
Mathematical Machine Learning
Head:
Guido Montúfar (Email)
Phone:
+49 (0) 341 - 9959 - 880
Fax:
+49 (0) 341 - 9959 - 658
Address:
Inselstr. 22
04103 Leipzig
Math Machine Learning seminar MPI MiS + UCLA
The Math Machine Learning seminar MPI MiS + UCLA is an online seminar series (via Zoom) organized by Guido Montúfar's research group at the Max Planck Institute for Mathematics in the Sciences and UCLA. The seminar is usually Thursdays (sometimes also Fridays) at 5 pm (CEST) (GMT+2; PDT: 8-9 am). The talks are about 50 minutes with time for questions and discussion.
If you want to participate in this video broadcast please register using our registration form (see the abstract for every talk). One day before each seminar, an announcement with the Zoom link is mailed to the Math ML seminar e-mail list and the registered participants. Some talks might be officially recorded at the discretion of the speaker. Enthusiasts of mathematical machine learning are very welcome to attend the sessions!
You can find titles, abstracts and recordings of upcoming and previous seminar sessions below.
Upcoming Seminars
05.10.2023, 17:00 Uhr:
- Michael Murray (UCLA)
- Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
- Live Stream, registration required, check abstract for details
12.10.2023, 17:00 Uhr:
- Micah Goldblum (NYU)
- Bridging the gap between deep learning theory and practice
- Live Stream, registration required, check abstract for details
19.10.2023, 17:00 Uhr:
- Daniel Murfet (University of Melbourne)
- Phase transitions in learning machines
- Live Stream, registration required, check abstract for details
02.11.2023, 17:00 Uhr:
- Oscar Leong (Caltech)
- to be announced
- Live Stream, registration required, check abstract for details
Previous Seminars
02.04.2020, 11:00 Uhr:
- Yu Guang Wang (University of New South Wales, Sydney)
- Haar Graph Pooling
- see Abstract
- see video
09.04.2020, 17:00 Uhr:
- Michael Arbel (University College London)
- Kernelized Wasserstein Natural Gradient
- see video
16.04.2020, 17:00 Uhr:
- Anton Mallasto (University of Copenhagen)
- Estimation of Optimal Transport in Generative Models
- see video
23.04.2020, 17:00 Uhr:
- Johannes Mueller (MPI MiS, Leipzig)
- Deep Ritz Revisited
- see video
30.04.2020, 17:00 Uhr:
- Quynh Nguyen (University Saarbruecken)
- Loss surface of deep and wide neural networks
- see video
07.05.2020, 17:00 Uhr:
- Kathlén Kohn (KTH Royal Institute of Technology)
- The geometry of neural networks
- see video
14.05.2020, 17:00 Uhr:
- Benjamin Fehrman (University of Oxford)
- Convergence rates for the stochastic gradient descent method for non-convex objective functions
- see video
21.05.2020, 17:00 Uhr:
- Dennis Elbrächter (Universität Wien)
- How degenerate is the parametrization of (ReLU) neural networks?
