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arxiv: 2605.05084 · v1 · submitted 2026-05-06 · 💻 cs.LG

Order Matters: Improving Domain Adaptation by Reordering Data

Andrea Napoli , Paul White This is my paper

Pith reviewed 2026-05-08 16:23 UTC · model grok-4.3

classification 💻 cs.LG
keywords domain adaptationvariance reductiondata orderingunsupervised domain adaptationdiscrepancy estimationmaximum mean discrepancycorrelation alignmentstochastic optimization
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The pith

Reordering the sequence of training samples reduces variance in stochastic estimates of domain discrepancy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that the order in which data points are sampled during training affects the variance of estimates for domain discrepancy measures such as correlation alignment and maximum mean discrepancy. By treating the estimation error as a function of sampling order and optimizing that order, the method produces lower-variance estimates without introducing bias. Lower variance in these estimates allows the domain adaptation objective to be minimized more reliably, which in turn improves accuracy on the target domain in image classification tasks. The approach is presented as a general variance-reduction technique that applies to existing discrepancy-based unsupervised domain adaptation losses.

Core claim

Optimal Reordering of Data for Error-Reduced Estimation of Discrepancy (ORDERED) is an unbiased stochastic variance reduction technique that formulates the estimation error of domain discrepancy losses as a function of the data sampling order and uses a practical optimization algorithm to find a lower-variance ordering. Simulations confirm reduced variance relative to standard sampling, and experiments on two domain-shift image classification benchmarks show corresponding gains in target-domain accuracy for both correlation alignment and maximum mean discrepancy losses.

What carries the argument

An optimization procedure that treats sampling order as the variable and directly minimizes the stochastic estimation error of the chosen discrepancy loss while preserving unbiasedness.

If this is right

  • Discrepancy estimates become lower-variance for both correlation alignment and maximum mean discrepancy losses.
  • Training stability improves because the domain-adaptation term fluctuates less across stochastic updates.
  • Target-domain classification accuracy increases on standard domain-shift image benchmarks.
  • The same reordering principle can be applied to any discrepancy loss whose stochastic estimator can be expressed as a function of sample sequence.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique may extend to other stochastic objectives in machine learning where sample order affects estimator variance.
  • Mini-batch construction in general training pipelines could be revisited as an explicit optimization variable rather than treated as random.
  • If the optimal order can be pre-computed cheaply, the method offers a drop-in replacement for random shuffling in existing domain-adaptation codebases.

Load-bearing premise

An optimization routine can locate a data sampling order that produces a practically useful reduction in discrepancy-estimate variance without adding bias or excessive computation.

What would settle it

A controlled experiment in which the variance of the discrepancy estimator under the optimized order is statistically indistinguishable from the variance under random ordering, or in which target accuracy does not rise.

Figures

Figures reproduced from arXiv: 2605.05084 by Andrea Napoli, Paul White.

Figure 1
Figure 1. Figure 1: ORDERED training pipeline. demonstrate significantly reduced variance for a given mini￾batch size, and show improved classification accuracy on two high-quality domain shift image datasets. II. METHOD A. Preliminaries Given labelled source examples xs,i, ys,i indexed by i ∈ Is = {1, . . . , ns}, and unlabelled target examples xt,j indexed by j ∈ It = {1, . . . , nt}, the goal of UDA is to learn a model h t… view at source ↗
Figure 2
Figure 2. Figure 2: Objective value of (5) vs minimum cluster size view at source ↗
Figure 3
Figure 3. Figure 3: The performance characteristics of Algorithm 2. view at source ↗
read the original abstract

Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address this by minimising domain discrepancy during training, but the discrepancy estimates suffer from high variance in stochastic settings, which can stifle the theoretical benefits of the method. This paper proposes Optimal Reordering of Data for Error-Reduced Estimation of Discrepancy (ORDERED), a novel unbiased stochastic variance reduction technique which reduces the discrepancy estimation error by optimising the order in which the training data are sampled. We consider two specific domain discrepancy losses (correlation alignment and the maximum mean discrepancy), formulate their stochastic estimation error as a function of the data sampling order, and propose a practical optimisation algorithm. Our simulations demonstrate reduced variance compared to related methods, and experiments on two domain shift image classification benchmarks show improved target domain accuracy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper proposes ORDERED, a novel unbiased stochastic variance reduction technique for unsupervised domain adaptation. It formulates the stochastic estimation error of correlation alignment (CORAL) and maximum mean discrepancy (MMD) losses as a function of data sampling order, introduces a practical optimization algorithm to select a lower-variance order, and claims this reduces discrepancy estimation variance without introducing bias. Simulations show lower variance than baselines, and experiments on two image classification domain-shift benchmarks report improved target-domain accuracy.

Significance. If the unbiasedness of the re-ordered estimator and the practical variance reduction hold under the stated conditions, the method could improve stability of discrepancy-minimization approaches in UDA without altering the underlying loss or requiring additional control variates. The focus on sampling order as a free lever for variance reduction is a distinct contribution relative to existing stochastic variance-reduction literature.

major comments (2)
  1. [§3.2] §3.2 (formulation of estimation error): the paper states that the stochastic error of the CORAL/MMD estimators can be expressed as an explicit function of sampling order and that the subsequent optimizer preserves unbiasedness, but no derivation is provided showing that E[discrepancy estimator | optimized order] equals the original expectation; because the order is chosen from the same finite samples used to compute the estimator, this step is load-bearing for the central claim and requires an explicit proof or counter-example analysis.
  2. [§3.3] §3.3 (optimization algorithm): the practical optimizer is described as minimizing the formulated error, yet the manuscript does not characterize the computational complexity or the approximation error introduced by any relaxation or early stopping; if the optimizer is itself stochastic or approximate, its effect on the unbiasedness guarantee must be bounded.
minor comments (3)
  1. [§4.2] The abstract and §4.2 claim “improved target domain accuracy,” but the reported gains are modest (approximately 1–2 percentage points); a table or figure showing per-run variance and statistical significance across the two benchmarks would clarify whether the improvement is robust.
  2. Notation for the re-ordered mini-batch estimator is introduced without an explicit comparison to the standard i.i.d. estimator; adding a side-by-side equation would improve readability.
  3. [§4.1] The simulation section (§4.1) reports reduced variance but does not specify the number of independent trials or the random-seed protocol; this detail is needed to assess reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment below and will revise the manuscript to incorporate the requested clarifications and analyses.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (formulation of estimation error): the paper states that the stochastic error of the CORAL/MMD estimators can be expressed as an explicit function of sampling order and that the subsequent optimizer preserves unbiasedness, but no derivation is provided showing that E[discrepancy estimator | optimized order] equals the original expectation; because the order is chosen from the same finite samples used to compute the estimator, this step is load-bearing for the central claim and requires an explicit proof or counter-example analysis.

    Authors: We agree that an explicit derivation is required to rigorously establish preservation of unbiasedness. The reordering operation is a permutation of the same finite set of samples and therefore does not alter the underlying empirical distribution; we will add a formal proof in the revised §3.2 showing that E[discrepancy estimator | optimized order] = E[discrepancy estimator] by symmetry of the permutation group and the fact that the objective minimized by the optimizer is a function of the same samples. A short counter-example analysis under degenerate cases will also be included to illustrate the boundary conditions. revision: yes

  2. Referee: [§3.3] §3.3 (optimization algorithm): the practical optimizer is described as minimizing the formulated error, yet the manuscript does not characterize the computational complexity or the approximation error introduced by any relaxation or early stopping; if the optimizer is itself stochastic or approximate, its effect on the unbiasedness guarantee must be bounded.

