IDEAS
Calendar of Events
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ESE Spring Seminar – “Machine Learning: Algorithmic and Economic Perspectives”
ESE Spring Seminar – “Machine Learning: Algorithmic and Economic Perspectives”
Algorithms are increasingly integrated into various societal applications, often directly interacting with people and communities. This highlights the importance of understanding the interplay between algorithmic decisions and economic incentives when designing machine learning algorithms. In this talk, I will explore two examples of this dynamic through the lens of privacy in data markets and fairness […]
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IDEAS/STAT Optimization Seminar: “Foundations of Deep Learning: Optimization and Representation Learning”
IDEAS/STAT Optimization Seminar: “Foundations of Deep Learning: Optimization and Representation Learning”
Deep learning's success stems from the ability of neural networks to automatically discover meaningful representations from raw data. In this talk, I will describe some recent insights into how optimization enables this learning process. First, I will show how optimization algorithms exhibit surprisingly rich dynamics when training neural networks, and how these complex dynamics are […]
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ESE Spring Seminar – “AI as a Lens: Expanding Vision for Scientific Discovery”
ESE Spring Seminar – “AI as a Lens: Expanding Vision for Scientific Discovery”
Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden in the data we already have. The key lies in using AI not as a black-box predictor, but as a tool for interpreting data through […]
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IDEAS/STAT Optimization Seminar: “Theoretical foundations for multi-agent learning”
IDEAS/STAT Optimization Seminar: “Theoretical foundations for multi-agent learning”
As learning algorithms become increasingly capable of acting autonomously, it is important to better understand the behavior that results from their interactions. For example, a pervasive challenge in multi-agent learning settings, which spans both theory and practice and dates back decades, has been the failure of convergence for iterative algorithms such as gradient descent. Accordingly, […]
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MEAM Seminar: “Neural Operator for Scientific Computing”
MEAM Seminar: “Neural Operator for Scientific Computing”
Accurate simulations of physical phenomena governed by partial differential equations (PDEs) are foundational to scientific computing. While traditional numerical methods have proven effective, they remain computationally intensive, particularly for complex, large-scale systems. This talk introduces the neural operator, a machine learning framework that approximates solution operators in infinite-dimensional spaces, enabling efficient and scalable PDE simulations […]
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IDEAS/STAT Optimization Seminar: “ML for an Interactive World: From Learning to Unlearning”
IDEAS/STAT Optimization Seminar: “ML for an Interactive World: From Learning to Unlearning”
The remarkable recent success of Machine Learning (ML) is driven by our ability to develop and deploy interactive models that can solve complicated tasks by understanding and adapting to the ever-changing state of the world. However, the development of such models demands significant data and computing resources. Moreover, as these models increasingly interact with humans, […]