- see video
28.05.2020, 17:00 Uhr:
- Jonathan Frankle (Massachusetts Institute of Technology)
- The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks
04.06.2020, 17:00 Uhr:
- Ulrich Terstiege (Rheinisch-Westfälische Technische Hochschule Aachen)
- Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
11.06.2020, 17:00 Uhr:
- Poorya Mianjy (Johns Hopkins University)
- Understanding the Algorithmic Regularization due to Dropout
- see video
18.06.2020, 17:00 Uhr:
- Alessandro Achille (University of California, Los Angeles)
- Structure of Learning Tasks and the Information in the Weights of a Deep Network
- see video
25.06.2020, 17:00 Uhr:
- Adam Gaier (INRIA)
- Weight Agnostic Neural Networks
02.07.2020, 17:00 Uhr:
- Wenda Zhou (Columbia University)
- New perspectives on cross-validation
- see video
03.07.2020, 17:00 Uhr:
- Kai Fong Ernest Chong (Singapore University of Technology and Design)
- The approximation capabilities of neural networks
- see video
10.07.2020, 17:00 Uhr:
- Nasim Rahaman (MPI-IS Tübingen, and Mila, Montréal)
- On the Spectral Bias of Neural Networks
- see video
23.07.2020, 17:00 Uhr:
- Léonard Blier (Facebook AI Research, Université Paris Saclay, Inria)
- The Description Length of Deep Learning Models
30.07.2020, 17:00 Uhr:
- Robert Peharz (TU Eindhoven)
- Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
- see video
31.07.2020, 17:00 Uhr:
- Greg Ongie (University of Chicago)
- A function space view of overparameterized neural networks
- see video
06.08.2020, 17:00 Uhr:
- Simon S. Du (University of Washington)
- Ultra-wide Neural Network and Neural Tangent Kernel
- see video
13.08.2020, 17:00 Uhr:
- Lénaïc Chizat (CNRS - Laboratoire de Mathématiques d'Orsay)
- Analysis of Gradient Descent on Wide Two-Layer ReLU Neural Networks
- see video
14.08.2020, 17:00 Uhr:
- Ido Nachum (École Polytechnique Fédérale de Lausanne)
- Regularization by Misclassification in ReLU Neural Networks
- see video
20.08.2020, 17:00 Uhr:
- Arthur Jacot (École Polytechnique Fédérale de Lausanne)
- Neural Tangent Kernel: Convergence and Generalization of DNNs
- see video
21.08.2020, 17:00 Uhr:
- Preetum Nakkiran (Harvard University)
- Distributional Generalization: A New Kind of Generalization
- see video
27.08.2020, 17:00 Uhr:
- Felix Draxler (Heidelberg University)
- Characterizing The Role of A Single Coupling Layer in Affine Normalizing Flows
- see video
03.09.2020, 17:00 Uhr:
- Niladri S. Chatterji (UC Berkeley)
- Upper and lower bounds for gradient based sampling methods
- see video
04.09.2020, 17:00 Uhr:
- Nadav Cohen (Tel Aviv University)
- Analyzing Optimization and Generalization in Deep Learning via Dynamics of Gradient Descent
- see video
17.09.2020, 17:00 Uhr:
- Mahito Sugiyama (National Institute of Informatics, JST, PRESTO)
- Learning with Dually Flat Structure and Incidence Algebra
18.09.2020, 17:00 Uhr:
- Mert Pilanci (Stanford University)
- Exact Polynomial-time Convex Formulations for Training Multilayer Neural Networks: The Hidden Convex Optimization Landscape
24.09.2020, 17:00 Uhr:
- Liwen Zhang (University of Chicago)
- Tropical Geometry of Deep Neural Networks
- see video
25.09.2020, 17:00 Uhr:
- Randall Balestriero (Rice University)
- Max-Affine Spline Insights into Deep Networks
- see video
01.10.2020, 17:00 Uhr:
- David Rolnick (McGill University & Mila)
- Expressivity and learnability: linear regions in deep ReLU networks
- see video
08.10.2020, 17:00 Uhr:
- Yaoyu Zhang (Shanghai Jiao Tong University)
- Impact of initialization on generalization of deep neural networks
- see video
15.10.2020, 17:00 Uhr:
- Guy Blanc (Stanford University)
- Provable Guarantees for Decision Tree Induction
- see video
22.10.2020, 17:00 Uhr:
- Boris Hanin (Princeton University)
- Neural Networks at Finite Width and Large Depth
- see video
29.10.2020, 17:00 Uhr:
- Yaim Cooper (Institute for Advanced Study, Princeton)
- The geometry of the loss function of deep neural networks
05.11.2020, 17:00 Uhr:
- Kenji Kawaguchi (MIT)
- Deep learning: theoretical results on optimization and mixup
12.11.2020, 17:00 Uhr:
- Yasaman Bahri (Google Brain)
- The Large Learning Rate Phase of Wide, Deep Neural Networks
- see video
20.11.2020, 17:00 Uhr:
- Amartya Sanyal (University of Oxford)
- How benign is benign overfitting?
