurips 2023. Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar link
• Nov 2, 2023, Talk by Sebastian Bordt
• Nov 9, 2023 Talk by David on the MLCloud compute cluster
• 27.7.2023 BSc Defense talks by Nico Sarink and Adam Koenig
• August and September: summer break
• 6.7.2023 (paper discussion) Formal Algorithms for Transformers Mary Phuong, Marcus Hutter, pdf
• 22.6.2023 (paper discussion, Vaclav) Learning threshold neurons via the “edge of stability”, 2022
• 15.6.2023 (paper discussion) Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji, Explainability in graph neural networks: A taxonomic survey, 2022. arxiv
• 25.5.2023 Talk by Damien Garreau: On the Robustness of Text Vectorizers. Abstract: A fundamental issue in natural language processing pipelines is their robustness with respect to changes in the input. One critical step in this process is the embedding of documents, which transforms sequences of words or tokens into vector representations. In this talk, I will show how popular embedding schemes, such as concatenation, TF-IDF, and Paragraph Vector (a.k.a. doc2vec), exhibit robustness in the Hölder or Lipschitz sense with respect to the Hamming distance. I will present quantitative bounds and demonstrate how the constants involved are affected by the length of the document. Preprint: https://arxiv.org/abs/2303.07203
• 11.5.2023 Talk by Peru Bhardwaj (currently a postdoc in Stuttgart), Title tba.
• 4.5.2023 (Paper discussion) Towards better understanding attribution methods, Sukrut Rao, Moritz Böhle, Bernt Schiele, 2022pdf
• 27.4.2023 (Paper discussion, Moritz) Algorithmic collective action pdf
• 20.4.2023 (Paper discussion, who?) Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. ICLR 2023
• 30.3.2023 Exceptionally at 9:00 Talk by Gunnar Koenig, Title: Improvement-Focused Causal Recourse
• 23.3.2023 (Paper discussino) Iterative Teaching by Data Hallucination pdfaistats 2023
• 16.3.2023 (Paper discussion) High-dimensional analysis of double descent for linear regression with random projections, pdf
• 7.3.2023 TML day: a whole day of internal talks.
• 2.3.2022 (Paper discussion) Binz, M.; Schulz, E.: Using cognitive psychology to understand GPT-3. PNAS, 2023.
• 13.2.2022 Talk by Martin Pawelczyk: On the Trade-Off between Actionable Explanations and the Right to be Forgotten
• 9.2.2022 (paper discussion) Denoising Diffusion Probabilistic Models pdf, helpful blog posts here and here, Jonathan Ho, Ajay Jain, Pieter Abbeel, 2020.
• 12.1.2022 (paper discussion, Solveig) DiGress: Discrete Denoising diffusion for graph generation, Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard, pdf
• 23.11.2022, 11:00, (attention, unusual day and time) MvL6 Lecture hall: Talk by Tim Erven, Attribution-based Explanations that Provide Recourse Cannot be Robust.
• 30.11. TML Day with talks and discussions
• 17.11.2022 (paper discussion, Moritz) Conformalized Quantile Regression, Yaniv Romano, Evan Patterson, Emmanuel Candes (NeurIPS 2019).
• 3.11.2022 (paper discussion) Attribution-based Explanations that Provide Recourse Cannot be Robust, Hidde Fokkema, Rianne de Heide, Tim van Erven, 2022. Note that Tim Erven is also going to visit us in Nov.
• 27.10.2022 Talk by Klara Burger and Franz Baumdicker: Phylogenetic trees and potential of Tangles.
• 20.10.2022 Talk by Gyorgy Turan (University of Illinois at Chicago, and Research Group on AI of University of Szeged), , University of Illinois at Chicago. Topic: Interpreting deep-learned error-correcting codes.
• 06.10.2022 Master thesis presentation by Alexander Conzelmann
• 16.9.2022 Master thesis presentation by Johannes Hölscher and Bachelor thesis presentation by Amelie Schaefer.
• 28.7.2022 (Paper discussion) The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? (pdf)
• 21.7.2022 (Paper discussion) On Uniform Convergence and Low-Norm Interpolation Learning. Lijia Zhou, Danica J. Sutherland, Nathan Srebro, Neurips 2020. pdf
• 14.7.2022 Master thesis presentation (Kornelius Raeth): Gibbs priors beyond approximate Bayesian inference
• 30.6.2022 (paper discussion, Alex) How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks (Moritz) (OpenReview)
• 23.6.2022 (paper discussion, Moritz) Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose your Model, not your Loss Function (Moritz) (pdf)
• 2.6.2022 Talk by Damien Garreau (France): Title: How to scale hyperparameters for quickshift image segmentation Abstract: Quickshift is a popular algorithm for image segmentation, used as a preprocessing step in many applications. Unfortunately, it is quite challenging to understand the hyperparameters’ influence on the number and shape of superpixels produced by the method. In this paper, we study theoretically a slightly modified version of the quickshift algorithm, with a particular emphasis on homogeneous image patches with i.i.d. pixel noise and sharp boundaries between such patches. Leveraging this analysis, we derive a simple heuristic to scale quickshift hyperparameters with respect to the image size, which we check empirically. Link to the paper
• 28.04, 5.5. and 12.5.2022 (paper discussion, Leena) Fit without fear:remarkable mathematical phenomena of deep learning through the prism of interpolation, Mikhail Belkin, Acta Numerica 30 (2021)
• 21.04..2022 Master's thesis: Kinesthetic Learning Activities in Algorithm Lectures (Benedikt Gottschlich)
• 31.3..2022 (paper discussion, Luca): A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning Ben Adlam, Jake Levinson, Jeffrey Pennington, AISTATS 2022
• 7.4.2022 Refining Language Models with Compositional Explanations, Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, Xiang Ren, NeurIPS 2021
• 17.3.2022 Talk by Yann Issartel, Paris
• 10.3.2022 (paper discussion, Valentyn) - A Variational Perspective on Diffusion-Based Generative Models and Score Matching, pdf.
