Award Abstract # 2023166
TRIPODS: Institute for Foundations of Data Science
NSF Org: |
DMS
Division Of Mathematical Sciences
|
Recipient: |
UNIVERSITY OF WASHINGTON
|
Initial Amendment Date: |
August 31, 2020 |
Latest Amendment Date: |
August 28, 2023 |
Award Number: |
2023166 |
Award Instrument: |
Continuing Grant |
Program Manager: |
Christopher Stark
cstark@nsf.gov
(703)292-4869
DMS
Division Of Mathematical Sciences
MPS
Direct For Mathematical & Physical Scien
|
Start Date: |
September 1, 2020 |
End Date: |
August 31, 2025 (Estimated) |
Total Intended Award Amount: |
$4,852,999.00 |
Total Awarded Amount to Date: |
$3,920,286.00 |
Funds Obligated to Date: |
FY 2020 = $1,125,915.00
FY 2021 = $1,058,612.00
FY 2022 = $825,788.00
FY 2023 = $909,971.00
|
History of Investigator: |
-
Maryam
Fazel
(Principal Investigator)
mfazel@uw.edu
-
Zaid
Harchaoui
(Co-Principal Investigator)
-
Dmitriy
Drusvyatskiy
(Co-Principal Investigator)
-
Yin Tat
Lee
(Co-Principal Investigator)
-
Kevin
Jamieson
(Co-Principal Investigator)
|
Recipient Sponsored Research Office: |
University of Washington
4333 BROOKLYN AVE NE
SEATTLE
WA
US
98195-1016
(206)543-4043
|
Sponsor Congressional District: |
07
|
Primary Place of Performance: |
University of Washington
4333 Brooklyn Ave. NE
Seattle
WA
US
98195-2500
|
Primary Place of Performance Congressional District: |
07
|
Unique Entity Identifier (UEI): |
HD1WMN6945W6
|
Parent UEI: |
|
NSF Program(s): |
TRIPODS Transdisciplinary Rese, Algorithmic Foundations
|
Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
|
Program Reference Code(s): |
048Z,
075Z,
079Z
|
Program Element Code(s): |
041Y00,
779600
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing Number(s): |
47.049, 47.070
|
ABSTRACT
Data science is making an enormous impact on science and society, but its success is uncovering pressing new challenges that stand in the way of further progress. Outcomes and decisions arising from many machine learning processes are not robust to errors and corruption in the data; data science algorithms are yielding biased and unfair outcomes, as concerns about data privacy continue to mount; and machine learning systems suited to dynamic, interactive environments are less well developed than corresponding tools for static problems. Only by an appeal to the foundations of data science can we understand and address challenges such as these. Building on the work of three TRIPODS Phase I institutes, the new Institute for Foundations of Data Science (IFDS) brings together researchers from the Universities of Washington, Wisconsin-Madison, California-Santa Cruz, and Chicago, organized around the goal of tackling these critical issues. Members of IFDS have complementary strengths in the TRIPODS disciplines of mathematics, statistics, and theoretical computer science, and a proven record of collaborating to push theoretical boundaries by synthesizing knowledge and experience from diverse areas. Students and postdoctoral members of IFDS will be trained to be fluent in the languages of several disciplines, and able to bridge these communities and perform transdisciplinary research in the foundations of data science. In concert with its research agenda, IFDS will engage the data science community through workshops, summer schools, and hackathons. Its diverse leadership, committed to equity and inclusion, proposes extensive plans for outreach to traditionally underrepresented groups. Governance, management, and evaluation of the institute will build on the successful and efficient models developed during Phase I.
To address critical issues at the cutting edge of data science research, IFDS will organize its research around four core themes. The complexity theme will synthesize various notions of complexity from multiple disciplines to make breakthroughs in the analysis of optimization and sampling methods, develop tools for assessing the complexity of data models, and seek new methods with better complexity properties, to make complexity a more powerful tool for understanding and inventing algorithms in data science. The robustness theme considers data that contains errors or outliers, possibly due to an adversary, and will design methods for data analysis and prediction that are robust in the face of these errors. The theme on closed-loop data science tackles the issues of acquiring data in ways that reveal the information content of the data efficiently, using strategic and sequential policies that leverage information gathered already from past data. The theme on ethics and algorithms addresses issues of fairness and bias in machine learning, data privacy, and causality and interpretability. The four themes intersect in many ways, and most IFDS researchers will work in two or more of them. By making concerted progress on these fundamental fronts, IFDS will lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas. Additionally, IFDS will organize and host activities that engage the data science community at all levels of seniority. Annual workshops will focus on the critical issues identified above and others that are sure to arise over the next five years. Comprehensive plans for outreach and education will draw on previous experience of the Phase I institutes and leverage institutional resources at the four sites. Collaborations with domain science researchers in academia, national laboratories, and industry, so important in illuminating issues in the fundamentals of data science, will continue through the many channels available to IFDS members, including those established in the TRIPODS+X program. Relationships with other institutes at each IFDS site will further extend the impact of IFDS on domain sciences and applications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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(Showing: 1 - 10 of 41)
(Showing: 1 - 41 of 41)
Sadeghi, Omid and Fazel, Maryam
"Fast First-Order Methods for Monotone Strongly DR-Submodular Maximization"
Proceedings of SIAM Conference on Applied and Computational Discrete Algorithms
, 2023
Citation Details
Dong, Sally and Lee, Yin Tat and Ye, Guanghao
"A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path"
53rd Annual ACM SIGACT Symposium on Theory of Computing
, 2021
https://doi.org/10.1145/3406325.3451056
Citation Details
Katz-Samuels, Julian and Zhang, Jifan and Jain, Lalit and Jamieson, Kevin
"Improved Algorithms for Agnostic Pool-based Active Classification"
Proceedings of Machine Learning Research
, v.139
, 2021
Citation Details
Joshua Cutler and Dmitriy Drusvyatskiy and Zaid Harchaoui
"Stochastic Optimization under Distributional Drift"
Journal of Machine Learning Research
, v.24
, 2023
Citation Details
Lee, Yin Tat and Shen, Ruoqi and Tian, Kevin
"Structured Logconcave Sampling with a Restricted Gaussian Oracle"
Proceedings of Machine Learning Research
, 2021
Citation Details
Maiti, Arnab and Jamieson, Kevin and Ratliff, Lillian J.
"Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games"
Proceedings of Machine Learning Research
, v.206
, 2023
Citation Details
Sally Dong and Yu Gao and Gramoz Goranci and Yin Tat Lee and Richard Peng and Sushant Sachdeva and Guanghao Ye
"Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time"
Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)
, 2022
Citation Details
Yin Tat Lee and Ruoqi Shen and Kevin Tian
"Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions"
Conference on Neural Information Processing Systems
, 2021
Citation Details
Sivakanth Gopi and Yin Tat Lee and Lukas Wutschitz
"Numerical Composition of Differential Privacy"
Conference on Neural Information Processing Systems
, 2021
Citation Details
Sadeghi, Omid and Raut, Prasanna and Fazel, Maryam
"A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints"
Advances in neural information processing systems
, v.33
, 2020
Citation Details
Davis, Damek and Drusvyatskiy, Dmitriy
"Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions"
Foundations of Computational Mathematics
, 2021
https://doi.org/10.1007/s10208-021-09516-w
Citation Details
Daniilidis, Aris and Drusvyatskiy, Dmitriy
"The slope robustly determines convex functions"
Proceedings of the American Mathematical Society
, 2023
https://doi.org/10.1090/proc/16503
Citation Details
Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and hoi, Yejin and Harchaoui, Zaid
"MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers"
Advances in neural information processing systems
, 2022
Citation Details
Maiti, Arnab and Jamieson, Kevin and Ratliff, Lillian J.
"Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback"
Proceedings of Machine Learning Research
, v.151
, 2022
Citation Details
Cui, Qiwen and Xiong, Zhihan and Fazel, Maryam and Du, Simon S.
"Learning in Congestion Games with Bandit Feedback"
Advances in neural information processing systems
, 2022
Citation Details
Jiang, Haozhe and Cui, Qiwen and Xiong, Zhihan and Fazel, Maryam and Du, Simon S.
"Offline Congestion Games: How Feedback Type Affects Data Coverage Requirement"
International Conference on Learning Representations
, 2023
Citation Details
van den Brand, Jan and Gao, Yu and Jambulapati, Arun and Lee, Yin Tat and Liu, Yang P. and Peng, Richard and Sidford, Aaron
"Faster maxflow via improved dynamic spectral vertex sparsifiers"
54th ACM Symposium on Theory of Computing
, 2022
https://doi.org/10.1145/3519935.3520068
Citation Details
van den Brand, Jan and Lee, Yin Tat and Liu, Yang P. and Saranurak, Thatchaphol and Sidford, Aaron and Song, Zhao and Wang, Di
"Minimum cost flows, MDPs, and l1-regression in nearly linear time for dense instances"
53rd Annual ACM SIGACT Symposium on Theory of Computing
, 2021
https://doi.org/10.1145/3406325.3451108
Citation Details
Janardhan Kulkarni and Yin Tat Lee and Daogao Liu
"Private Non-smooth ERM and SCO in Subquadratic Steps"
Conference on Neural Information Processing Systems
, 2021
Citation Details
Davis, Damek and Drusvyatskiy, Dmitriy
"Graphical Convergence of Subgradients in Nonconvex Optimization and Learning"
Mathematics of Operations Research
, v.47
, 2022
https://doi.org/10.1287/moor.2021.1126
Citation Details
Sadeghi, Omid and Fazel, Maryam
"Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints"
Proceedings of Machine Learning Research
, v.130
, 2021
Citation Details
Camilleri, Romain and Jamieson, Kevin and Katz-Samuels, Julian
"High-Dimensional Experimental Design and Kernel Bandits"
Proceedings of Machine Learning Research
, v.139
, 2021
Citation Details
Jiang, Haotian and Kathuria, Tarun and Lee, Yin Tat and Padmanabhan, Swati and Song, Zhao
"A Faster Interior Point Method for Semidefinite Programming"
61st IEEE Annual Symposium on Foundations of Computer Science
, 2020
https://doi.org/10.1109/FOCS46700.2020.00089
Citation Details
Joshua Cutler, Dmitriy Drusvyatskiy
"Stochastic Optimization under Distributional Drift"
Journal of machine learning research
