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Information-Theoretic Methods for Trustworthy Machine Learning
Location
Calvin Lab Auditorium
Date
Monday, May 22
–
Thursday, May 25, 2023
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The Workshop
Schedule
Videos
All talks will be held in Pacific Time.
*Schedule subject to change*
Monday, May 22, 2023
8:30
–
8:55 a.m.
Light Breakfast and Check-In
8:30
–
9 a.m.
Light Breakfast and Check-In
8:55
–
9 a.m.
Opening Remarks
9
–
10 a.m.
KEYNOTE: A (Con)Sequential View of Information for Statistical Learning and Optimization
Tara Javidi (UC San Diego)
9
–
10 a.m.
KEYNOTE: A (Con)Sequential View of Information for Statistical Learning and Optimization
Tara Javidi (UC San Diego)
10
–
10:30 a.m.
Coffee Break
10
–
10:30 a.m.
Coffee Break
10:30
–
11:05 a.m.
Majorizing Measures, Codes, and Information
Maxim Raginsky (University of Illinois, Urbana-Champaign)
10:30
–
11:05 a.m.
Majorizing Measures, Codes, and Information
Maxim Raginsky (University of Illinois, Urbana-Champaign)
11:05
–
11:40 a.m.
On the Robustness to Misspecification of α-Posteriors and Their Variational Approximations
Cynthia Rush (Columbia)
11:05
–
11:40 a.m.
On the Robustness to Misspecification of α-Posteriors and Their Variational Approximations
Cynthia Rush (Columbia)
11:40 a.m.
–
12:15 p.m.
Secure Distributed Matrix Multiplication
Rafael D'Oliveira (Clemson University)
11:40 a.m.
–
12:15 p.m.
Secure Distributed Matrix Multiplication
Rafael D'Oliveira (Clemson University)
12:15
–
1:45 p.m.
Lunch
12:15
–
1:45 p.m.
Lunch
1:45
–
2:20 p.m.
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
Haewon Jeong (UC Santa Barbara)
2:20
–
2:55 p.m.
Optimal Neural Network Compressors and the Manifold Hypothesis
Aaron Wagner (Cornell University)
2:55
–
3:25 p.m.
Coffee Break
2:55
–
3:25 p.m.
Coffee Break
3
–
3:30 p.m.
Reception
3:25
–
4 p.m.
Generalization bounds for Neural Network Based Decoders
Ravi Tandon (University of Arizona)
3:25
–
4 p.m.
Generalization bounds for Neural Network Based Decoders
Ravi Tandon (University of Arizona)
4
–
4:35 p.m.
A loss function perspective for robust machine learning
Lalitha Sankar (Arizona State University)
4
–
4:35 p.m.
Information-theoretic Foundations of Generative Adversarial Models: Addressing Training Instabilities
Lalitha Sankar (Arizona State University)
4:40
–
5:30 p.m.
Reception
Tuesday, May 23, 2023
8:30
–
9 a.m.
Light Breakfast and Check-In
9
–
10 a.m.
KEYNOTE: Differential Privacy & Variants
Ilya Mironov (Google)
9
–
10 a.m.
KEYNOTE: Differential Privacy & Variants
Ilya Mironov (Meta)
10
–
10:30 a.m.
Coffee Break
10
–
10:30 a.m.
Coffee Break
10:30
–
11:05 a.m.
Contraction of Markov kernels and differential privacy (PART II)
Shahab Asoodeh (McMaster University)
10:30
–
11:05 a.m.
Contraction of Markov kernels and differential privacy (PART II)
Shahab Asoodeh (Ravi)
11:05
–
11:40 a.m.
Contraction of Markov Kernels and Differential Privacy (PART I)
Mario Diaz (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas…
11:05
–
11:40 a.m.
Contraction of Markov Kernels and Differential Privacy (PART I)
Mario Diaz (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas…
11:40 a.m.
–
12:15 p.m.
The Saddle-Point Accountant for Differential Privacy
Oliver Kosut (Arizona State University)
11:40 a.m.
–
12:15 p.m.
The Saddle-Point Accountant for Differential Privacy
Oliver Kosut (Arizona State University)
12:15
–
1:45 p.m.
Lunch
12:15
–
1:45 p.m.
Lunch
1:45
–
2:20 p.m.
Fundamental trade-offs in FL/FA + sparsity + DP + communication constaints
Peter Kairouz (Google)
1:45
–
2:20 p.m.
Towards “One-Shot” Privacy Auditing and Estimation
Peter Kairouz (Google)
2:20
–
2:55 p.m.
Improving Accuracy-Privacy Tradeoff via Model Reprogramming
Pin-Yu Chen (IBM Research)
2:20
–
2:55 p.m.
Improving Accuracy-Privacy Tradeoff via Model Reprogramming
Pin-Yu Chen (IBM Research)
2:55
–
3:25 p.m.
Coffee Break
2:55
–
3:25 p.m.
