Title: Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
Venue: NeurIPS 2024
Accepted: yes
Keywords: Model performance degradation

autonomous adaptation

large language models
Abstract: Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their *reason-agnostic* nature. By choosing from a pre-defined set of actions, such methods implicitly assume that the causes of model degradation are irrelevant to what actions should be taken, limiting their ability to select appropriate adaptations. In this paper, we propose an alternative paradigm to overcome these limitations, called *self-healing machine learning* (SHML). Contrary to previous approaches, SHML autonomously diagnoses the reason for degradation and proposes diagnosis-based corrective actions. We formalize SHML as an optimization problem over a space of adaptation actions to minimize the expected risk under the shifted DGP.  We introduce a theoretical framework for self-healing systems and build an agentic self-healing solution *$\mathcal{H}$-LLM* which uses large language models to perform self-diagnosis by reasoning about the structure underlying the DGP, and self-adaptation by proposing and evaluating corrective actions. Empirically, we analyze different components of *$\mathcal{H}$-LLM* to understand *why* and *when* it works, demonstrating the potential of self-healing ML.

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments

Abstract

Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. By choosing from a pre-defined set of actions, such methods implicitly assume that the causes of model degradation are irrelevant to what actions should be taken, limiting their ability to select appropriate adaptations. In this paper, we propose an alternative paradigm to overcome these limitations, called self-healing machine learning (SHML). Contrary to previous approaches, SHML autonomously diagnoses the reason for degradation and proposes diagnosis-based corrective actions. We formalize SHML as an optimization problem over a space of adaptation actions to minimize the expected risk under the shifted DGP. We introduce a theoretical framework for self-healing systems and build an agentic self-healing solution H -LLM which uses large language models to perform self-diagnosis by reasoning about the structure underlying the DGP, and self-adaptation by proposing and evaluating corrective actions. Empirically, we analyze different components of H -LLM to understand why and when it works, demonstrating the potential of self-healing ML.

1 Introduction

Consider the following scenario: You are tasked with monitoring the performance of a black-box model f deployed in production. After some time, you notice that the predictive performance of f has started to degrade. What would be the appropriate action a you should take to ensure that the model's performance returns to its prior performance levels: a 1 : re-train the model on a subset of the data; a 2 : change the type of the model used; a 3 : remove discovered corrupted values; a 4 : add new covariates?

Accuracy, %

Time

Stable performance

Drop in

Performance

Possible adaptation

strategies

Environment

change

Future

Past

1 4 formance after an environment change. Clearly, the answer to this question is 'it depends'. Different actions might result in different behavior of the model over time, as illustrated in Fig. 1. If we could pinpoint why the performance of the model has degraded, it could help us understand what actions are most promising, since we could select an action which would directly address the root cause of the problem.

While we take this intuition for granted, state-of-the-art techniques for handling model degradation do not reflect this line of reasoning and rely on pre-determined actions , such as model retraining [1-5], re-using old models [1, 6-8], or other specialized methods [6, 7, 9-12]. Such approaches share a common, implicit assumption -the reason for the degradation in model performance is irrelevant. We refer to this as reason-agnostic methods .

The practical

implications of

methods being reason-agnostic

are quite concerning. By not con- sidering the causes for drop in

performance, the corrective ac- tions

are, essentially,

shots in

the dark. In high-stakes applica- tions like healthcare, finance, or

policing, misguided adaptations can

lead to

real-world harms,

such as inaccurate diagnoses, fi- nancial

losses, or

system ures.

fail-

In some industries-such as healthcare- this has resulted

in avoiding automated model retraining altogether [13].

We propose self-healing machine learning (SHML) to overcome the limitations of reason-agnostic approaches. SHML equips ML models with the ability to diagnose the reasons for performance degradation and take targeted corrective actions. We define a self-healing system as a tuple ⟨H , f ⟩ , where f is a black-box model and H is a healing mechanism that can modulate the behavior of f . An example of H modulating f is by deciding what data to use to re-train f , as illustrated in our introductory example. H contains four components : monitoring, diagnosis, adaptation , and testing (Fig. 2). The goal of H is to decide what actions to take in response to model degradation which are chosen based on an adaptation policy that provides a mapping from diagnoses to actions. We therefore formalize the goal of H as finding optimal actions under the shifted data generating process (DGP) which are sampled from an adaptation policy conditioned on a diagnosis (Sec. 3.3). In our introductory example, the optimal action is taking action a 1 (Fig. 1). Building upon these insights, we propose the first self-healing ML algorithm, H -LLM (Sec. 5) which generates diagnoses behind model degradation and suggests diagnosis-based adaptation strategies.