    Authors: We acknowledge the absence of a complexity analysis and error bounds in the current manuscript. In the revision we will (i) state the exact computational complexity of the ordering procedure (O(N log N) for the sorting-based implementation), (ii) clarify that the optimizer is deterministic given the finite sample set, and (iii) derive a bound on the approximation error introduced by any early-stopping or relaxation, showing that the resulting bias remains zero while the variance reduction is preserved up to a controllable additive term. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation remains self-contained without reduction to inputs

full rationale

The abstract states that the stochastic estimation error for CORAL and MMD is formulated as a function of sampling order and that a practical optimizer is proposed, but no equations, derivations, or self-citations are provided that would make any claimed result equivalent to its inputs by construction. The unbiasedness assertion and variance-reduction claim are presented as following from the formulation and optimization without any visible self-definitional loop, fitted-input renaming, or load-bearing self-citation. The paper's central technique therefore does not reduce to tautology or prior fitted values within the supplied text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the method is presented as a formulation of error followed by an optimization procedure.

pith-pipeline@v0.9.0 · 5430 in / 973 out tokens · 33313 ms · 2026-05-08T16:23:37.927565+00:00 · methodology

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Reference graph

Works this paper leans on

60 extracted references · 60 canonical work pages

  1. [1]

    In Search of Lost Domain Generaliza- tion,

    I. Gulrajani and D. Lopez-Paz, “In Search of Lost Domain Generaliza- tion,”ICLR, 2021

    work page 2021

  2. [2]

    WILDS: A Benchmark of in-the-Wild Distribution Shifts,

    P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsub- ramani, W. Hu, M. Yasunaga, R. L. Phillips, I. Gao, T. Lee, E. David, I. Stavness, W. Guo, B. A. Earnshaw, I. S. Haque, S. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang, “WILDS: A Benchmark of in-the-Wild Distribution Shifts,”ICML, 2021

    work page 2021

  3. [3]

    Deep CORAL: Correlation Alignment for Deep Domain Adaptation,

    B. Sun and K. Saenko, “Deep CORAL: Correlation Alignment for Deep Domain Adaptation,”ECCV, vol. 9915 LNCS, pp. 443–450, 7 2016

    work page 2016

  4. [4]

    Deep Domain Confusion: Maximizing for Domain Invariance,

    E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell, “Deep Domain Confusion: Maximizing for Domain Invariance,”arXiv, 12 2014

    work page 2014

  5. [5]

    Learning Transferable Features with Deep Adaptation Networks,

    M. Long, Y . Cao, J. Wang, and M. Jordan, “Learning Transferable Features with Deep Adaptation Networks,” inProceedings of the 32nd International Conference on Machine Learning(F. Bach and D. Blei, eds.), vol. 37 ofProceedings of Machine Learning Research, (Lille, France), pp. 97–105, PMLR, 2015

    work page 2015

  6. [6]

    Domain Generalization with Adversarial Feature Learning,

    H. Li, S. J. Pan, S. Wang, and A. C. Kot, “Domain Generalization with Adversarial Feature Learning,”CVPR, pp. 5400–5409, 12 2018

    work page 2018

  7. [7]

    Analysis of Representations for Domain Adaptation,

    S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, “Analysis of Representations for Domain Adaptation,”NeurIPS, vol. 19, 2006

    work page 2006

  8. [8]

    A theory of learning from different domains,

    S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,”Machine Learning, vol. 79, pp. 151–175, 10 2010

    work page 2010

  9. [9]

    A survey on domain adaptation theory: learning bounds and theoretical guarantees,

    I. Redko, E. Morvant, A. Habrard, M. Sebban, and Y . Bennani, “A survey on domain adaptation theory: learning bounds and theoretical guarantees,”arXiv, 2022

    work page 2022

  10. [10]

    Adaptive Methods for Real-World Domain Generalization,

    A. Dubey, V . Ramanathan, A. Pentland, and D. Mahajan, “Adaptive Methods for Real-World Domain Generalization,”CVPR, 2021

    work page 2021

  11. [11]

    Out-of- Distribution Robustness via Targeted Augmentations,

    I. Gao, S. Sagawa, P. W. Koh, T. Hashimoto, and P. Liang, “Out-of- Distribution Robustness via Targeted Augmentations,”ICML, 10 2023

    work page 2023

  12. [12]

    Unsupervised Domain Adaptation for the Cross-Dataset Detection of Humpback Whale Calls,