• 24.2.2022 Language Models are Few-Shot Learners (also the broader impacts section) pdf with details, see figure 2.1 for the task description
• 17.2.2022 (paper discussion) - Secure Single-Server Aggregation with (Poly)Logarithmic Overhead, pdf.
• 20.01.2022 Talk by Solveig Klepper: Graph Variational Auto Encoders
• 27.01.2022 Talk by Guillaume Dalle
• 3.2.2022 paper discussion by Michael - Communication-Efficient Learning of Deep Networks from Decentralized Data, pdf.
• Order of the following two can be switched:
• 10.2.2022 (paper discussion) - Fair Resource Allocation in Federated Learning, pdf.
• 16.12.2021 (paper discussion, Luca) Overparameterization Improves Robustness to Covariate Shift in High Dimensions, Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington, Neurips 2021
• 2.12.2021 (paper discussion, Moritz) A Universal Law of Robustness via Isoperimetry Sebastien Bubeck, Mark Sellke, Neurips 2021
• 24.+25.11.2021 Group retreat with talks by all of us!!!
• 11.11.2021 (paper discussion): Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? Zhiyuan Li, Yi Zhang, Sanjeev Arora, ICLR 2021
• 4.11.2021 (paper discussion): Machine unlearning via Algorithmic stability Enayat Ullah , Tung Mai , Anup Rao , Ryan A. Rossi , Raman Arora, COLT 2021
• 28.10.2021 (paper discussion, Solveig): Contrastive Learning with Hard Negative Samples Joshua David Robinson Joshua_David_Robinson, , Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka, ICLR 2021
• 21.10.2021 (paper discussion): A Theory of Heuristic Learnability , Mikito Nanashima, COLT 2021
• 29.7.2021 Master thesis defense Jonas Koenig
• 22.7.2021. Paper discussion: Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence volume 1, pages 206–215 (2019) pdf
• 15.7.2021 Paper discussion: Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri, pdf
• 8.7.2021 Paper discussion (Moritz) Biau, G., Cadre, B., Sangnier, M., & Tanielian, U. Some theoretical properties of GANs. Annals of Statistics, 2020 pdf
• 1.7.2021 Paper discussion (Sebastian) Asynchronous Online Testing of Multiple Hypotheses, Zrnic, Ramdas, , Jordan, JMLR 2021
• 10.6.2021 Paper discussion (Sascha) Strength from Weakness: Fast Learning Using Weak Supervision Joshua Robinson, Stefanie Jegelka, Suvrit Sra, ICML 2020 pdf
• 1.6. Talk by Janosch Döcker on phylogentic trees
• 20.5.2021 Paper discussion: Auditing ML Models for Individual Bias and Unfairness Songkai Xue, Mikhail Yurochkin, Yuekai Sun, ICML 2020, pdf
• 6.5.2021 Paper discussion: Random deep neural networks are biased towards simple functions, NeurIPS 2019.
• 29.4.2021 Paper discussion (David) When do neural networks outperform kernel methods?, NeurIPS 2020. Video
• 22.4.2021 Masters thesis presentation (Julius & Rabanus)
• 25.03.21 Paper discussion (Sebastian), Kernel and rich regimes in overparametrized models, Conference on Learning Theory. PMLR, 2020.
• 18.03.21 Paper discussion , Neural Tangent Kernel: Convergence and Generalization in Neural Networks., NeurIPS. 2018.
• 11.3.2021 Paper discussion (Leena), Conditional variance penalties and domain shift robustness., Machine Learning, 2020. Drop Section 5.3 and following.
• 4.3. Bachelor thesis presentations by Fynn Neurath and Tabea Frisch
• 25.2.2021 Paper discussion (Sascha), Generative causal explanations of black-box classifiers, NeurIPS, 2020.
• 18.2.2021 Paper discussion (Luca), Shapley Flow: A Graph-based Approach to Interpreting Model Predictions, arXiv, 2020.
• 11.2.2021 Paper discussion, Fairwashing Explanations with Off-Manifold Detergent, ICML 2020.
• 4.2.2021 Paper discussion (Sebastian), Anchors: High-Precision Model-Agnostic Explanations, AAAI 2018.
• 21.1.2021 Paper discussion, A Unified Approach to Interpreting Model Predictions, NeurIPS 2017.
• 3.12.2020 Discussion about future research directions
• 19.11.2020 Paper discussion: Explainable k-Means and k-Medians Clustering, Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz, Cyrus Rashtchian, Arxiv 2020 pdf
• 12.11.2020 Paper discussion (Solveig) Noga Alon, Yossi Azar, Danny Vainstein: Hierarchical Clustering: A 0.585 Revenue Approximation. COLT 2020
• 5.11.2020 Paper discussion (Michael) Bounding the fairness and accuracy of classifiers from population statistics Sivan Sabato, Elad Yom-Tov, ICML 2020. link
• 22.10.2020 Paper discussion (Sebastian): How Powerful are Graph Neural Networks? Keyulu Xu*, Weihua Hu*, Jure Leskovec, Stefanie Jegelka, ICLR 2019, link
• 29.10.2020 A day full of internal presentations by many group members
• 1.10.2020 Paper discussion (Luca) Relational inductive biases, deep learning, and graph networks arXiv 2018.
• 13.8.2020 BSc Defense by Margaretha Schlueter: Is ordinal embedding NP hard?