- see video
17.12.2020, 17:00 Uhr:
- Tim G. J. Rudner (University of Oxford)
- Outcome-Driven Reinforcement Learning via Variational Inference
14.01.2021, 17:00 Uhr:
- Mahdi Soltanolkotabi (University of Southern California)
- Learning via early stopping and untrained neural nets
- see video
21.01.2021, 17:00 Uhr:
- Taiji Suzuki (The University of Tokyo, and Center for Advanced Intelligence Project, RIKEN, Tokyo)
- Statistical efficiency and optimization of deep learning from the viewpoint of non-convexity
- see video
28.01.2021, 17:00 Uhr:
- Suriya Gunasekar (Microsoft Research, Redmond)
- Rethinking the role of optimization in learning
- see video
04.02.2021, 17:00 Uhr:
- Tailin Wu (Stanford University)
- Phase transitions on the universal tradeoff between prediction and compression in machine learning
- see video
11.02.2021, 17:00 Uhr:
- Samuel L. Smith (DeepMind)
- A Backward Error Analysis for Random Shuffling SGD
- see video
18.02.2021, 17:00 Uhr:
- Umut Şimşekli (INRIA - École Normale Supérieure (Paris))
- Towards Building a Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
- see video
25.02.2021, 17:00 Uhr:
- Zhiyuan Li (Princeton University)
- Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
- see video
04.03.2021, 17:00 Uhr:
- Marcus Hutter (DeepMind and Australian National University)
- Learning Curve Theory
- see video
11.03.2021, 17:00 Uhr:
- Spencer Frei (Department of Statistics, UCLA)
- Generalization of SGD-trained neural networks of any width in the presence of adversarial label noise
- see video
18.03.2021, 17:00 Uhr:
- Ryo Karakida (AIST (National Institute of Advanced Industrial Science and Technology), Tokyo)
- Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
- see video
25.03.2021, 17:00 Uhr:
- Pratik Chaudhari (University of Pennsylvania)
- Learning with few labeled data
- see video
01.04.2021, 17:00 Uhr:
- Roi Livni (Tel Aviv University)
- Regularization, what is it good for?
- see video
08.04.2021, 17:00 Uhr:
- Cristian Bodnar (University of Cambridge)
- Fabrizio Frasca (Twitter)
- Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
- see video
15.04.2021, 17:00 Uhr:
- Stanislav Fort (Stanford University)
- Neural Network Loss Landscapes in High Dimensions: Theory Meets Practice
22.04.2021, 17:00 Uhr:
- Matus Jan Telgarsky (UIUC)
- Recent advances in the analysis of the implicit bias of gradient descent on deep networks
- see video
29.04.2021, 17:00 Uhr:
- Mehrdad Farajtabar (DeepMind)
- Catastrophic Forgetting in Continual Learning of Neural Networks
- see video
06.05.2021, 17:00 Uhr:
- Haizhao Yang (Purdue University)
- A Few Thoughts on Deep Learning-Based PDE Solvers
- see video
13.05.2021, 17:00 Uhr:
- Eirikur Agustsson (Google research)
- Universally Quantized Neural Compression
27.05.2021, 17:00 Uhr:
- Sebastian Reich (University of Potsdam and University of Reading)
- Statistical inverse problems and affine-invariant gradient flow structures in the space of probability measures
- see slides
03.06.2021, 17:00 Uhr:
- Stanisław Jastrzębski (Molecule.one and Jagiellonian University)
- Reverse-engineering implicit regularization due to large learning rates in deep learning
- see video
10.06.2021, 17:00 Uhr:
- Isabel Valera (Saarland University and MPI for Intelligent Systems)
- Algorithmic recourse: theory and practice
- see video
17.06.2021, 17:00 Uhr:
- Ziv Goldfeld (Cornell University)
- Scaling Wasserstein distances to high dimensions via smoothing
- see video
01.07.2021, 17:00 Uhr:
- Dominik Janzing (Amazon Research, Tuebingen, Germany)
- Why we should prefer simple causal models
15.07.2021, 17:00 Uhr:
- Hossein Mobahi (Google Research)
- Self-Distillation Amplifies Regularization in Hilbert Space
- see video
22.07.2021, 17:00 Uhr:
- Soledad Villar (Johns Hopkins University)
- Scalars are universal: Gauge-equivariant machine learning, structured like classical physics
- see video
26.08.2021, 17:00 Uhr:
- Guodong Zhang (University of Toronto)
- Differentiable Game Dynamics: Hardness and Complexity of Equilibrium Learning
- see video
02.09.2021, 17:00 Uhr:
- Matthew Trager (Amazon US)
- A Geometric View of Functional Spaces of Neural Networks
09.09.2021, 17:00 Uhr:
- Shaowei Lin (working in a stealth startup)
- All you need is relative information
- see video
16.09.2021, 17:00 Uhr:
- Pavel Izmailov (New York University)
- What Are Bayesian Neural Network Posteriors Really Like?
