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References from Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1007/978-1-4757-3264-1
10.1007/978-1-4757-3264-1 · External reference
Unresolved reference
2003 · External reference
10.1287/mnsc.1120.1641
10.1287/mnsc.1120.1641 · External reference
10.1137/16m1058297
10.1137/16m1058297 · External reference
10.1214/20-aos2004
10.1214/20-aos2004 · External reference
10.1287/moor.2020.1085
10.1287/moor.2020.1085 · External reference
Does distributionally robust supervised learning give robust classifiers?
External reference
Adaptive sampling for stochastic risk-averse learning
External reference
Non-convex distributionally robust optimization: Non-asymptotic analysis
External reference
Minimax regret optimization for robust machine learning under distribution shift
External reference
10.18653/v1/d19-1432
10.18653/v1/d19-1432 · External reference
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
External reference
Agnostic federated learning
External reference
10.1137/070704277
10.1137/070704277 · External reference
10.52202/068431-0030
10.52202/068431-0030 · External reference
Optimal algorithms for group distributionally robust optimization and beyond
2022 · External reference
10.52202/068431-2599
10.52202/068431-2599 · External reference
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
External reference
10.1609/aaai.v33i01.33011544
10.1609/aaai.v33i01.33011544 · External reference
Minimizing the maximal loss: How and why
External reference
Learning with average top-k loss
External reference
10.52202/075280-2286
10.52202/075280-2286 · External reference
10.1057/9781137024381_3
10.1057/9781137024381_3 · External reference
10.1515/9781400831050
10.1515/9781400831050 · External reference
10.1287/opre.2015.1374
10.1287/opre.2015.1374 · External reference
Certifying some distributional robustness with principled adversarial training
External reference
Fairness without demographics in repeated loss minimization
External reference
Class-weighted classification: Trade-offs and robust approaches
External reference
10.1109/iccv48922.2021.00936
10.1109/iccv48922.2021.00936 · External reference
Coping with label shift via distributionally robust optimisation
External reference
10.1287/opre.1090.0741
10.1287/opre.1090.0741 · External reference
10.1287/educ.2019.0198
10.1287/educ.2019.0198 · External reference
10.1287/opre.2023.0294
10.1287/opre.2023.0294 · External reference
10.1007/s10107-017-1174-z
10.1007/s10107-017-1174-z · External reference
Beyond minimax rates in group distributionally robust optimization via a novel notion of sparsity
External reference
On distributionally robust optimization and data rebalancing
External reference
Efficient stochastic approximation of minimax excess risk optimization
External reference
Efficient algorithms for empirical group distributionally robust optimization and beyond
External reference
Collaborative PAC learning
External reference
Improved algorithms for collaborative PAC learning
External reference
Multi-group agnostic PAC learnability
External reference
Unsupervised domain adaptation by backpropagation
External reference
10.1109/tnnls.2018.2890018
10.1109/tnnls.2018.2890018 · External reference
10.1007/s11263-021-01442-2
10.1007/s11263-021-01442-2 · External reference
10.1017/9781108231596
10.1017/9781108231596 · External reference
10.1006/jcss.1997.1504
10.1006/jcss.1997.1504 · External reference
Using anytime algorithms in intelligent systems
1996 · External reference
Anytime online-to-batch, optimism and acceleration
External reference
10.1006/game.1999.0738
10.1006/game.1999.0738 · External reference
Fast convergence of regularized learning in games
External reference
Efficient online-bandit strategies for minimax learning problems
2021 · External reference
10.1137/s0097539701398375
10.1137/s0097539701398375 · External reference
10.1017/9781108571401
10.1017/9781108571401 · External reference
Efficient learning by implicit exploration in bandit problems with side observations
External reference
A modern introduction to online learning
2019 · External reference
10.52202/068431-0030
10.52202/068431-0030 · External reference
10.1145/3605209
10.1145/3605209 · External reference
10.1287/moor.2013.0598
10.1287/moor.2013.0598 · External reference
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
2016 · External reference
10.1109/tsp.2019.2928952
10.1109/tsp.2019.2928952 · External reference
10.1145/1147954.1147956
10.1145/1147954.1147956 · External reference
10.1007/978-3-642-16108-7_30
10.1007/978-3-642-16108-7_30 · External reference
Adult
1996 · External reference
MNIST handwritten digit database
2010 · External reference
Variance-based regularization with convex objectives
External reference
10.1007/s10107-017-1172-1
10.1007/s10107-017-1172-1 · External reference
Stochastic gradient methods for distributionally robust optimization with $f$f-divergences
External reference
Large-scale methods for distributionally robust optimization
External reference
An online method for a class of distributionally robust optimization with non-convex objectives
External reference
10.1080/10556788.2021.1895152
10.1080/10556788.2021.1895152 · External reference
10.1038/s41551-023-01056-8
10.1038/s41551-023-01056-8 · External reference
Predict then interpolate: A simple algorithm to learn stable classifiers
External reference
10.1561/2200000024 · Admitted local publication
Online convex optimization in the bandit setting: Gradient descent without a gradient
External reference
10.1017/cbo9780511546921
10.1017/cbo9780511546921 · External reference
