All talks are listed in Pacific Daylight Time. Schedule subject to change.

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Privacy and Complex Learning Models
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Coffee and Check-In
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Opening Remarks
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Privacy and Complex Learning Models
Abhradeep Thakurta (Google), Thomas Steinke (Google)
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Break
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DP-FTRL in Practice
Om Thakkar (Google)
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Label Differential Privacy
Ilya Mironov (Facebook AI)
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Lunch
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Protocols
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Differentially Private Fine-tuning of Language Models
Jana Kulkarni (Microsoft Research)
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Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking Of Users
Moni Naor (Weizmann Institute of Science)
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Break
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Secret-Shareable Compression of Local Randomizers
Kunal Talwar (Apple)
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Hiding Among The Clones: A Simple And Nearly Optimal Analysis Of Privacy Amplification By Shuffling
Vitaly Feldman (Apple)
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Reception
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High-dimensional Estimation with Robustness and Privacy
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Coffee and Check-In
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Differential Privacy in High-Dimensional Statistics
Jonathan Ullman (Northeastern University)
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Break
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Efficient Mean Estimation with Pure Differential Privacy via a Sum-Of-Squares Exponential Mechanism
Gautam Kamath (University of Waterloo)
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Differential Privacy over Riemannian Manifolds
Matthew Reimherr (Penn State University)
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Lunch
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Non-Euclidean Differentially Private Stochastic Convex Optimization
Cristóbal Guzmán (University of Twente)
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Differential Privacy for Black Boxes
Paul Laskowski (UC Berkeley)
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Break
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Multiclass PAC Learning under Differential Privacy
Mark Bun (Boston University)
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Private Statistics at Small Scales
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Coffee and Check-In
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What Tools Are We Missing For Inference From Differentially Private Outputs
Adam Smith (Boston University)
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Quantifying Uncertainty with Privacy
Audra McMillan (Apple)
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Break
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Differentially Private Heavy-Tailed Synthetic Data
Seša Slavković (Pennsylvania State University)
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Mean Estimation with User-Level Privacy under Data Heterogeneity
Rachel Cummings (Georgia Institute of Technology)
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Lunch
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Differentially Private Sampling from Distributions
Sofya Raskhodnikova (Boston University)
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Attacks on De-ID's Defenses
Aloni Cohen (Boston University)
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Break
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A Discussion of Data Co-Ops
Katrina Ligett (Hebrew University of Jerusalem), Kobbi Nissim (Georgetown University)
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Hike! (Starting from Calvin Lab - weather pending)
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Implementations and the Census
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Coffee and Check-In
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Negotiating Privacy and Fitness for Use in Recent DP Deployments at Federal Statistical Agencies
Ashwin Machanavajjhala (Duke University)
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Designing an Open-Source Platform for Differentially Private Analytics That Is Usable, Scalable, and Extensible
Michael Hay (Colgate University)
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Break
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Protecting Privacy in Practice
Frauke Kreuter (UMD and LMU Munich)
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Disparate Impact of Uncertainty (and Privacy) on Formula Funding
Steven Wu (Carnegie Mellon University)
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Lunch
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TBD
Ryan Rogers (LinkedIn)
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Quantum Technologies: Hype and Reality
Chris Hoofnagle (UC Berkeley)
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Break
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Rump Session (short talks, to be decided online during the workshop)
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Fairness
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Coffee and Check-In
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Loss Minimization and m=Multi-group Fairness
Parikshit Gopalan (VMWare)
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Break
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Universal Adaptability
Michael Kim (UC Berkeley)
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Scaffolding Sets
Zhun Deng (Harvard University)
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Schrödinger’s Cactus: Differential Private Mechanisms in the Large-Composition Regime
Flavio Calmon (Harvard University)
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Lunch
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Discussion Group