transfer learning

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See also notes on causal inference for some close connections.

domain adaptation algorithms

Domain test bed available here, for generalizating to new domains (i.e. performing well on domains that differ from previous seen data)

  • Empirical Risk Minimization (ERM, Vapnik, 1998) - standard training
  • Invariant Risk Minimization (IRM, Arjovsky et al., 2019) - learns a feature representation such that the optimal linear classifier on top of that representation matches across domains.
  • Group Distributionally Robust Optimization (GroupDRO, Sagawa et al., 2020) - ERM + increase importance of domains with larger errors (see also papers from Sugiyama group e.g. 1, 2)
    • Variance Risk Extrapolation (VREx, Krueger et al., 2020) - encourages robustness over affine combinations of training risks, by encouraging strict equality between training risks
  • Interdomain Mixup (Mixup, Yan et al., 2020) - ERM on linear interpolations of examples from random pairs of domains + their labels
  • Marginal Transfer Learning (MTL, Blanchard et al., 2011-2020) - augment original feature space with feature vector marginal distributions and then treat as a supervised learning problem
  • Meta Learning Domain Generalization (MLDG, Li et al., 2017) - use MAML to meta-learn how to generalize across domains
  • learning more diverse predictors
    • Representation Self-Challenging (RSC, Huang et al., 2020) - adds dropout-like regularization to important features, forcing model to depend on many features
    • Spectral Decoupling (SD, Pezeshki et al., 2020) - regularization which forces model to learn more predictive features, even when only a few suffice
  • embedding prior knowledge
    • Style Agnostic Networks (SagNet, Nam et al., 2020) - penalize style features (assumed to be spurious)
    • Penalizing explanations (Rieger et al. 2020) - penalize spurious features using prior knowledge
  • Domain adaptation under structural causal models (chen & buhlmann, 2020)
    • make clearer assumptions for domain adaptation to work
    • introduce CIRM, which works better when both covariates and labels are perturbed in target data
  • kernel approach (blanchard, lee & scott, 2011) - find an appropriate RKHS and optimize a regularized empirical risk over the space

domain invariance

key idea: want repr. to be invariant to domain label

dynamic selection

Dynamic Selection (DS) refers to techniques in which, for a new test point, pre-trained classifiers are selected/combined from a pool at test time review paper (cruz et al. 2018), python package

  1. define region of competence
    1. clustering
    2. kNN - more refined than clustering
    3. decision space - e.g. a model’s classification boundary, internal splits in a model
    4. potential function - weight all the points (e.g. by their distance to the query point)
  2. criteria for selection
    1. individual scores: acc, prob. behavior, rank, meta-learning, complexity
    2. group: data handling, ambiguity, diversity
  3. combination
    1. non-trainable: mean, majority vote, product, median, etc.
    2. trainable: learn the combination of models
      1. related: in mixture of experts models + combination are trained jointly
    3. dynamic weighting: combine using local competence of base classifiers
    4. Oracle baseline - selects classifier predicts correct label, if such a classifier exists

test-time adaptation

  • test-time adaptation
  • combining train-time and test-time adaptation
    • Adaptive Risk Minimization (ARM, Zhang et al., 2020) - combines groups at training time + batches at test-time
      • meta-train the model using simulated distribution shifts, which is enabled by the training groups, such that it exhibits strong post-adaptation performance on each shift

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