- see video
23.09.2021, 17:00 Uhr:
- Huan Xiong (MBZUAI, Abu Dhabi)
- On the Number of Linear Regions of Convolutional Neural Networks with Piecewise Linear Activations
- see video
07.10.2021, 17:00 Uhr:
- Roger Grosse (University of Toronto)
- Self-tuning networks: Amortizing the hypergradient computation for hyperparameter optimization
- see video
14.10.2021, 17:00 Uhr:
- Mathias Drton (Technical University of Munich)
- A Bayesian information criterion for singular models
- see video
21.10.2021, 17:00 Uhr:
- David Stutz (MPI for Informatics, Saarbrücken)
- Relating Adversarial Robustness and Weight Robustness Through Flatness
- see video
04.11.2021, 17:00 Uhr:
- Hyeyoung Park (Kyungpook National University)
- Effect of Geometrical Singularity on Learning Dynamics of Neural Networks
- see video
11.11.2021, 17:00 Uhr:
- Christoph Hertrich (TU Berlin)
- Towards Lower Bounds on the Depth of ReLU Neural Networks
- see video
18.11.2021, 17:00 Uhr:
- Daniel Soudry (Technion)
- Algorithmic Bias Control in Deep learning
- see video
25.11.2021, 17:00 Uhr:
- Michael Joswig (TU Berlin)
- Geometric Disentanglement by Random Convex Polytopes
- see video
02.12.2021, 17:00 Uhr:
- Ekaterina Lobacheva (HSE University)
- Maxim Kodryan (HSE University)
- On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
- see video
16.12.2021, 17:00 Uhr:
- Zhi-Qin John Xu (Shanghai Jiao Tong University)
- Occam’s razors in neural networks: frequency principle in training and embedding principle of loss landscape
- see video
13.01.2022, 17:00 Uhr:
- Yuan Cao (University of California, LA)
- Towards Understanding and Advancing the Generalization of Adam in Deep Learning
- see video
20.01.2022, 17:00 Uhr:
- Philipp Petersen (University of Vienna)
- Optimal learning in high-dimensional classification problems
- see video
27.01.2022, 17:00 Uhr:
- Daniel Russo (Columbia University)
- Adaptivity and Confounding in Multi-armed Bandit Experiments
- see video
04.02.2022, 17:00 Uhr:
- Song Mei (University of California, Berkeley)
- The efficiency of kernel methods on structured datasets
- see video
10.02.2022, 17:00 Uhr:
- Hui Jin (University of California, Los Angeles)
- Learning curves for Gaussian process regression with power-law priors and targets
- see video
17.02.2022, 17:00 Uhr:
- Maksim Velikanov (Skolkovo Institute of Science and Technology)
- Spectral properties of wide neural networks and their implications to the convergence speed of different Gradient Descent algorithms
24.02.2022, 17:00 Uhr:
- Danijar Hafner (Google Brain & University of Toronto)
- General Infomax Agents through World Models
- see video
- see slides
03.03.2022, 17:00 Uhr:
- Marco Mondelli (Institute of Science and Technology Austria)
- Gradient Descent for Deep Neural Networks: New Perspectives from Mean-field and NTK
- see video
- see slides
04.03.2022, 17:00 Uhr:
- Daniel McKenzie (UCLA)
- Implicit Neural Networks: What they are and how to use them.