Coffee Break
3:25
–
5:30 p.m.
Poster Session (Click to view Presenters & Topics)
3:25
–
5:30 p.m.
Poster Session
Wednesday, May 24, 2023
8:30
–
9 a.m.
Light Breakfast and Check-In
9
–
10 a.m.
KEYNOTE: Variational Formulations and Distributed Convex Optimization Methods for Generative Modeling in High Dimensions
Todd Coleman (UC San Diego)
9
–
10 a.m.
KEYNOTE: Variational Formulations and Distributed Convex Optimization Methods for Generative Modeling in High Dimensions
Todd Coleman (UC San Diego)
10
–
10:30 a.m.
Coffee Break
10
–
10:30 a.m.
Coffee Break
10:30
–
11:05 a.m.
Fair, Explainable, and Lawful Machine Learning for High-Stakes Applications
Sanghamitra Dutta (University of Maryland, College Park)
10:30
–
11:05 a.m.
Topic: Fairness, Explainability, Information Theory (Exact Title: TBD)
Sanghamitra Dutta (University of Maryland, College Park)
11:05
–
11:40 a.m.
Optimal Neural Network Compressors and the Manifold Hypothesis
Aaron Wagner (Cornell University)
11:05
–
11:40 a.m.
Information Thresholds in Structure Estimation
Anand Sarwate (Rutgers University)
11:40 a.m.
–
12:15 p.m.
Decision making with information-theoretic constraints
Matthieu Bloch (Georgia Institute of Technology)
11:40 a.m.
–
12:15 p.m.
Decision making with information-theoretic constraints
Matthieu Bloch (Georgia Institute of Technology)
12:15
–
1:45 p.m.
Lunch
1:45
–
2:20 p.m.
The Limits of Group Fairness and Predictive Multiplicity
Flavio Calmon (Harvard University)
1:45
–
2:20 p.m.
Talk By
Flavio Calmon (Harvard University)
2:20
–
2:55 p.m.
Privacy and Fairness in Collaborative AI
Yahya Hussain Essa (University of Southern California)
2:20
–
2:55 p.m.
Privacy and Fairness in Collaborative AI
Salman Avestimehr (University of Southern California)
2:55
–
3:25 p.m.
Coffee Break
2:55
–
3:25 p.m.
Coffee Break
3:25
–
5:30 p.m.
Poster Session (Click to view Presenters & Topics)
Thursday, May 25, 2023
8:30
–
9 a.m.
Light Breakfast and Check-In
8:30
–
9 a.m.
Light Breakfast and Check-In
9
–
10 a.m.
KEYNOTE: Information Constrained Optimal Transport
Ayfer Ozgur (Stanford University)
9
–
10 a.m.
KEYNOTE: Robustness & Privacy and Wasserstein GANs topic (if not then Communicaiton Efficiency and Privacy in FL)
Ayfer Ozgur (Stanford University)
10
–
10:30 a.m.
Coffee Break
10:30
–
11:05 a.m.
Distribution estimation with user-level privacy and communication constraints
Jayadev Acharya (Cornell University)
10:30
–
11:05 a.m.
Distribution estimation with user-level privacy and communication constraints
Jayadev Acharya (Cornell University)
11:05
–
11:40 a.m.
Memorization in Machine Learning
Adam Smith (Boston University)
11:05
–
11:40 a.m.
Memorization in Machine Learning
Adam Smith (Boston University)
11:40 a.m.
–
12:15 p.m.
Data Valuation: A Perturbation-Aware Approach
Giulia Fanti (Carnegie Mellon University)
11:40 a.m.
–
12:15 p.m.
Data Valuation: A Perturbation-Aware Approach
Giulia Fanti (Carnegie Mellon University)
12:15
–
1:45 p.m.
Lunch
12:15
–
1:45 p.m.
Lunch
1:45
–
2:20 p.m.
Talk By
Ahmad Beirami (Google Research)
1:45
–
2:20 p.m.
Differentially Quantized Gradient Methods
Victoria Kostina (California Institute of Technology)
2:20
–
2:55 p.m.
Learning classification metrics from preference feedback
Sanmi Koyejo (Stanford University)
2:20
–
2:55 p.m.
Learning classification metrics from preference feedback
Sanmi Koyejo (Stanford University)
2:55
–
3:25 p.m.
Coffee Break
3:25
–
4 p.m.
Overparameterized learning: an asymptotic toy model that shows interesting phenomena
Anant Sahai (UC Berkeley)
3:25
–
4 p.m.
Overparameterized learning: an asymptotic toy model that shows interesting phenomena
Anant Sahai (UC Berkeley)
4
–
4:35 p.m.
Learning Under Data Poisoning
Amin Karbasi (Yale University)
4
–
4:35 p.m.
Information-Theoretic Methods for Fair Risk Minimization
Ahmad Beirami (Google Research)
4:40
–
5:15 p.m.
Learning Under Data Poisoning
Amin Karbasi (Yale University)
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