סמינרים
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Algorithmic Contract Design
Abstract: We explore the framework of contract design through a computational perspective. Contract design is a fundamental pillar of microeconomics, addressing the essential question of how to incentivize people to work. The significance of contract design was acknowledged by the Nobel Prize awarded to Hart and Holmström, and it applies to various real-life scenarios, such… Continue Reading Algorithmic Contract Design
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When Less Is More: Overcoming Algorithm Aversion with Biased Advice
ABSTRACT In an era of rapid technological advancement, decision-making processes across various domains increasingly integrate algorithmic systems. Despite algorithms often outperforming human experts, individuals frequently exhibit algorithm aversion- a tendency to prefer human judgment even when algorithmic recommendations are more accurate. Previous research finds that people who develop experience-based task expertise often provide biased advice,… Continue Reading When Less Is More: Overcoming Algorithm Aversion with Biased Advice
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The Structure of Cross-Validation Error: Stability, Covariance, and Minimax Limits
Abstract: Despite extensive theoretical work on cross-validation (CV), many foundational questions about its statistical behavior remain open. In this talk, I’ll explore how the interplay between algorithmic and distributional properties influences the optimal choice of the number of folds in k-fold CV. I’ll present a new decomposition of the mean-squared error (MSE) of CV risk estimates that… Continue Reading The Structure of Cross-Validation Error: Stability, Covariance, and Minimax Limits
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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
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The Agent Perspective In LLM-Based Strategic Information Retrieval Ecosystems
Abstract: Information retrieval ecosystems are strategic environments in which publishers, mediators, advertisers, and users interact under mechanisms that allocate visibility and revenue. The integration of large language models (LLMs) into search and question answering systems fundamentally reshapes these dynamics, altering ranking incentives and long-term corpus evolution. My dissertation develops a unified strategic perspective on LLM-based… Continue Reading The Agent Perspective In LLM-Based Strategic Information Retrieval Ecosystems
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Efficient LLM Systems: From Algorithm Design to Deployment – Job Talk
Abstract: Large Language Models (LLMs) have transformed what machines can do and how systems are designed to serve them. These models are both computationally and memory demanding, revealing the limits of traditional optimization methods that once sufficed for conventional systems. A central challenge in building LLM systems is improving system metrics while ensuring response quality.… Continue Reading Efficient LLM Systems: From Algorithm Design to Deployment – Job Talk
