Fairness and Robustness in Federated Learning with Virginia Smith -#504

Fairness and Robustness in Federated Learning with Virginia Smith -#504

Today we kick off our ICML coverage joined by Virginia Smith, an assistant professor in the Machine Learning Department at Carnegie Mellon University. In our conversation with Virginia, we explore her work on cross-device federated learning applications, including where the distributed learning aspects of FL are relative to the privacy techniques. We dig into her paper from ICML, Ditto: Fair and Robust Federated Learning Through Personalization, what fairness means in contrast to AI ethics, the particulars of the failure modes, the relationship between models, and the things being optimized across devices, and the tradeoffs between fairness and robustness. We also discuss a second paper, Heterogeneity for the Win: One-Shot Federated Clustering, how the proposed method makes heterogeneity beneficial in data, how the heterogeneity of data is classified, and some applications of FL in an unsupervised setting. The complete show notes for this episode can be found at twimlai.com/go/504.

Episoder(779)

High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

In this episode, Hung Bui, Technology Vice President at Qualcomm, joins us to explore the latest high-efficiency techniques for running generative AI, particularly diffusion models, on-device. We dive...

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Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Today, we're joined by Alexandre Pesant, AI lead at Lovable, who joins us to discuss the evolution and practice of vibe coding. Alex shares his take on how AI is enabling a shift in software developme...

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Dataflow Computing for AI Inference with Kunle Olukotun - #751

Dataflow Computing for AI Inference with Kunle Olukotun - #751

In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss ...

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Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750

Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750

Today, we're joined by Jacob Buckman, co-founder and CEO of Manifest AI to discuss achieving long context in transformers. We discuss the bottlenecks of scaling context length and recent techniques to...

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The Decentralized Future of Private AI with Illia Polosukhin - #749

The Decentralized Future of Private AI with Illia Polosukhin - #749

In this episode, Illia Polosukhin, a co-author of the seminal "Attention Is All You Need" paper and co-founder of Near AI, joins us to discuss his vision for building private, decentralized, and user-...

30 Sep 20251h 5min

Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capab...

23 Sep 20251h 3min

Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747

Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747

Today, we're joined by Aditi Raghunathan, assistant professor at Carnegie Mellon University, to discuss the limitations of LLMs and how we can build more adaptable and creative models. We dig into her...

16 Sep 202558min

Building an Immune System for AI Generated Software with Animesh Koratana - #746

Building an Immune System for AI Generated Software with Animesh Koratana - #746

Today, we're joined by Animesh Koratana, founder and CEO of PlayerZero to discuss his team’s approach to making agentic and AI-assisted coding tools production-ready at scale. Animesh explains how rap...

9 Sep 20251h 5min

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