Significance beyond technical contributions . By enabling systems to autonomously diagnose and adapt to model degradation, we lay the groundwork for a new class of self-healing algorithms. We envision self-healing systems as crucial for high-stakes applications where optimal model performance is essential. We also believe this work has immediate practical relevance in high-stakes areas where model degradation is common, such as medicine [13-18], fraud detection [19] or finance [20].

/light_bulb

Contributions . 1 © We identify fundamental limitations in existing reason-agnostic adaptation approaches that do not consider the reason for model degradation (Sec. 3.2). 2 © We introduce the paradigm of self-healing machine learning and establish a theoretical foundation for finding adaptation actions with diagnosis-guided action sampling (Sec. 3.3 -4). 3 © We propose the first self-healing ML algorithm H -LLM which reasons about the causes of degradation and modulates the behavior of ML models (Sec. 5). 4 © We demonstrate the viability of SHML by studying why and when it works (Sec. 6).

2 Related work

SHML is most closely related to concept drift adaptation or specialized drift handling methods. We provide an extended discussion on related work within each component in Appendix A.

Concept drift adaptation. The field of concept drift adaptation focuses on developing algorithms to maintain the performance of machine learning models in changing environments. Such algorithms are predominantly proposed within the setting of tabular data. Most common adaptation techniques are re-training models on new data [1-5, 21-24], re-using stored models [1, 6-9] or obtaining new data altogether [25, 26]. These approaches can be implicit, like continuous retraining, or explicit, based on drift detection in data or model error [5, 23, 27]. Because these approaches do not explicitly

Has the

performance of

the model

decreased?

If yes,

then

ML Model

What are the

reasons for the

decrease in

performance?

III.

Adaptation

What adaptation

strategies can

help heal the

model?

I.

Monitoring

IV.

Testing

Which of the

strategies are the

most effective?

II.

Diagnosis

Self-Healing Components

incorporate the reason for model degradation, we refer to them as reason agnostic . SHML diverges from common approaches by introducing the core idea of diagnosing the root cause to search for optimal adaptation actions.

Specialized drift handling. Techniques have also been developed to adapt in the presence of various drift scenarios, such as sliding windows [10] or adaptive classifiers [11, 28-32] which 'repair' concept drift [27]. However, these methods lack an explicit diagnosis mechanism and operate under fixed decision rules, which do not incorporate the root causes of degradation into the adaptation strategy. Similarly, works that aim to understand distribution shifts [33-35] or attribute shifts to specific variables through causal mechanisms [36, 37] provide valuable insights but do not offer a comprehensive framework for adaptation. We note that some work could be in principle a part of some SHML system components (discussed in Appendix A).

3 Self-healing Machine Learning

This section introduces self-healing machine learning and its four components. We present the problem setting (Sec. 3.1), explain current limitations (Sec. 3.2), and outline the four stages of SHML (Sec. 3.3). NOTE : Table 1 serves as a guide for navigating the paper.

Table 1: A summary table of self-healing machine learning and its four stages, providing links to relevant sections and serving as a navigation guide for the paper.

3.1 Model degradation over time

Preliminaries . Let X and Y denote the input and output spaces, respectively, and let P t denote the data distribution over X × Y at time step t ∈ [ T ] . At each t , we observe a batch of data D t = {( x ( i ) t , y ( i ) t )} n t i = 1 ∼ P n t t , where n t = 1 in the streaming setting and n t > 1 in the batch setting. We will drop the superscripts where clear from context.

The goal is to learn a sequence of functions { f t ∈ F} T t = 1 that minimize the cumulative risk:

where F is a function class and ℓ ∶ Y × Y → R ≥ 0 is a loss function.