• 30.7.2020 Master Defesnse by Solveig: Tangles in Machine Learning.
• 9.7.2020 Paper discussion: Low-rank regularization and solution uniqueness in over-parameterized matrix sensing Kelly Geyer, Anastasios Kyrillidis, Amir Kalev ; Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:930-940, 2020. pdf
• 2.7.2020 Paper discussion (Luca): Graph Coarsening with Preserved Spectral Properties AISTATS 2020
• 25.6.2020 Paper discussion (Diego): Making AI Forget You: Data Deletion in Machine Learning, NeurIPS 2019.
• 18.6.2020 Paper discussion (Sebastian) Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan J. Foster, Alexander Rakhlin
• 7.5.2020 Paper discussion: A Meta-Analysis of Overfitting in Machine Learning, NeurIPS 2019.
• 14.5.2020 Paper discusssion: Model Similarity Mitigates Test Set Overuse, NeurIPS 2019.
• 9.4.2020 Paper discussion (Sascha) On Medians of (Randomized) Pairwise Means ICML 2019
• 2.4.2020 Paper discussion (Michael) Fairness and Utilization in Allocating Resources with Uncertain Demand Kate Donahue, Jon Kleinberg, FAT*2020, Best Paper Award.
• 5.3.2020 Paper discussion (Luca) Structure and Overlaps of Ground-Truth Communities in Networks, Yang and Leskovec, 2012.
• 13.2.2020 (Talk by Diego) Introduction to Tangles Tangles in the Social Sciences (example paper) Reinhard Diestel
• 24.1.2020, 10:00 Talk by Reinhard Diestel (University of Hamburg):
  Tangles: from graph minors to identifying political mindsets
  
• 16.1.2020 Paper discussion (Leena)Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman
• 19.12.2019 Paper discussion (Diego)VC Classes are Adversarially Robustly Learnable, but Only Improperly, Omar Montasser, Steve Hanneke, Nathan Srebro, COLT 2019
• 12.12.2019 Paper discussion (Sebastian)The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro, JMLR 2018
• 5.12.2019 Sample-Optimal Low-Rank Approximation of Distance Matrices, Pitor Indyk, Ali Vakilian, Tal Wagner, David P Woodruff COLT 2019
• 28.11.2019 Paper discussion (Damien): Reconciling modern machine learning practic eand the bias-variance trade-off , Mikhail Belkina, Daniel Hsub, Siyuan Maa, and Soumik Mandala, 2019
• 21.11.2019 Paper discussion (Michael):Fairness risk measures, Robert Williamson, Aditya Menon, ICML 2019
• 14.11.2019 Paper discussion (Sascha): Adversarial examples from computational constraints, Sebastien Bubeck, Yin Tat Lee, Eric Price, Ilya Razenshteyn, ICML 2019
• 31.10.2019 Paper discussion (Solveig):Robustness of Spectral Methods for Community Detection, Ludovic Stephan, Laurent Massoulie, Colt 2019
• 17.10.2019 Paper discussion (Sebastian): The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally, Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer Chayes, 2019
• 11.07.2019 Paper discussion (Luca) Counting Motifs with Graph Sampling Jason M. Klusowski, Yihong Wu. COLT 2018
• 04.07.19: Defense of Siavash's PhD thesis: Comparison-based methods in machine learning.
• 27.6.2019 Paper discussion (Diego) Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu A Comprehensive Survey on Graph Neural Networks
• 6.6.2019 Benjamin Hogl will defend his Master thesis: Extending fairness in machine learning to multiple protected groups
• 16.5.2019 Paper discussion: Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi. A Theoretical Analysis of Contrastive Unsupervised Representation Learning. 2019. pdf
• 2.5.2019 Paper discussion (Sascha) Draief, M., Kutzkov, K., Scaman, K., Vojnovic, M.KONG: Kernels for ordered-neighborhood graphs. NeurIPS 2018
• 25.4.2019 Paper discussion (Debarghya): Zhang, Levina, Zhu. Estimating network edge probabilities by neighbourhood smoothing Biometrika, 2017. [arxiv]
• 18.4.2019 Paper discussion: Bojchevski A, Shchur O, Zügner D, Günnemann S: NetGAN: Generating Graphs via Random Walks. International Conference on Machine Learning. 2018; 609-618.
• 11.4.2019 TML DAY, a full day of internal talks:
  • 9:00-9:45 Talk by Michael Lohaus - Biased access to international networks
  • 9:45-10:30 Talk by Damien Garreau - Theoretical Guarantees for Local Interpretations
  • coffee break
  • 11:00-11:45 Talk by Michael Perrot - Foundations of Comparison-Based Hierarchical Clustering
  • 11:45-12:30 Talk by Leena Chennuru Vankadara - MMD based generalized clustering
  • lunch break and group picture
  • 14:00-14:45 Talk by Debarghya Ghoshdastidar - Clustering and graph problems
  • 14:45-15:30 Talk by Sascha Meyen - Two Pitfalls In Consciousness Research: The Indirect Task Advantage and Confidence Weighted Majority Voting
  • coffee break
  • 16:00-16:45 Talk by Siavash Haghiri - Comparison-based setting in machine learning
• 4.4.2019 Paper discussion: Calandriello, Rosasco. Statistical and Computational Trade-Offs in Kernel K-Means NeurIPS 2018
• 28.3.2019 Paper discussion: Fairness Through Awareness Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Rich Zemel pdf
• 21.3.2019 Paper Discussion: Grover, Leskovec. node2vec: Scalable Feature Learning for Networks KDD 2016
• 14.3.2019 Paper discussion: J. Kleinberg, M. Raghavan: Selection Problems in the Presence of Implicit Bias. 2018.
