לוח שנה משולב

Machine learning relies on its ability to generalize from limited data, yet a principled theoretical understanding of generalization remains incomplete. While binary classification is well understood in the classical PAC framework, even its natural extension to multiclass learning is substantially more challenging.

In this talk, I will present recent progress in multiclass learning that characterizes when generalization is possible and how much data is required, resolving a long-standing open problem on extending the Vapnik–Chervonenkis (VC) dimension beyond the binary setting. I will then turn to complementary results on efficient learning via boosting.  We extend boosting theory to multiclass classification, while maintaining computational and statistical efficiency even for unbounded label spaces.

Lastly, I will discuss generalization in sequential learning settings, where a learner interacts with an environment over time. We introduce a new framework that subsumes classically studied settings (bandits and statistical queries) together with a combinatorial parameter that bounds the number of interactions required for learning.

Bio: 

Nataly Brukhim is a postdoctoral researcher at the Institute for Advanced Study (IAS) and the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). She received her Ph.D. in Computer Science from Princeton University, where she was advised by Elad Hazan, and was a student researcher at Google AI Princeton. She earned her M.Sc. and B.Sc. in Computer Science from Tel Aviv University.

While AI models are becoming an ever-increasing part of our lives, our understanding of their behavior in unexpected situations is drifting even further out of reach. This gap poses significant risks to users, model owners, and society at large.

In the first part of the talk, I will overview my research on detecting unexpected phenomena with and within deep learning models. Specifically, detecting (i) anomalous samples, (ii) unexpected model behavior, and (iii) unexpected security threats. In the second part of the talk, I will dive into my recent research on a specific type of unexpected security threat: attacks on image watermarks. I will review such attacks and present my recent work toward addressing them. I will conclude with a discussion of future research directions.

Bio:

Niv Cohen is a postdoctoral researcher at the school of Computer Science & Engineering at New York University. He received his Ph.D. in Computer Science from the Hebrew University in 2024. His research interests include representation learning, computer vision, and AI safety. He is a recipient of the VATAT Scholarship for Outstanding Postdoctoral Fellows in Data Science and the 2024 Blavatnik Prize for Outstanding Israeli Doctoral Students in Computer Science.

Dear Colleagues,You are cordially invited to a scientific and application focused webinar entitled Vesiculab: Advancing the Extracellular Vesicle Workflow. This webinar will present state of the art approaches for improving reproducibility, analytical rigor, and translational relevance in extracellular vesicle research, with an emphasis on practical solutions for everyday laboratory workflows. The presentation will be delivered by Dr Dimitri Aubert, PhD, CEO of Vesiculab. Scientific topics include:Fast size exclusion chromatography for efficient EV isolation,Total EV staining strategies for in vitro and in vivo studies,Optimized EV sample preparation for analytical and functional assays,Calibration principles for nanoflow cytometry and fluorescence NTA,Best practices for EV handling, storage, and preservation.

Microelectrode array (MEA) technology is routinely used to measure the physiological activity of electrogenic cells. We will present two novel high-density MEA (HD-MEA) systems designed for studying neural signals at high resolution, in networks and single cells. Additionally, we will highlight key applications, including neurocomputing, present relevant data and analysis techniques, and demonstrate how our HD-MEA technology advances the study of physiological processes in biological samples