All talks will be held in Pacific Time.

*Schedule subject to change*

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Light Breakfast and Check-In
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Light Breakfast and Check-In
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Opening Remarks
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KEYNOTE: A (Con)Sequential View of Information for Statistical Learning and Optimization

Tara Javidi (UC San Diego)
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KEYNOTE: A (Con)Sequential View of Information for Statistical Learning and Optimization

Tara Javidi (UC San Diego)
Video
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Coffee Break
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Coffee Break
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Majorizing Measures, Codes, and Information

Maxim Raginsky (University of Illinois, Urbana-Champaign)
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Majorizing Measures, Codes, and Information

Maxim Raginsky (University of Illinois, Urbana-Champaign)
Video
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On the Robustness to Misspecification of α-Posteriors and Their Variational Approximations

Cynthia Rush (Columbia)
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On the Robustness to Misspecification of α-Posteriors and Their Variational Approximations

Cynthia Rush (Columbia)
Video
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Secure Distributed Matrix Multiplication

Rafael D'Oliveira (Clemson University)
Video
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Secure Distributed Matrix Multiplication

Rafael D'Oliveira (Clemson University)
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Lunch
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Lunch
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Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values

Haewon Jeong (UC Santa Barbara)
Video
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Optimal Neural Network Compressors and the Manifold Hypothesis

Aaron Wagner (Cornell University)
Video
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Coffee Break
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Coffee Break
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Reception
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Generalization bounds for Neural Network Based Decoders

Ravi Tandon (University of Arizona)
Video
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Generalization bounds for Neural Network Based Decoders

Ravi Tandon (University of Arizona)
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A loss function perspective for robust machine learning

Lalitha Sankar (Arizona State University)
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Information-theoretic Foundations of Generative Adversarial Models: Addressing Training Instabilities

Lalitha Sankar (Arizona State University)
Video
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Reception
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Light Breakfast and Check-In
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KEYNOTE: Differential Privacy & Variants

Ilya Mironov (Google Brain)
Video
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KEYNOTE: Differential Privacy & Variants

Ilya Mironov (Meta)
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Coffee Break
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Coffee Break
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Contraction of Markov kernels and differential privacy (PART II)

Shahab Asoodeh (McMaster University)
Video
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Contraction of Markov kernels and differential privacy (PART II)

Shahab Asoodeh (Ravi)
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Contraction of Markov Kernels and Differential Privacy (PART I)

Mario Diaz (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas…
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Contraction of Markov Kernels and Differential Privacy (PART I)

Mario Diaz (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas…
Video
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The Saddle-Point Accountant for Differential Privacy

Oliver Kosut (Arizona State University)
Video
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The Saddle-Point Accountant for Differential Privacy

Oliver Kosut (Arizona State University)
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Lunch
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Lunch
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Fundamental trade-offs in FL/FA + sparsity + DP + communication constaints

Peter Kairouz (Google)
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Towards “One-Shot” Privacy Auditing and Estimation

Peter Kairouz (Google)
Video
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Improving Accuracy-Privacy Tradeoff via Model Reprogramming

Pin-Yu Chen (IBM Research)
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Improving Accuracy-Privacy Tradeoff via Model Reprogramming

Pin-Yu Chen (IBM Research)
Video
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Coffee Break
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Coffee Break
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Poster Session (Click to view Presenters & Topics)
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Poster Session
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Light Breakfast and Check-In
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KEYNOTE: Variational Formulations and Distributed Convex Optimization Methods for Generative Modeling in High Dimensions

Todd Coleman (UC San Diego)
Video
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KEYNOTE: Variational Formulations and Distributed Convex Optimization Methods for Generative Modeling in High Dimensions

Todd Coleman (UC San Diego)
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Coffee Break
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Coffee Break
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Fair, Explainable, and Lawful Machine Learning for High-Stakes Applications

Sanghamitra Dutta (University of Maryland, College Park)
Video
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Topic: Fairness, Explainability, Information Theory (Exact Title: TBD)

Sanghamitra Dutta (University of Maryland, College Park)
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Optimal Neural Network Compressors and the Manifold Hypothesis

Aaron Wagner (Cornell University)
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Information Thresholds in Structure Estimation

Anand Sarwate (Rutgers University)
Video
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Decision making with information-theoretic constraints

Matthieu Bloch (Georgia Institute of Technology)
Video
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Decision making with information-theoretic constraints

Matthieu Bloch (Georgia Institute of Technology)
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Lunch
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The Limits of Group Fairness and Predictive Multiplicity

Flavio Calmon (Harvard University)
Video
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Talk By

Flavio Calmon (Harvard University)
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Privacy and Fairness in Collaborative AI

Yahya Hussain Essa (University of Southern California)
Video
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Privacy and Fairness in Collaborative AI

Salman Avestimehr (University of Southern California)
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Coffee Break
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Coffee Break
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Poster Session (Click to view Presenters & Topics)
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Light Breakfast and Check-In
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Light Breakfast and Check-In
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KEYNOTE: Information Constrained Optimal Transport

Ayfer Ozgur (Stanford University)
Video
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KEYNOTE: Robustness & Privacy and Wasserstein GANs topic (if not then Communicaiton Efficiency and Privacy in FL)

Ayfer Ozgur (Stanford University)
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Coffee Break
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Distribution estimation with user-level privacy and communication constraints

Jayadev Acharya (Cornell University)
Video
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Distribution estimation with user-level privacy and communication constraints

Jayadev Acharya (Cornell University)
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Memorization in Machine Learning

Adam Smith (Boston University)
Video
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Memorization in Machine Learning

Adam Smith (Boston University)
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Data Valuation: A Perturbation-Aware Approach

Giulia Fanti (Carnegie Mellon University)
Video
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Data Valuation: A Perturbation-Aware Approach

Giulia Fanti (Carnegie Mellon University)
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Lunch
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Lunch
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Talk By

Ahmad Beirami (Google Research)
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Differentially Quantized Gradient Methods

Victoria Kostina (California Institute of Technology)
Video
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Learning classification metrics from preference feedback

Sanmi Koyejo (Stanford University)
Video
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Learning classification metrics from preference feedback

Sanmi Koyejo (Stanford University)
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Coffee Break
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Overparameterized learning: an asymptotic toy model that shows interesting phenomena

Anant Sahai (UC Berkeley)
Video
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Overparameterized learning: an asymptotic toy model that shows interesting phenomena

Anant Sahai (UC Berkeley)
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Learning Under Data Poisoning

Amin Karbasi (Yale University)
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Information-Theoretic Methods for Fair Risk Minimization

Ahmad Beirami (Google Research)
Video
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Learning Under Data Poisoning

Amin Karbasi (Yale University)
Video