How Many and Which Training Points Would Need to be Removed to Flip this
Prediction?
- URL: http://arxiv.org/abs/2302.02169v1
- Date: Sat, 4 Feb 2023 13:55:12 GMT
- Title: How Many and Which Training Points Would Need to be Removed to Flip this
Prediction?
- Authors: Jinghan Yang, Sarthak Jain, Byron C. Wallace
- Abstract summary: We consider the problem of identifying a minimal subset of training data $mathcalS_t$.
If the instances comprising $mathcalS_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different.
We propose comparatively fast approximation methods to find $mathcalS_t$ based on influence functions.
- Score: 34.9118528281516
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the problem of identifying a minimal subset of training data
$\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been
removed prior to training, the categorization of a given test point $x_t$ would
have been different. Identifying such a set may be of interest for a few
reasons. First, the cardinality of $\mathcal{S}_t$ provides a measure of
robustness (if $|\mathcal{S}_t|$ is small for $x_t$, we might be less confident
in the corresponding prediction), which we show is correlated with but
complementary to predicted probabilities. Second, interrogation of
$\mathcal{S}_t$ may provide a novel mechanism for contesting a particular model
prediction: If one can make the case that the points in $\mathcal{S}_t$ are
wrongly labeled or irrelevant, this may argue for overturning the associated
prediction. Identifying $\mathcal{S}_t$ via brute-force is intractable. We
propose comparatively fast approximation methods to find $\mathcal{S}_t$ based
on influence functions, and find that -- for simple convex text classification
models -- these approaches can often successfully identify relatively small
sets of training examples which, if removed, would flip the prediction. To our
knowledge, this is the first work in to investigate the problem of identifying
a minimal training set necessary to flip a given prediction in the context of
machine learning.
Related papers
- A Theory of Interpretable Approximations [61.90216959710842]
We study the idea of approximating a target concept $c$ by a small aggregation of concepts from some base class $mathcalH$.
For any given pair of $mathcalH$ and $c$, exactly one of these cases holds: (i) $c$ cannot be approximated by $mathcalH$ with arbitrary accuracy.
We show that, in the case of interpretable approximations, even a slightly nontrivial a-priori guarantee on the complexity of approximations implies approximations with constant (distribution-free and accuracy-
arXiv Detail & Related papers (2024-06-15T06:43:45Z) - Relabeling Minimal Training Subset to Flip a Prediction [20.708004593740004]
We find that relabeling fewer than 2% of the training points can always flip a prediction.
We show that $|mathcalS_t|$ is highly related to the noise ratio in the training set and $|mathcalS_t|$ is correlated with but complementary to predicted probabilities.
arXiv Detail & Related papers (2023-05-22T08:10:43Z) - HappyMap: A Generalized Multi-calibration Method [23.086009024383024]
Multi-calibration is a powerful and evolving concept originating in the field of algorithmic fairness.
In this work, we view the term $(f(x)-y)$ as just one specific mapping, and explore the power of an enriched class of mappings.
We propose textitHappyMap, a generalization of multi-calibration, which yields a wide range of new applications.
arXiv Detail & Related papers (2023-03-08T05:05:01Z) - Statistical Learning under Heterogeneous Distribution Shift [71.8393170225794]
Ground-truth predictor is additive $mathbbE[mathbfz mid mathbfx,mathbfy] = f_star(mathbfx) +g_star(mathbfy)$.
arXiv Detail & Related papers (2023-02-27T16:34:21Z) - On Optimal Learning Under Targeted Data Poisoning [48.907813854832206]
In this work we aim to characterize the smallest achievable error $epsilon=epsilon(eta)$ by the learner in the presence of such an adversary.
Remarkably, we show that the upper bound can be attained by a deterministic learner.
arXiv Detail & Related papers (2022-10-06T06:49:48Z) - Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal
Predictions [37.92747047688873]
We show that the commonly-used $tau=1$ can be insufficient to drive good decisions in many settings of interest.
We also show that, as $tau$ grows, performing well according to $mathbfd_mathrmKLtau$ recovers universal guarantees for any possible decision.
arXiv Detail & Related papers (2021-07-20T01:55:01Z) - Agnostic learning with unknown utilities [70.14742836006042]
In many real-world problems, the utility of a decision depends on the underlying context $x$ and decision $y$.
We study this as agnostic learning with unknown utilities.
We show that estimating the utilities of only the sampled points$S$ suffices to learn a decision function which generalizes well.
arXiv Detail & Related papers (2021-04-17T08:22:04Z) - Proving Non-Inclusion of B\"uchi Automata based on Monte Carlo Sampling [19.09848789158933]
We present a specific algorithm for proving B"uchi automata non-inclusion $mathcalL(mathcalA) notsubseteq mathcalL(mathcalB)$.
$mathsfIMC2$ is a fast and reliable way to find counterexamples to B"uchi automata inclusion.
arXiv Detail & Related papers (2020-07-05T10:17:02Z) - Taking a hint: How to leverage loss predictors in contextual bandits? [63.546913998407405]
We study learning in contextual bandits with the help of loss predictors.
We show that the optimal regret is $mathcalO(minsqrtT, sqrtmathcalETfrac13)$ when $mathcalE$ is known.
arXiv Detail & Related papers (2020-03-04T07:36:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.