• 21.2.2019 Talk by Oindrila Kanjilal: Structural reliability estimation using Markov chain splitting and Girsanov's transformation based methods
  Abstract: This talk is in the area of Monte Carlo simulation based methods for structural reliability estimation with special focus on strategies to reduce sampling variance of the estimator for the probability of failure. The talk will focus on two specific variance reduction schemes for reliability assessment, namely, the Markov chain Monte Carlo based particle splitting methods, and the Girsanov transformation based importance sampling methods for dynamical systems. Specifically, three issues will be discussed: (a) strategies to reduce sampling variance in the subset simulation based methods by modifying a few intermediate steps in the existing subset simulation algorithm, (b) development of closed loop Girsanov controls in the study of structural dynamical systems governed by stochastic differential equations, and (c) combining the Markov chain particle splitting methods and the closed loop Girsanov transformation based method to assess reliability of dynamical systems with uncertain parameters.
• 7.2.2019 Paper discussion: Mikhail Belkin, Daniel Hsu, Partha Mitra: Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. NeurIPS 2018.
• 24.1.2019 Talk by Marina Meila (University of Washington): Unsupervised Validation for Unsupervised Learning
  Abstract: Scientific research involves finding patterns in data, formulating hypotheses, and validating them with new observations. Machine learning is many times faster than humans at finding patterns, yet the task of validating these as "significant" is still left to the human expert or to further experiment. In this talk I will present a few instances in which unsupervised machine learning tasks can be augmented with data driven validation. In the case of clustering, I will demonstrate a new framework of proving that a clustering is approximately correct, that does not require a user to know anything about the data distribution. This framework has some similarities to PAC bounds in supervised learning; unlike PAC bounds, the bounds for clustering can be calculated exactly and can be of direct practical utility. In the case of non-linear dimension reduction by manifold learning, I will present a way around the following well-known problem. It is widely recognized that the low dimensional embeddings obtained with manifold learning algorithms distort the geometric properties of the original data, like distances and angles. These algorithm dependent distortions make it unsafe to pipeline the output of a manifold learning algorithm into other data analysis algorithms, limiting the use of these techniques in engineering and the sciences. Our contribution is a statistically founded methodology to estimate and then cancel out the distortions introduced by any embedding algorithm, thus effectively preserving the distances in the original data. This method is based on augmenting the output of a manifold learning algorithm with "the pushforward Riemannian metric", i.e. with additional metric information that allows it to reconstruct the original geometry. Joint work with Dominique Perrault-Joncas, James McQueen, Jacob VanderPlas, Zhongyue Zhang, Grace Telford, Yu-chia Chen, Samson Koelle
• 17.1.2019 Talk by Jesse Anderton (Northeastern University, Boston): Ordinal Embedding: A Geometric Approach
• 10.1.2019 Talk by Moritz Haas about his master thesis: Ranking based on local comparisons
• 13.12.2018 Paper discussion: Clustering Redemption--Beyond the Impossibility of Kleinberg's Axioms. Vincent Cohen-Addad, Varun Kanade and Frederik Mallmann-Trenn, NeurIPS 2018.
• 29.11.2018 Paper discussion: Approximate Nearest Neighbors in Limited Space Piotr Indyk and Tal Wagner. COLT 2018
• 22.11.2018 Paper discussion: Xue, Kpotufe Achieving the time of 1-NN, but the accuracy of k-NN AISTATS 2018.
• 15.11.2018 Paper discussion:Delayed Impact of Fair Machine Learning, Lydia Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt, ICML 2018
• 8.11.2018 Paper discussion: Fairness Without Demographics in Repeated Loss Minimization, Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang, ICML 2018
• 25.10.2018 Paper discussion: Wasserstein auto-encoders (Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf)
• 18.10.2018 Master thesis talk by Tosca Lechner
• 11.10.2018 Paper discussion: Approximate ranking from pairwise comparisons (AISTATS 2018) Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, and Martin J. Wainwright
• 25.7.2018 Talk by Krikamol Muandet, about learning theory approaches in counterfactual causality
• 11.7.2018 Talk by Carl Johann Simon-Gabriel on his PhD thesis
• 4.7.2018 Leena presents her master thesis: Metric Embeddings for Machine Learning, Siavash presents his ICML talk: Comparison-Based Random Forests
• 27.6.2018 Talk by Tobias Frangen about his master thesis: Consistency of Relative Neighborhood Classification Rules
• 20.6.2018 Paper discussion: An Analysis of the t-SNE Algorithm for Data Visualization,Sanjeev Arora, Wei Hu and Pravesh K Kothari, COLT 2018
• 15.6.2018 Talk by Karl Rohe (University of Wisconsin-Madison)
  Abstract: This paper uses the relationship between graph conductance and spectral clustering to study (i) the failures of spectral clustering and (ii) the benefits of regularization. The explanation is simple. Sparse and stochastic graphs create a lot of small trees that are connected to the core of the graph by only one edge. Graph conductance is sensitive to these noisy "dangling sets." Spectral clustering inherits this sensitivity. The second part of the paper starts from a previously proposed form of regularized spectral clustering and shows that it is related to the graph conductance on a "regularized graph." We call the conductance on the regularized graph CoreCut. Based upon previous arguments that relate graph conductance to spectral clustering (e.g. Cheeger inequality), minimizing CoreCut relaxes to regularized spectral clustering. Simple inspection of CoreCut reveals why it is less sensitive to small cuts in the graph. Together, these results show that unbalanced partitions from spec tral clustering can be understood as overfitting to noise in the periphery of a sparse and stochastic graph. Regularization fixes this overfitting. In addition to this statistical benefit, these results also demonstrate how regularization can improve the computational speed of spectral clustering. We provide simulations and data examples to illustrate these results.