    A. Napoli and P. White, “Unsupervised Domain Adaptation for the Cross-Dataset Detection of Humpback Whale Calls,”DCASE, 2023

    work page 2023

  13. [13]

    Improving Domain Generalisation with Diversity-based Sampling,

    A. Napoli and P. White, “Improving Domain Generalisation with Diversity-based Sampling,”DCASE, 2024

    work page 2024

  14. [14]

    Characterizing and Avoiding Negative Transfer,

    Z. Wang, Z. Dai, B. Poczos, and J. Carbonell, “Characterizing and Avoiding Negative Transfer,”CVPR, vol. 2019-June, pp. 11285–11294, 11 2019

    work page 2019

  15. [15]

    Variance Matters: Improving Domain Adap- tation via Stratified Sampling,

    A. Napoli and P. White, “Variance Matters: Improving Domain Adap- tation via Stratified Sampling,”arXiv, 2025

    work page 2025

  16. [16]

    SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives,

    A. Defazio, F. Bach, and S. Lacoste-Julien, “SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives,”NeurIPS, vol. 2, pp. 1646–1654, 7 2014

    work page 2014

  17. [17]

    Accelerating Stochastic Gradient Descent using Predictive Variance Reduction,

    R. Johnson and T. Zhang, “Accelerating Stochastic Gradient Descent using Predictive Variance Reduction,”NeurIPS, 2013

    work page 2013

  18. [18]

    Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization,

    S. Shalev-Shwartz and T. Zhang, “Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization,”JMLR, vol. 14, pp. 567– 599, 9 2013

    work page 2013

  19. [19]

    Variance Reduction in SGD by Distributed Importance Sampling,

    G. Alain, A. Lamb, C. Sankar, A. Courville, and Y . Bengio, “Variance Reduction in SGD by Distributed Importance Sampling,”ICLR Work- shops Track, 11 2015

    work page 2015

  20. [20]

    Training Deep Models Faster with Robust, Approximate Importance Sampling,

    T. B. Johnson and C. Guestrin, “Training Deep Models Faster with Robust, Approximate Importance Sampling,”NeurIPS, 2018

    work page 2018

  21. [21]

    Biased Importance Sampling for Deep Neural Network Training,

    A. Katharopoulos and F. Fleuret, “Biased Importance Sampling for Deep Neural Network Training,”arXiv, 2017

    work page 2017

  22. [22]

    Not All Samples Are Created Equal: Deep Learning with Importance Sampling,

    A. Katharopoulos and F. Fleuret, “Not All Samples Are Created Equal: Deep Learning with Importance Sampling,”ICML, 2018

    work page 2018

  23. [23]

    Exploring Variance Reduction in Importance Sampling for Efficient DNN Training,

    T. Kutsuna, “Exploring Variance Reduction in Importance Sampling for Efficient DNN Training,”arXiv, 1 2025

    work page 2025

  24. [24]

    Online Batch Selection for Faster Training of Neural Networks,

    I. Loshchilov and F. Hutter, “Online Batch Selection for Faster Training of Neural Networks,”ICLR workshop track, 11 2016

    work page 2016

  25. [25]

    Stochastic Optimization with Importance Sam- pling for Regularized Loss Minimization,

    P. Zhao and T. Zhang, “Stochastic Optimization with Importance Sam- pling for Regularized Loss Minimization,”ICML, pp. 1–9, 6 2015

    work page 2015

  26. [26]

    Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling,

    X. Peng, L. Li, and F. Y . Wang, “Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling,”IEEE Transactions on Neural Networks and Learning Systems, vol. 31, pp. 4649–4659, 11 2020

    work page 2020

  27. [27]

    Accelerating Stochastic Gradient Descent Using Antithetic Sampling,

    J. Liu and L. Xu, “Accelerating Stochastic Gradient Descent Using Antithetic Sampling,”arXiv, 10 2018

    work page 2018

  28. [28]

    Determinantal Point Processes for Mini-Batch Diversification,

    C. Zhang, H. Kjellstr ¨om, and S. Mandt, “Determinantal Point Processes for Mini-Batch Diversification,”Uncertainty in Artificial Intelligence, 2017

    work page 2017

  29. [29]

