On May 26,  Dr. Or Litany from the Technion will give a talk titled: Real-to-Sim: Towards interpretable and controllable digital twins Abstract: Do we live in a simulation? Perhaps we should consider the possibility. Replicating real-world observations into a digital twin offers numerous potential benefits. For instance, in autonomous navigation, one could recreate safety-critical scenarios ... Read more

On May 26,  Dr. Kfir Levy from the Technion will give a talk titled: Do Stochastic, Feel Noiseless: Stable Optimization via a Double Momentum Mechanism Abstract: The tremendous success of the Machine Learning paradigm heavily relies on the development of powerful optimization methods, and the canonical algorithm for training learning models is SGD (Stochastic Gradient ... Read more

On June 9,  Dr. Wasim Huleihel from the Tel Aviv university will give a talk titled: Testing for Dependency of Databases Abstract: In this talk, we investigate the problem of detecting the dependency between two random databases represented as matrices. This is formalized as a hypothesis testing problem, where under the null hypothesis, the two ... Read more

On June 16,  Dr. Ronen Talmon from the Technion will give a talk titled: Revealing Latent Hierarchical Structures in High-Dimensional Data Using Hyperbolic Representations Abstract: The tremendous success of the Machine Learning paradigm heavily relies on the development of powerful optimization methods, and the canonical algorithm for training learning models is SGD (Stochastic Gradient Descent). ... Read more

On June 23,  we will have 4 BIU Students giving the following talks on their research progress. First hour (12:00-13:00) will be dedicated for the students talks Second hour (13:00 - 14:00) for networking. 12:00 - 12:15 Presenter: Osnat Drien Lab Head: Prof. Yael Amsterdamer Title: Query-Guided Resolution in Uncertain Databases Abstract: We present a ... Read more

On June 30,  Dr. Nadav Cohen from the Tel Aviv University will give a talk titled: What Makes Data Suitable for Deep Learning? Abstract: Deep learning is delivering unprecedented performance when applied to various data modalities, yet there are data distributions over which it utterly fails. The question of what makes a data distribution suitable ... Read more

On July 7,  Prof. Aryeh Kontorovich from the Tel Aviv University will give a talk titled: Local Glivenko-Cantelli (or: estimating the mean in infinite dimensions) Abstract: If μ is a distribution over the d-dimensional Boolean cube {0,1}ᵈ, our goal is to estimate its mean p∈ᵈ based on n iid draws from μ. Specifically, we consider ... Read more

On July 14,  Dr. Adam Hakim from Microsoft WSSI will give a talk titled: Protecting AI From Theft with 2-Party Security Abstract: Large language models (LLMs) have recently seen widespread adoption, in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting enormous investments by their owners. Moreover, the high ... Read more

On November 18,  Dr. Haggai Maron from the Technion will give a talk titled: Exploiting Symmetries for Learning in Deep Weight Spaces Abstract: This talk explores the emerging research direction that studies neural network weights as a novel data modality. We'll discuss recent advances in processing and analyzing raw weight matrices, which exhibit inherent symmetries ... Read more

On November 25,  Dr. Nir Weinberger from the Technion will give a talk titled: Statistical curriculum learning -- An elimination algorithm achieving the weak oracle risk Abstract: Curriculum Learning (CL) is a successful machine learning strategy that improves a learner’s performance by ordering the tasks according to difficulty, similarly to the way humans learn.  However, ... Read more

On December 1 (Note this is Sunday),  Dr. Yehuda Dar from the Ben Gurion University will give a talk titled: Generalization in Overparameterized Machine Learning Abstract: Modern machine learning models are highly overparameterized (i.e., they are very complex with many more parameters than the number of training data examples); yet, these models often generalize extremely ... Read more