, v.24
, 2023
Citation Details
Zhiqi Bu and Sivakanth Gopi and Janardhan Kulkarni and Yin Tat Lee and Judy Hanwen Shen and Uthaipon Tantipongpipat
"Fast and Memory Efficient Differentially Private-SGD via JL Projections"
Conference on Neural Information Processing Systems
, 2021
Citation Details
Ray, Mitas and Ratliff, Lillian J. and Drusvyatskiy, Dmitriy and Fazel, Maryam
"Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments"
Proceedings of the AAAI Conference on Artificial Intelligence
, v.36
, 2022
https://doi.org/10.1609/aaai.v36i7.20780
Citation Details
Sun, Yue and Narang, Adhyyan and Gulluk, Ibrahim and Oymak, Samet and Fazel, Maryam
"Towards Sample-efficient Overparameterized Meta-learning"
Advances in neural information processing systems
, v.34
, 2021
Citation Details
Irons, Nicholas J. and Scetbon, Meyer and Pal, Soumik and Harchaoui, Zaid
"Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates"
Proceedings of Machine Learning Research
, v.151
, 2022
Citation Details
Davis, D and Drusvyatskiy, D and Xiao, L and Zhang, J.
"From Low Probability to High Confidence in Stochastic Convex Optimization"
Journal of machine learning research
, v.22
, 2021
Citation Details
Wagenmaker, Andrew and Simchowitz, Max and Jamieson, Kevin
"Task-Optimal Exploration in Linear Dynamical Systems"
Proceedings of Machine Learning Research
, v.139
, 2021
Citation Details
Charisopoulos, Vasileios and Chen, Yudong and Davis, Damek and Díaz, Mateo and Ding, Lijun and Drusvyatskiy, Dmitriy
"Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence"
Foundations of Computational Mathematics
, 2020
https://doi.org/10.1007/s10208-020-09490-9
Citation Details
Sun, Yue and Fazel, Maryam
"Learning Optimal Controllers by Policy Gradient: Global Optimality via Convex Parameterization"
60th IEEE Conference on Decision and Control (CDC),
, 2021
https://doi.org/10.1109/CDC45484.2021.9682821
Citation Details
van den Brand, Jan and Lee, Yin-Tat and Nanongkai, Danupon and Peng, Richard and Saranurak, Thatchaphol and Sidford, Aaron and Song, Zhao and Wang, Di
"Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs"
61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 16-19, 2020
, 2020
https://doi.org/10.1109/FOCS46700.2020.00090
Citation Details
Charisopoulos, Vasileios and Davis, Damek and Díaz, Mateo and Drusvyatskiy, Dmitriy
"Composite optimization for robust rank one bilinear sensing"
Information and Inference: A Journal of the IMA
, 2020
https://doi.org/10.1093/imaiai/iaaa027
Citation Details
Narang, A. and Faulkner, E. and Drusvyatskiy, D. and Fazel, M. and Ratliff, L.
"Learning in Stochastic Monotone Games with Decision-Dependent Data"
The 25th International Conference on Artificial Intelligence and Statistics
, v.151
, 2022
Citation Details
Wang, Yiping and Chen, Yifang and Du, Simon and Jamieson, Kevin
"Improved Active Multi-Task Representation Learning via Lasso"
Proceedings of Machine Learning Research
, v.202
, 2023
Citation Details
Ren, Zhaolin and Zheng, Yang and Fazel, Maryam and Li, Na
"On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds"
Proceedings of Machine Learning Research
, v.211
, 2023
Citation Details
Xiong, Zhihan and Shen, Ruoqi and Cui, Qiwen and Fazel, Maryam and Du, Simon S.
"Near-Optimal Randomized Exploration for Tabular Markov Decision Processes"
Advances in neural information processing systems
, v.35
, 2022
Citation Details
Qin, Yuzhen and Li, Yingcong and Pasqualetti, Fabio and Fazel, Maryam and Oymak, Samet
"Stochastic Contextual Bandits with Long Horizon Rewards"
Proceedings of the AAAI Conference on Artificial Intelligence
, v.37
, 2023
https://doi.org/10.1609/aaai.v37i8.26140
Citation Details
Lee, Yin Tat and Shen, Ruoqi and Tian, Kevin
"Structured Logconcave Sampling with a Restricted Gaussian Oracle"
Conference on Learning Theory
, 2021
Citation Details
(Showing: 1 - 10 of 41)
(Showing: 1 - 41 of 41)
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