• 13.6.2018 Whole day: Seminar on statistics on graphs and networks
• 6.6.2018 Talk by Michael Schober about his PhD thesis (PhD student of Philipp Hennig)
• 30.5.2018 Paper discussion: Arias-Castro Some theory for ordinal embedding, Bernoulli 23(3):1663-1693, 2017.
• 9.5.2018 Paper discussion: Kazemi, Chen, Dasgupta, Karbasi. Comparison Based Learning from Weak Oracles. AISTATS, 2018.
• 2.5.2018 Paper discussion: Kremer, Sha, Igel Robust Active Label Correction AISTATS 2018.
• 25.4.2018 Paper discussion: Ukkonen Crowdsourced correlation clustering with relative distance comparisons. ICDM, 2017.
• 18.4.2018 Talk by Erwan Scornet (Center for Applied Mathematics, Ecole Polytechnique, Paris) (at the MPI, seminar room N0.002): Consistency and minimax rates of random forests
  Abstract: The recent and ongoing digital world expansion now allows anyone to have access to a tremendous amount of information. However collecting data is not an end in itself and thus techniques must be designed to gain in-depth knowledge from these large data bases. This has led to a growing interest for statistics, as a tool to find patterns in complex data structures, and particularly for turnkey algorithms which do not require specific skills from the user. Such algorithms are quite often designed based on a hunch without any theoretical guarantee. Indeed, the overlay of several simple steps (as in random forests or neural networks) makes the analysis more arduous. Nonetheless, the theory is vital to give assurance on how algorithms operate thus preventing their outputs to be misunderstood. Among the most basic statistical properties is the consistency which states that predictions are asymptotically accurate when the number of observations increases. In this talk, I will present a first result on Breiman’s forests consistency and show how it sheds some lights on its good performance in a sparse regression setting. I will also present new results on minimax rates of Mondrian forests which highlight the benefits of forests compared to individual regression trees.
• 11.4.2018 Paper discussion: Efficient k-nearest neighbor graph construction for generic similarity measures Dong, Charikar, Li, WWW-2011.
• 4.4.2018 Paper discussion: Which Distribution Distances are Sublinearly Testable? Daskalakis, Kamath, Wright, SODA 2018
• 28.3. no meeting, easter vacation
• 21.3.2018 no meeting, Conference on computational archeology , program
• 14.3.2018 TML Day: lots of internal talks
  9:00 - 9:45 Siavash Haghiri (Comparison-Based Framework for Psychophysics)
  9:45 - 10:30 Damien Garreau (Comparison-Based Random Forests)
  11:00 - 11:45 Michael Perrot (Boosting Triplets for Classification)
  11:45 - 12:30 Michael Lohaus (Bayesian Optimization for Distance Estimation)
  13:30 - 14:30 Debarghya Ghoshdastidar (Minimax Rates in Graph Testing and Feature Clustering)
  14:30 - 15:15 Georgios Arvanitidis
  15:45 - 16:30 Diego Fioravanti (A principled approach to adversarial examples)
  
• 28.2.2018 Paper discussion: Crowdsourced Clustering: Querying Edges vs Triangle , NIPS 2016
• 21.2.2018 Paper discussio: Learning Low-Dimensional Metrics , Blake Mason, Lalit Jain, Robert Nowak, NIPS 2017
• 14.2.2018 Talk by Maren Mahsereci about her PhD thesis (PhD student in Philipp Hennigs group)
• 7.2.2018 Paper discussion: On clustering network-valued data Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin, NIPS 2017
• 17.1.2018 Paper discussion: Yizhen Wang, Somesh Jha, Kamalika Chaudhuri, Analyzing the Robustness of Nearest Neighbors to Adversarial Examples , 2017
• 10.1.2018 Paper discussion: Formal Guarantees on the Robustness of a Classifier against Adversarial ManipulationHein et al, NIPS 2017
• 20.12.2017 Paper discussion: Supervised Word Movers Distance Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger, NIPS 2016
• 13.12.2017 Paper discussion: S. Dasgupta. A cost function for similarity-based hierarchical clustering. STOC, 2016.
• 29.11.2017 Paper discussion: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. NIPS 2016. link
• 22.11.2017 Paper discussion: k*-Nearest Neighbors: From Global to Local. NIPS 2016
• 15.11. 2017 Paper discussion: Active Learning from Imperfect Labelers Songbai Yan, Kamalika Chaudhuri, Tara Javidi, NIPS 2016
• 8.11.2017 Paper discussion: Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing. COLT 2017.
• 25.10.2017 Paper discussion: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam T. Kalai . NIPS 2016. link
• 4.10.2017 Paper discussion: The Perturbed Variation Haarel, Mannor, NIPS 2015
• Wed, 26.7.2017: Paper discussion: Kondor, Pan: Multiscale graph Laplacian kernel. NIPS 2016
• 18.+19.7. Mini-conference ML in Science, Tuebingen
• Wed, 12.7.2017: Paper discussion: Clustering with Same-Cluster Queries.Hasan Ashtiani et al. NIPS, 2016
• Wed, 5.7.2017: Paper discussion: Understanding deep learning requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals
• Thur, 29.6. Machine Learning Summer School
• Wed, 21.6. Machine Learning Summer School
• Tue, 13.6. 14:00 Talk by Cheng Tang on subspace clustering
• 12.6. 15:00 Talk by Nicolas Garcia (at the MPI, seminar room fourth floor): The variational formulation of the Bayesian update and its connection to the choice of metric tensor in Riemannian MCMC.