- see video
10.03.2022, 17:00 Uhr:
- Gergely Neu (Universitat Pompeu Fabra, Barcelona)
- Generalization Bounds via Convex Analysis
- see video
11.03.2022, 17:00 Uhr:
17.03.2022, 17:00 Uhr:
- Gabin Maxime Nguegnang (RWTH Aachen University)
- Convergence of gradient descent for learning deep linear neural networks
- see video
24.03.2022, 17:00 Uhr:
- Ohad Shamir (Weizmann Institute of Science)
- Implicit bias in machine learning
- see video
- see slides
31.03.2022, 17:00 Uhr:
- Benjamin Eysenbach (Carnegie Mellon University & Google Brain)
- The Information Geometry of Unsupervised Reinforcement Learning
- see video
07.04.2022, 17:00 Uhr:
- Chaoyue Liu (Ohio State University)
- Transition to Linearity of Wide Neural Networks
- see video
14.04.2022, 17:00 Uhr:
- Christoph Lampert (Institute of Science and Technology, Austria)
- Robust and Fair Multisource Learning
- see video
21.04.2022, 17:00 Uhr:
- Berfin Şimşek (Ecole Polytechnique Fédérale de Lausanne (EPFL))
- The Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetry-Induced Saddles and Global Minima Manifold
- see video
- see slides
05.05.2022, 17:00 Uhr:
- Noam Razin (Tel Aviv University)
- Generalization in Deep Learning Through the Lens of Implicit Rank Minimization
- see slides
12.05.2022, 17:00 Uhr:
19.05.2022, 17:00 Uhr:
- Sayan Mukherjee (MPI MiS, Leipzig + Universität Leipzig)
- Inference in dynamical systems and the geometry of learning group actions
- see video
26.05.2022, 17:00 Uhr:
- Melikasadat Emami (UCLA)
- Asymptotics of Learning in Generalized Linear Models and Recurrent Neural Networks
- see video
02.06.2022, 17:00 Uhr:
- Tan Nguyen (UCLA)
- Principled Models for Machine Learning
- see video
09.06.2022, 17:00 Uhr:
- Guang Cheng (UCLA)
- Nonparametric Perspective on Deep Learning
- see video
16.06.2022, 17:00 Uhr:
- Jingfeng Wu (Johns Hopkins University)
- A Fine-Grained Characterization for the Implicit Bias of SGD in Least Square Problems
- see video
- see slides
07.07.2022, 17:00 Uhr:
- Sebastian Kassing (University of Bielefeld)
- Convergence of Stochastic Gradient Descent for analytic target functions
- see video
- see slides
14.07.2022, 17:00 Uhr:
- Rong Ge (Duke University)
- Towards Understanding Training Dynamics for Mildly Overparametrized Models
- see video
21.07.2022, 17:00 Uhr:
- Mark Schmidt (University of British Columbia)
- Optimization Algorithms for Training Over-Parameterized Models
- see video
- see slides
28.07.2022, 17:00 Uhr:
- Arash A. Amini (UCLA)
- Target alignment in truncated kernel ridge regression
- see video
04.08.2022, 17:00 Uhr:
- Wei Hu (University of California, Berkeley)
- More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
- see video
- see slides
12.08.2022, 17:00 Uhr:
- Francesco Di Giovanni (Twitter)
- Over-squashing and over-smoothing through the lenses of curvature and multi-particle dynamics
- see video
- see slides
18.08.2022, 17:00 Uhr:
- Aditya Golatkar (UCLA)
- Selective Forgetting and Mixed Privacy in Deep Networks
- see video
