סמינרים

• Reinforcement Learning Algorithm for Learning Shadow Variables in Combinatorial Optimization Problems

  Abstract: Many real-world decision-making problems are modeled as Mixed-Integer Linear Optimization (MILO) problems, and are solved by exact tree-search-based optimization algorithms. Some knowledge, such as preferences for the solution, often exists but is not explicitly modeled. We examine incorporating such knowledge as additional constraints to potentially shorten optimization. A wise choice of "equality preferences" (i.e.,… Continue Reading Reinforcement Learning Algorithm for Learning Shadow Variables in Combinatorial Optimization Problems

  ---

• First-Order Methods for Two-Stage Stochastic Optimization

  Abstract: The emergence of big data has highlighted the growing importance of data-driven approaches in stochastic optimization. Sample Average Approximation (SAA) is a widely used method, known for its simplicity and compatibility with first-order optimization techniques. However, SAA often exhibits overfitting when the amount of data is limited, and it struggles to obtain feasible solution… Continue Reading First-Order Methods for Two-Stage Stochastic Optimization

  ---

• Deep Learning for approximating solutions to hard computational problems indirectly

  Abstract: Computational reduction serves as a powerful tool in optimization by enabling the translation between different computational problems while preserving key structural and computational properties. This allows one problem to be reformulated in terms of another, often facilitating the solution of otherwise complex problems. Since reductions are mostly manual, it is plausible that alternative, better… Continue Reading Deep Learning for approximating solutions to hard computational problems indirectly

  ---

• Computational and Statistical Limits in Modern Machine Learning – Job Talk

  Abstract: Modern machine learning systems operate in regimes that challenge classical learning-theoretic assumptions. Models are highly overparameterized, trained with simple optimization algorithms, and rely critically on how data is collected and curated. Understanding the limits of learning in these settings requires revisiting both the computational and statistical foundations of learning theory. A central question in learning… Continue Reading Computational and Statistical Limits in Modern Machine Learning – Job Talk

  ---

• Algorithms for Structured Simple Bilevel Problems

  Abstract: Simple convex bilevel optimization problems, in which we seek to minimize an (outer) objective function over a feasible set, which itself is the set of minimizers of another (inner) function. Such problems can be found in the machine learning and signal processing applications. In this work, we address the case where both outer and… Continue Reading Algorithms for Structured Simple Bilevel Problems

  ---

• Bridging Generative Models and Visual Communication – Job Talk

  Abstract: From rough sketches that spark ideas to polished designs that explain complex concepts, visual communication is central to how humans think, create, and share knowledge. Yet despite major advances in generative AI, we are still far from models that can reason and communicate through visual forms. I will present my work on bridging generative models… Continue Reading Bridging Generative Models and Visual Communication – Job Talk

  ---

• Bridging Generative Models and Visual Communication – Job Talk

  Abstract: From rough sketches that spark ideas to polished designs that explain complex concepts, visual communication is central to how humans think, create, and share knowledge. Yet despite major advances in generative AI, we are still far from models that can reason and communicate through visual forms. I will present my work on bridging generative models… Continue Reading Bridging Generative Models and Visual Communication – Job Talk

  ---

• Bridging Generative Models and Visual Communication – Job Talk

  Abstract: From rough sketches that spark ideas to polished designs that explain complex concepts, visual communication is central to how humans think, create, and share knowledge. Yet despite major advances in generative AI, we are still far from models that can reason and communicate through visual forms. I will present my work on bridging generative models… Continue Reading Bridging Generative Models and Visual Communication – Job Talk

  ---

• Fundamentals of Aligning General-Purpose AI – Job Talk

  Abstract: The field of artificial intelligence (AI) is undergoing a paradigm shift, moving from neural networks trained for narrowly defined tasks (e.g., image classification and machine translation) to general-purpose models such as ChatGPT. These models are trained at unprecedented scales to perform a wide range of tasks, from providing travel recommendations to solving Olympiad-level math problems.… Continue Reading Fundamentals of Aligning General-Purpose AI – Job Talk

  ---

אנשים

• 

  לוין אסף

  Professor Asaf Levin joined the Technion in 2008. He received his Ph.D. in Operations Research from Tel Aviv University in 2003. From 2003 to 2004 he was a Postdoctoral Fellow at the Minerva Optimization Center, the Technion, then, he joined the Department of Statistics at the Hebrew University of Jerusalem as a lecturer. Prof. Levin… Continue Reading לוין אסף

  ---