  Abstract: A pressing question in Bayesian statistics and machine learning is to introduce a unified theoretical framework that brings together some of the many statistical models and algorithmic methodologies employed by practitioners. In this talk I will argue in favor of the variational formulation of the Bayesian update as it provides both an overarching structure and a powerful tool for the analysis of many such models and algorithms. When coupled with the theory of gradient flows on the space of probability measures, the variational formulation of the Bayesian update suggests a natural path connecting prior and posterior distributions. This path however, is only natural after the choice of a metric in the base space where the prior and posterior distributions are defined. I will then show how the variational framework suggests clear optimality criteria for the choice of metric in the Riemannian MCMC methodology introduced by Girolami and Calderhead. This is joint work with Daniel Sanz-Alonso.
• 1.6.2017: Paper discussion: False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking ICML 2016
• 24.5. 2017 Paper discussion: Fast Distributed k-Center Clustering with Outliers on Massive Data
• 10.5.2017: Paper discussion: Representational Similarity Learning with Application to Brain Networks ICML 2016
• 4.5.2017: Paper discussion: Scalable Semi-Supervised Aggregation of Classifiers. NIPS 2015
• 26.4.2017: Paper discussion: Sign rank versus VC dimension COLT 2016
• 20.4.2017: Paper discussion: Starting Small - Learning with Adaptive Sample Sizes ICML 2016
• 13.4.2017 Paper discussion: Compressive Spectral Clustering ICML 2016
• 5.4.2017 Presentations by our group members
  • 10:00 - 10:30 Talk by Debarghya Ghoshdastidar: Two-Sample Tests for Large Random Graphs
  • 10:30 - 11:00 Talk by Matthaeus Kleindessner: Machine learning in a setting of ordinal distance information --- kernel functions as an alternative to the embedding approach
  • 11:30 - 12:00 Talk by Lennard Schulz: A comparison-based setup in psychophysics: comparing subsampling strategies
  • 12:00 - 12:30 Talk by Siavash Haghiri: Comparison Based Nearest Neighbor Search
  • 13:15 - 13:45 Talk by Cheng Tang: Understanding the empirical success of clustering heuristics
• 30.3.2017 Paper discussion: Clustering Signed Networks with the Geometric Mean of Laplacians Pedro Mercado, Francesco Tudisco, Matthias Hein. NIPS 2016 (Debarghya)
• 23.3.2017 Paper discussion: Hardt, Price, Srebro: Equality of Opportunity in Supervised Learning. NIPS 2016.
• 16.3.2017 Paper discussion: Memory, Communication, and Statistical Queries COLT 2016
• 23.2.2017 Talk by Damien Garreau, ENS Paris
• 16.+17.2. 2017 Seminar on crowdsourcing takes place.
• 9.2.2017 Talk by Michael Perrot, Université Saint-Étienne: Learning Metrics with a Controlled Behaviour

  Abstract: The goal in Machine Learning is to acquire new knowledge from data. To achieve this many algorithms make use of a notion of distance or similarity between examples. A very representative example is the nearest neighbour classifier which is based on the idea that two similar examples should share the same label: it thus critically depends on the notion of metric considered. Depending on the task at hand these metrics should have different properties but manually choosing an adapted comparison function can be tedious and difficult. The idea behind Metric Learning is to automatically tailor such metrics to the problem at hand. One of the main limitation of standard methods is that the control over the behaviour of the learned metrics is often limited. In this talk I will present two approaches specifically designed to overcome this problem. In the first one we consider a general framework able to take into account a reference metric acting as a guide for the learned metric. We are then interested in theoretically studying the interest of using such side information. In the second approach we propose to control the underlying transformation of the learned metric. Specifically we use some recent advances in the field of Optimal Transport to force it to follow a particular geometrical transformation.

• 26.1.2017 Paper discussion: Recommendations as Treatments: Debiasing Learning and Evaluation ICML 2016
• 19.1.2017 Paper discussion: Data-driven Rank Breaking for Efficient Rank Aggregation JMLR 2016
• 12.1.2017 Paper discussion: Greedy Column Subset Selection: New Bounds and Distributed Algorithms ICML 2016
• 24.11.2016 Paper discussion: Learning Combinatorial Functions from Pairwise Comparisons COLT 2016
• 17.11.2016 Paper discussion: Provably Manipulation-Resistant Reputation Systems COLT 2016
• 10.11.2016 Paper discussion: Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don’t Help arXiv
• 3.11.2016 Paper discussion: When Can We Rank Well from Comparisons of $O(n\log n)$ Non-Actively Chosen Pairs? COLT 2016
• 28.7.2016 Paper discussion: Finite Sample Prediction and Recovery Bounds for Ordinal Embedding, Lalit Jain, Kevin Jamieson, Robert Nowak, arxiv 2016
• 21.7.2016 Paper discussion: Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser. NIPS 2015
• 30.6.2016 Paper discussion: Nihar Shah et al: Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
• 23.6.2016 Talk by Debarghya
• 14.7.2016 Paper discussion: Precision-Recall-Gain Curves: PR Analysis Done Right Peter Flach, Meelis Kull. NIPS 2015
• 16.6.2016 Paper discussion: Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan NIPS 2015
• 9.6.2016 Paper discussion: Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam C. Kamath. NIPS 2015
• 2.6.2016 Paper discussion: Competitive Distribution Estimation: Why is Good-Turing Good Alon Orlitsky, Ananda Theertha Suresh. NIPS 2015.