25.08.2022, 17:00 Uhr:
- Melanie Weber (Harvard University)
- Exploiting geometric structure in (matrix-valued) optimization
- see video
08.09.2022, 17:00 Uhr:
- Devansh Arpit (Salesforce)
- Optimization Aspects that Improve IID and OOD Generalization in Deep Learning
- see video
- see slides
15.09.2022, 17:00 Uhr:
- Lisa Maria Kreusser (University of Bath)
- Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
- see slides
22.09.2022, 17:00 Uhr:
- Gintare Karolina Dziugaite (Google Brain)
- Deep Learning through the Lens of Data
- see video
06.10.2022, 17:00 Uhr:
- Felix Dietrich (Technical University of Munich)
- Learning dynamical systems from data
- see video
13.10.2022, 17:00 Uhr:
- Chao Ma (Stanford University)
- Implicit bias of optimization algorithms for neural networks: static and dynamic perspectives
- see video
- see slides
20.10.2022, 17:00 Uhr:
- Joe Kileel (University of Texas at Austin)
- The Effect of Parametrization on Nonconvex Optimization Landscapes
- see video
27.10.2022, 17:00 Uhr:
- Lechao Xiao (Google Brain)
- Eigenspace restructuring: A Principle of space and frequency in neural networks
- see video
03.11.2022, 17:00 Uhr:
- Itay Safran (Purdue University)
- On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
- see video
10.11.2022, 17:00 Uhr:
- El Mehdi Achour (IMT Toulouse)
- The loss landscape of deep linear neural networks: A second-order analysis
- see video
- see slides
17.11.2022, 17:00 Uhr:
- Sebastian Goldt (SISSA Trieste)
- On the importance of higher-order input statistics for learning in neural networks
01.12.2022, 17:00 Uhr:
- Levon Nurbekyan (UCLA)
- Efficient natural gradient method for large-scale optimization problems
- see video
08.12.2022, 17:00 Uhr:
- Mojtaba Sahraee-Ardakan (UCLA)
- Equivalence of Kernel Methods and Linear Models in High Dimensions
- see video
15.12.2022, 17:00 Uhr:
- Nicolás García Trillos (University of Wisconsin-Madison)
- Analysis of adversarial robustness and of other problems in modern machine learning
- see video
- see slides
16.12.2022, 17:00 Uhr:
- Jona Lelmi (University of Bonn)
- Large data limit of the MBO scheme for data clustering
- see video
- see slides
12.01.2023, 17:00 Uhr:
- Felipe Suárez-Colmenares (MIT)
- Learning threshold neurons via the "edge of stability”
- see video
- see slides
19.01.2023, 17:00 Uhr:
- Xiaowu Dai (UCLA)
- Kernel Ordinary Differential Equations
- see video
26.01.2023, 17:00 Uhr:
- Liu Ziyin (University of Tokyo)
- The Probabilistic Stability and Low-Rank Bias of SGD
- see video
02.02.2023, 17:00 Uhr:
- Mihaela Rosca (DeepMind)
- On continuous time models of gradient descent and instability in deep learning
- see video
09.02.2023, 17:00 Uhr:
- Renjie Liao (University of British Columbia)
- Gaussian-Bernoulli RBMs Without Tears
- see video
16.02.2023, 17:00 Uhr:
- Julia Elisenda Grigsby (Boston College)
- Functional dimension of ReLU Networks
- see video
23.02.2023, 17:00 Uhr:
- Jalaj Bhandari (Columbia University)
- Global guarantees for policy gradient methods.