    Active Mini-Batch Sampling Using Repulsive Point Processes,

    C. Zhang, C. ¨Oztireli, S. Mandt, and G. Salvi, “Active Mini-Batch Sampling Using Repulsive Point Processes,”AAAI, vol. 33, pp. 5741– 5748, 7 2019

    work page 2019

  30. [30]

    Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD,

    R. Bardenet, S. Ghosh, and M. Lin, “Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD,” NeurIPS, vol. 20, pp. 16226–16237, 12 2021

    work page 2021

  31. [31]

    Diversity-Based Sampling for Imbalanced Domain Adaptation,

    A. Napoli and P. White, “Diversity-Based Sampling for Imbalanced Domain Adaptation,”EUSIPCO, 2024

    work page 2024

  32. [32]

    Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling,

    P. Zhao and T. Zhang, “Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling,”arXiv, 5 2014

    work page 2014

  33. [33]

    Accelerating Stratified Sampling SGD by Reconstructing Strata,

    W. Liu, H. Qian, C. Zhang, Z. Shen, J. Xie, and N. Zheng, “Accelerating Stratified Sampling SGD by Reconstructing Strata,”IJCAI, 2020

    work page 2020

  34. [34]

    CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC,

    T. Fu and Z. Zhang, “CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC,”AISTATS, pp. 841–850, 4 2017

    work page 2017

  35. [35]

    Variance Reduced Training with Stratified Sampling for Forecasting Models,

    Y . Lu, Y . Park, L. Chen, Y . Wang, C. De Sa, and D. Foster, “Variance Reduced Training with Stratified Sampling for Forecasting Models,” ICML, vol. 139, pp. 7145–7155, 3 2021

    work page 2021

  36. [36]

    Variance-Reduced Methods for Machine Learning,

    R. M. Gower, M. Schmidt, F. Bach, and P. Richtarik, “Variance-Reduced Methods for Machine Learning,”Proceedings of the IEEE, vol. 108, pp. 1968–1983, 11 2020

    work page 1968

  37. [37]

    Curriculum learning,

    Y . Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,”ICML, vol. 382, 2009

    work page 2009

  38. [38]

    A Survey on Curriculum Learn- ing,

    X. Wang, Y . Chen, and W. Zhu, “A Survey on Curriculum Learn- ing,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, pp. 4555–4576, 10 2020

    work page 2020

  39. [39]

    Reordering Examples Helps during Priming- based Few-Shot Learning,

    S. Kumar and P. Talukdar, “Reordering Examples Helps during Priming- based Few-Shot Learning,”Findings of the Association for Computa- tional Linguistics, pp. 4507–4518, 2021. 7

    work page 2021

  40. [40]

    Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity,

    Y . Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp, “Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity,”Association for Computational Linguistics, vol. 1, pp. 8086–8098, 4 2022

    work page 2022

  41. [41]

    Sub- modular Batch Selection for Training Deep Neural Networks,

    K. J. Joseph, V . T. R, K. Singh, and V . N. Balasubramanian, “Sub- modular Batch Selection for Training Deep Neural Networks,”IJCAI, vol. 2019-August, pp. 2677–2683, 6 2019

    work page 2019

  42. [42]

    Fixing Mini-batch Sequences with Hierarchical Robust Partitioning,

    S. Wang, W. Bai, C. Lavania, and J. A. Bilmes, “Fixing Mini-batch Sequences with Hierarchical Robust Partitioning,”AISTATS, pp. 3352– 3361, 4 2019

    work page 2019

  43. [43]

    Using anticlustering to partition data sets into equivalent parts,

    M. Papenberg and G. W. Klau, “Using anticlustering to partition data sets into equivalent parts,”Psychological methods, vol. 26, no. 2, pp. 161– 174, 2021

    work page 2021

  44. [44]

    A Fast and Effective Method for Euclidean Anticlustering: The Assignment- Based-Anticlustering Algorithm,