• 12.5.2016 Paper discussion: Fast two sample testing with analytic representations of probabiilty measures. Chwialkowski, Ramdas, Sejdinovic, Gretton, NIPS 2015.link
• 28.4.2016 Paper discussion: A Nearly-Linear Time Framework for Graph-Structured Sparsity. ICML 2015 pdf
• 19.4.2016 Paper discussion: Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds Yuchen Zhang, Martin Wainwright, Michael Jordan. ICML 2015
• 12.4.2016 Paper discussion: Multiview Triplet Embedding: Learning Attributes in Multiple Maps. ICML 2015. pdf
• 7.4.2016 Learning preferences from ordinal data. Oh, Thekumparampil, Xu, NIPS 2015
• 3.3.2016 Paper discussion: Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco, NIPS 2015
• 10.3.2016 Paper discussion: Shah, Wainwright: Simple, Robust and Optimal Ranking from Pairwise Comparison. 2015 link
• 25.2.2016 Paper discussion: Statistical Model Criticism using Kernel Two Sample Tests James R. Lloyd, Zoubin Ghahramani, NIPS 2015.
• 18.2.2016 Paper discussion: Approval Voting and Incentives in Crowdsourcing, Nihar Shah, Dengyong Zhou, Yuval Peres, ICML 2015. pdf
• 4.2.1.2016 Paper discussion: Muhammad Bilal Zafar, Isabel Valera Martinez, Manuel Gomez Rodriguez, and Krishna Gummadi Fairness Constraints: A Mechanism for Fair Classification . icml workshop on fairness and accountabilty in ml 2015
• 28.1.2016 Paper discussion: Y. Jiao and J.-P. Vert, "The Kendall and Mallows Kernels for Permutations", ICML 2015

In Hamburg (2012-2015)

• 4.6.2015 Paper discussion: Florent Krzakala, Cristopher Moore, Elchanan Mosseld, Joe Neemand, Allan Sly, Lenka Zdeborová, and Pan Zhanga: Spectral redemption in clustering sparse networks, PNAS 2013 pdf
• 28.5.2015 Paper discussion: Wauthier, Jojic, Jordan: Active spectral clustering via iterative uncertainty reduction. KDD, 2012. link
• 21.5.2015 Paper discussion: Jun Li , Juan A. Cuesta-Albertos, Regina Y. Liu, DD-Classifier: Nonparametric Classification Procedure Based on DD-Plot, JASA 2015. link
• 27.4.2015: Talk by Ruth Urner, Max Planck Institute for Intelligent Systems, Tuebingen.
• 23.4. 2015 Paper discussion: Learning Mixtures of Ranking Models (ranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan, NIPS 2014)
• 16.4. 2015 Paper discussion: Almost no label to cry (Giorgio Patrini, Richard Nock, Tiberio Caetano, Paul Rivera, NIPS 2014)
• 9.4. 2015 Paper discussion: Discrete Graph Hashing (Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang, NIPS 2014)
• 26.2.2015 We discuss again the paper "Ranking and combining multiple predictors without labeled data" pdf. Focus is on understanding how the key lemma 1 can be true and why it makes sense. Everybody prepare seriously, please ...
• 5.3. 2015 Machine learning brainstorming day!
  9-10 Tobias Lang: Active learning with user feedback
  10 - 11 Morteza Alamgir: Centrality based graph kernels
  11-12: Mehdi Sajjadi: peer grading algorithms, our data, our current insights
  12 - 13 lunch
  13 - 14 Sven Kurras: Clustering in an online game with adversarial players
  14 - 15 Rita Morisi: Spectral clustering applied to the Consensus problem
  Unfortunately, the talk by Matthaeus has to be skipped (was: Estimating median and modes and clusters from ordinal crowd data)
  
• 19.2. Two Bachelor-thesis defense talks
  Jonas Häring: Comparing graphs with small doubling dimension to expander graphs
  Alexis Engelke: Streaming algorithms for graph partitioning
• 12.2.2015 Paper discussion: Heikinheimo, Ukkonen. The crowd-median algorithm. First AAAI Conference on Human Computation and Crowdsourcing, 2013. pdf.
• 5.2.2015 Talk by Rita Morisi (Institute of Advanced Studies Lucca, Italy): Graph based techniques in machine learning and control
• 29.1. 2015 Paper discussion: Parisi, Nadler et al, PNAS 2014: Ranking and combining multiple predictors without labeled data pdf
• 22.1. 2015 Talk by Thomas Buehler, Uni Saarbruecken: Titel: A flexible framework for solving constrained ratio problems in machine learning
• 15.1.2015 Paper discussion: Distributed Balanced Clustering via Mapping Coresets, NIPS 2014 pdf
• 8.1. 2015 Paper discussion: Scalable Simple Random Sampling and Stratified Sampling, ICML 2013 pdf
• 18.12.2014 Paper discussion: Sinkhorn Distances: Lightspeed Computation of Optimal Transport. M. Cuturi, NIPS 2013 pdf
• 11.12.2014 Paper discussion: Dimensionality Reduction with Subspace Structure Preservation, NIPS 2014 pdf
• 4.12. 2014 Paper discussion: Graph clustering via a discrete uncoupling process. Stijn van Dongen. SIAM J. MATRIX ANAL. APPL. 2008 pdf
• 27.11.2014 We discuss the following two papers, just high level:
  Fennel: Streaming graph partitioning for massive scale graphs. 2014 pdf
  Streaming graph partitioning for large distributed graphs. I Stanton, G Kliot, KDD 2012. pdf
• 13.11.2014 Paper discussion: Network-based statistic: identifying differences in brain networks A Zalesky, A Fornito, ET Bullmore - Neuroimage, 2010. pdf
• 20.11.2014 Talk by Maximilian Christ, University of Dortmund: SNP DNA analysis with logic regression.
• 30.10.2014 Master defense talk by Longshan Sun: Algorithms for peer assessment.
• 23.10.2014 Paper discussion: On the convergence of maximum variance unfolding Ery Arias-Castro, Bruno Pelletier. JMLR, 2013 pdf
• 16.10.2014 Paper discussion: Ohad Shamir: Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation. Arxiv 2014. link (we read the main paper, don't dive into the proofs if you don't have time).