- see video
- see slides
02.03.2023, 17:00 Uhr:
- Samy Wu Fung (Colorado School of Mines)
- Using Hamilton Jacobi PDEs in Optimization
- see video
- see slides
09.03.2023, 17:00 Uhr:
- Ricardo S. Baptista (Caltech)
- On the representation and learning of triangular transport maps
- see video
- see slides
16.03.2023, 17:00 Uhr:
- Chulhee Yun (KAIST)
- Shuffling-based stochastic optimization methods: bridging the theory-practice gap
- see video
23.03.2023, 17:00 Uhr:
- Dohyun Kwon (University of Wisconsin-Madison)
- Convergence of score-based generative modeling
- see video
06.04.2023, 17:00 Uhr:
- Jaehoon Lee (Google Brain)
- Exploring Infinite-Width Limit of Deep Neural Networks
- see video
13.04.2023, 17:00 Uhr:
- Wei Zhu (University of Massachusetts Amherst)
- Implicit Bias of Linear Equivariant Steerable Networks
- see video
- see slides
20.04.2023, 17:00 Uhr:
- Aditya Grover (UCLA)
- Generative Decision Making Under Uncertainty
- see video
27.04.2023, 17:00 Uhr:
- Stéphane d'Ascoli (ENS and FAIR Paris)
- Double descent: insights from the random feature model
- see video
- see slides
11.05.2023, 17:00 Uhr:
- Henning Petzka (RWTH Aachen University)
- Relative flatness and generalization
- see video
18.05.2023, 17:00 Uhr:
- Duc N.M Hoang (University of Texas, Austin)
- Revisiting Pruning as Initialization through the Lens of Ramanujan Graph
- see video
25.05.2023, 17:00 Uhr:
- Łukasz Dębowski (IPI PAN)
- A Simplistic Model of Neural Scaling Laws: Multiperiodic Santa Fe Processes
- Link
- see slides
01.06.2023, 17:00 Uhr:
- Rishi Sonthalia (UCLA)
- Least Squares Denoising: Non-IID Data, Transfer Learning and Under-parameterized Double Descent
- see video
- see slides
08.06.2023, 17:00 Uhr:
- Simone Bombari (IST Austria)
- Optimization, Robustness and Privacy. A Story through the Lens of Concentration
- see video
- see slides
15.06.2023, 17:00 Uhr:
- Benjamin Bowman (UCLA and AWS)
- On the Spectral Bias of Neural Networks in the Neural Tangent Kernel Regime
- see slides
29.06.2023, 17:00 Uhr:
- Bingbin Liu (Carnegie Mellon University)
- Thinking Fast with Transformers: Algorithmic Reasoning via Shortcuts
- see slides
06.07.2023, 17:00 Uhr:
- Ronen Basri (Weizmann Institute of Science)
- On the Connection between Deep Neural Networks and Kernel Methods
- see video
13.07.2023, 17:00 Uhr:
- Behrooz Tahmasebi (MIT)
- Sample Complexity Gain from Invariances: Kernel Regression, Wasserstein Distance, and Density Estimation
- see video
20.07.2023, 17:00 Uhr:
- Sitan Chen (Harvard University)
- Theory for Diffusion Models
- see video
- see slides
27.07.2023, 17:00 Uhr:
- Sanghamitra Dutta (University of Maryland)
- Foundations of Reliable and Lawful Machine Learning: Approaches Using Information Theory
- see video
- see slides
03.08.2023, 17:00 Uhr:
- Haoyuan Sun (MIT)
- A Unified Approach to Controlling Implicit Regularization via Mirror Descent
- see video
17.08.2023, 17:00 Uhr:
- Surbhi Goel (University of Pennsylvania)
- Beyond Worst-case Sequential Prediction: Adversarial Robustness via Abstention
- see video
31.08.2023, 17:00 Uhr:
- Tom Tirer (Bar-Ilan University)
- Exploring Deep Neural Collapse via Extended and Penalized Unconstrained Features Models
- see video
07.09.2023, 17:00 Uhr:
- Rahul Parhi (EPFL)
- Deep Learning Meets Sparse Regularization
- see video
- see slides
14.09.2023, 17:00 Uhr:
- Vidya Muthukumar (Georgia Institute of Technology)
- Classification versus regression in overparameterized regimes: Does the loss function matter?
- see video
21.09.2023, 17:00 Uhr:
- Florian Schaefer (Georgia Tech)
- Solvers, Models, Learners: Statistical Inspiration for Scientific Computing