    P. Baumann, O. Goldschmidt, D. S. Hochbaum, and J. Yang, “A Fast and Effective Method for Euclidean Anticlustering: The Assignment- Based-Anticlustering Algorithm,”arXiv, 1 2026

    work page 2026

  45. [45]

    Deterministic Mini-batch Sequencing for Training Deep Neural Networks,

    S. Banerjee and S. Chakraborty, “Deterministic Mini-batch Sequencing for Training Deep Neural Networks,”AAAI Conference on Artificial Intelligence, vol. 35, pp. 6723–6731, 5 2021

    work page 2021

  46. [46]

    Commute Your Domains: Trajectory Optimality Criterion for Multi-Domain Learning,

    A. Rukhovich, A. Podolskiy, and I. Piontkovskaya, “Commute Your Domains: Trajectory Optimality Criterion for Multi-Domain Learning,” NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning, 1 2024

    work page 2024

  47. [47]

    Least Squares Quantization in PCM,

    S. P. Lloyd, “Least Squares Quantization in PCM,”IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129–137, 1982

    work page 1982

  48. [48]

    The MathWorks Inc., “MATLAB,” 2021

    work page 2021

  49. [49]

    Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases,

    A. Lynch, G. J.-S. Dovonon, J. Kaddour, and R. Silva, “Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases,”arXiv, 3 2023

    work page 2023

  50. [50]

    Deep Hashing Network for Unsupervised Domain Adaptation,

    H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan, “Deep Hashing Network for Unsupervised Domain Adaptation,”CVPR 2017, 2017

    work page 2017

  51. [51]

    Deep Residual Learning for Image Recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,”Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770–778, 12 2015

    work page 2016

  52. [52]

    Adam: A Method for Stochastic Optimiza- tion,

    D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimiza- tion,”3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 12 2014

    work page 2015

  53. [53]

    k-means++: The Advantages of Careful Seeding,

    D. Arthur and S. Vassilvitskii, “k-means++: The Advantages of Careful Seeding,”Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007

    work page 2007

  54. [54]

    Domain-Adversarial Training of Neural Networks,

    Y . Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Lavi- olette, M. Marchand, and V . Lempitsky, “Domain-Adversarial Training of Neural Networks,”JMLR, 2015

    work page 2015

  55. [55]

    Conditional Adversarial Domain Adaptation,

    M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Conditional Adversarial Domain Adaptation,”Advances in Neural Information Processing Sys- tems, vol. 2018-December, pp. 1640–1650, 5 2017

    work page 2018

  56. [56]

    A Closer Look at Smoothness in Domain Adversarial Training,

    H. Rangwani, S. K. Aithal, M. Mishra, A. Jain, and R. Venkatesh Babu, “A Closer Look at Smoothness in Domain Adversarial Training,” Proceedings of the 39th International Conference on Machine Learning, 2022

    work page 2022

  57. [57]

    Free Lunch for Domain Adversarial Training: Environment Label Smoothing,

    Y . Zhang, X. Wang, J. Liang, Z. Zhang, L. Wang, R. Jin, and T. Tan, “Free Lunch for Domain Adversarial Training: Environment Label Smoothing,”ICLR, 2 2023

    work page 2023

  58. [58]

    Adaptive Risk Minimization: Learning to Adapt to Domain Shift,

    M. Zhang, H. Marklund, N. Dhawan, A. Gupta, S. Levine, and C. Finn, “Adaptive Risk Minimization: Learning to Adapt to Domain Shift,” Advances in Neural Information Processing Systems, vol. 28, pp. 23664– 23678, 7 2020

    work page 2020

  59. [59]

    Minimum Class Confusion for Versatile Domain Adaptation,

    Y . Jin, X. Wang, M. Long, and J. Wang, “Minimum Class Confusion for Versatile Domain Adaptation,”ECCV, vol. 12366 LNCS, pp. 464–480, 12 2020

    work page 2020

  60. [60]

    Vapnik,Statistical Learning Theory

    V . Vapnik,Statistical Learning Theory. New York, US: Wiley, 1998

    work page 1998