• 2.10.2014 Talk by Tobias Lang, Zalando, Berlin (at 11:00!): From Planning and Exploration in Stochastic Relational Worlds to Recommender Systems in the E-Commerce World
• 24.8.2014 Sundus Israr's Master defense test talk
• 10.7. 2014 Morteza Alamgir's thesis defense test talk.
• 3. 6. 2014 A whole day of talks!
  9:00 - 9:45 Matthaeus Kleindessner: Uniqueness of Ordinal Embedding
  9:45 - 10:30 Yoshikazu Terada: Local ordinal embedding
  10:30 - 11:30 Ulrike von Luxburg: Density estimation from unweighted kNN graphs
  12:30 - 13:15 Sven Kurras: The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation
  13:15 - 13:45 Morteza Alamgir: Density-preserving quantization with application to graph downsampling
• 15.5.2014 Talk by Daniel Schmidtke about his master thesis (from 15:00-15:30). Then we discuss the paper by Dejan Slepcev et al, Continuum limit of total variation on point clouds. Preprint, 2014
• 22.5.2014 Paper discussion: Breaking the Small Cluster Barrier of Graph Clustering, Nir Ailon, Yudong Chen, Huan Xu, ICML 2013 pdf
• 17.4.2014 Paper discussion: Senelle, Garcia-Diez, Mantrach, Shimbo, Saerens, Fouss: The sum over forests density index: identifying dense regions in a graph. Preprint, 2013, not online yet, here is a local copy (with our usual login and password used for teaching). pdf
• 24.4. 2014: talk by Sharon Bruckner (FU Berlin) about "Random-walk based methods for clustering"
• 10.4.2014 Paper discussion: A Local Algorithm for Finding Well-Connected Clusters, Zeyuan Allen Zhu, Silvio Lattanzi, Vahab Mirrokni, ICML 2013 pdf
• 3.4.2014 Paper discussion: Two papers on peer grading: Piech, Koller at al. Tuned Models of Peer Assessment in MOOCs pdf Shah, Wainwright et al A Case for Ordinal Peer-evaluation in MOOCs pdf
• 23.1. 2014 Dominik Herrmann is going to talk about his PhD thesis. He used machine learning methods to investigate the computer security issues.
• 9.1.2014 Tutorial by Sven Kurras on the mean shift algorithm
• 21.11. 2013 Paper discussion: Estimating Unknown Sparsity in Compressed Sensing, Miles Lopes, ICML 2013 pdf
• 14.11. 2013 Paper discussion: Efficient Ranking from Pairwise Comparisons, Fabian Wauthier, Michael Jordan, Nebojsa Jojic, ICML 2013 pdf
• 7.11.2013 Paper discussion: Scalable Optimization of Neighbor Embedding for Visualization, Zhirong Yang, Jaakko Peltonen, Samuel Kaski, ICML 2013 pdf
• 31.10.2013 Maximum Variance Correction with Application to A* Search, Wenlin Chen, Kilian Weinberger, Yixin Chen, ICML 2013 pdf
• 25.10. 2013: Talk by Gina Gruenhage (TU Berlin). New data visualizations using cMDS: Embedding high dimensional data in a space of curves.
• 18.10.2013 Paper discussion: Robust Structural Metric Learning, Daryl Lim, Gert Lanckriet, Brian McFee, ICML 2013 pdf
• 11.10. 2013 Paper discussion: Vanishing Component Analysis, Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-Shwartz, Amir Globerson, best paper award at ICML 2013 pdf
• 26.9.2013 (16:45) Talk by Julian Busch: Randomized Algorithms for Balanced Graph Cuts (defense of his Bachelor Thesis).
• 15.7.2013 Talk by Cheng Soon Ong, Nicta Melbourne
• 10.7.2013 Paper discussion: Local equivalences of distances between clusterings - A geometric perspective. Learning Journal, 2011 pdf
• 3.7. 2013 Paper discussion: On the Hardness of Domain Adaptataion (And the Utility of Unlabeled Target Samples). ALT 2012 pdf
• 19.6. 2013 Paper discussion: Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. NIPS 2012 pdf
• 11.6.2013 Talk by Antoine Channarond
• 4.6.2013 Talk by Yoshikazu Terada
• 8.5.2013: Paper discussion: Sparse Algorithms are not Stable: A No-free-lunch Theorem. PAMI 2012. link
• 24.4. 2013: Paper discussion: Convergence and Energy Landscape for Cheeger Cut Clustering, NIPS 2012 link
• 6.3. 2013 Paper discussion: Clustering Sparse Graphs, NIPS 2012 link
• 3.4.2013 Paper discussion: Semi-supervised Eigenvectors for Locally-biased Learning, NIPS 2012 link
• 10.4.2013 Paper discussion: Learning with Partially Absorbing Random Walks, NIPS 2012 link
• 27.2.2013: Paper discussion: Clustering by Nonnegative Matrix Factorization Using Graph Random Walks. NIPS 2012 link
• 28.11.2012: Paper discussion: Maria A. Riolo, Mark Newman: First-principles multiway spectral partitioning of graphs. Arxiv, 2012
• 14.11.2012: Paper discussion: J. Lee, S. Gharan, L. Trevisan. Multi-way spectral partitioning and higher-order Cheeger inequalities. STOC 2012.
• 7.11.2012: Paper discussion: S. Dasgupta. Consistency of nearest neighbor classification under selective sampling. Twenty-Fifth Conference on Learning Theory (COLT) 2012.
• 31.10.2012: Paper discussion: A. Barabasi et al. Controllability of complex networks. Nature 2011.