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(778)

LLMs for Equities Feature Forecasting at Two Sigma with Ben Wellington - #736

LLMs for Equities Feature Forecasting at Two Sigma with Ben Wellington - #736

Today, we're joined by Ben Wellington, deputy head of feature forecasting at Two Sigma. We dig into the team’s end-to-end approach to leveraging AI in equities feature forecasting, covering how they i...

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Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735

Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735

Today, we're joined by Jason Corso, co-founder of Voxel51 and professor at the University of Michigan, to explore automated labeling in computer vision. Jason introduces FiftyOne, an open-source platf...

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Grokking, Generalization Collapse, and the Dynamics of Training Deep Neural Networks with Charles Martin - #734

Today, we're joined by Charles Martin, founder of Calculation Consulting, to discuss Weight Watcher, an open-source tool for analyzing and improving Deep Neural Networks (DNNs) based on principles fro...

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Google I/O 2025 Special Edition - #733

Google I/O 2025 Special Edition - #733

Today, I’m excited to share a special crossover edition of the podcast recorded live from Google I/O 2025! In this episode, I join Shawn Wang aka Swyx from the Latent Space Podcast, to interview Logan...

28 Mai 202526min

RAG Risks: Why Retrieval-Augmented LLMs are Not Safer with Sebastian Gehrmann - #732

RAG Risks: Why Retrieval-Augmented LLMs are Not Safer with Sebastian Gehrmann - #732

Today, we're joined by Sebastian Gehrmann, head of responsible AI in the Office of the CTO at Bloomberg, to discuss AI safety in retrieval-augmented generation (RAG) systems and generative AI in high-...

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From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

Today, we're joined by Mahesh Sathiamoorthy, co-founder and CEO of Bespoke Labs, to discuss how reinforcement learning (RL) is reshaping the way we build custom agents on top of foundation models. Mah...

13 Mai 20251h 1min

How OpenAI Builds AI Agents That Think and Act with Josh Tobin - #730

How OpenAI Builds AI Agents That Think and Act with Josh Tobin - #730

Today, we're joined by Josh Tobin, member of technical staff at OpenAI, to discuss the company’s approach to building AI agents. We cover OpenAI's three agentic offerings—Deep Research for comprehensi...

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CTIBench: Evaluating LLMs in Cyber Threat Intelligence with Nidhi Rastogi - #729

CTIBench: Evaluating LLMs in Cyber Threat Intelligence with Nidhi Rastogi - #729

Today, we're joined by Nidhi Rastogi, assistant professor at Rochester Institute of Technology to discuss Cyber Threat Intelligence (CTI), focusing on her recent project CTIBench—a benchmark for evalu...

30 Apr 202556min

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