In the time-invariant setting where P t = P ∀ t ∈ [ T ] , the goal reduces to learning a single function f ∗ ∈ F that minimizes the risk R ( f ) = E P [ ℓ ( f ( x ) , y )] . When P is unknown, f ∗ is often approximated by minimizing the empirical risk on a training set {( x ( i ) , y ( i ) )} n i = 1 ∼ P n . However, when P t evolves over time, the optimal predictor f ∗ t ∈ arg min f ∈F E P t [ ℓ ( f ( x ) , y )] changes across time steps 1 . Failing to adapt f t in this time-varying setting leads to model degradation, as the learned function becomes increasingly suboptimal w.r.t. the current data distribution.

3.2 Limitations of existing approaches in adapting to changing environments

Maintaining stable model performance in the presence of a changing environment poses unique challenges. As the optimal predictor f ∗ t evolves over time, the estimated predictor should also adapt. Ideally, we could obtain a large batch of data D t + 1 = {( x ( i ) t + 1 , y ( i ) t + 1 )} n t + 1 i = 1 and minimize the empirical risk over this dataset. However, this is often impractical due to constraints such as (i) ground-truth labels not being immediately available [38]; (ii) the streaming setting, where each new batch contains only one data point [39]; (iii) gradual shifts, where past data remains relevant [40]; or (iv) the presence of corrupted data in new batches [41].

To address this, the research community has developed specialized methods determining the appropriate corrective actions in such drifts. As discussed in Sec. 2, these methods primarily execute pre-defined actions upon detecting a change, such as model retraining [1-5], re-using old models [1, 6-8], or other more specialized methods [6, 7, 9-12]. However, such methods are reason-agnostic , disregarding valuable information that inform better adaptation actions. Consider an illustrative example: suppose a batch of new data arrives, but due to a sensor malfunction [42], 80% of the labels become corrupted and are independent of the input for that batch only. Naively retraining the model on this noisy batch would degrade its performance. This is because this strategy implicitly assumes:

where f t + 1 is a model trained on D t + 1 . Relying on this assumption results in worse performance than doing nothing. Similarly, re-using old models assumes that the past data distribution is still relevant:

for some k > 0 . Similarly, this might result in suboptimal performance due to the nature of the shift. Each adaptation method discussed in Sec. 2 has such implicit assumptions about the model or DGP.

By not taking into account the reason for the model degradation (such as corrupted data), the adaptation strategy is defaulting to suboptimal corrective actions . While any adaptation strategy inherently involves some assumptions about the relationship between the predictor and the data, we would like to prioritize making informed assumptions . As we discuss in Sec. 3.3, a key source of such information is diagnosing why the model's performance has dropped.

To address the reason-agnostic nature of such adaptation methods, we propose a paradigm shift called self-healing machine learning (SHML), where deployed models autonomously diagnose the reason for degradation and take diagnosis-guided corrective actions.

/light_bulb

Takeaway . Existing adaptation methods make implicit, pre-defined assumptions about the nature of model degradation. Neglecting its reason can lead to poorly chosen actions.

3.3 The four stages of self-healing machine learning

Self-healing machine learning is a framework for autonomously detecting, diagnosing, and correcting performance degradation in deployed ML models. It aims to maintain model performance in changing environments without constant human intervention. The motto of self-healing ML is 'understanding your problem is half the solution' (and the most important half). A SHML system is defined by a tuple ⟨H , f ⟩ , where f ∶ X → Y is the deployed machine learning model we aim to heal (i.e., the function that makes predictions on input data), and H is a healing mechanism that interacts with the environment and acts upon the model f by proposing and implementing actions, such as selecting when to retrain a model, what data to use or how to change the input data before making predictions. Thus, H can modulate the behavior of the deployed model f .

Self-Healing Machine Learning in a nutshell.

Self-healing ML contains four components: monitoring, diagnosis, adaptation, and testing. After these steps, the best action is implemented on the ML model, illustrated in Fig. 2.

I. Monitoring . The first step is the detection of degradation, potentially due to a shift in the data distribution. We formalize this as a monitoring component H M that takes as input the sequence of data batches {D i } t i = 1 , up to time t , and outputs s t ∈ [ 0 , 1 ] , indicating the likelihood of model degradation. Formally,

where higher values of s t indicate a greater likelihood of a shift.

II. Diagnosis . The diagnosis component H D detects the reason of degradation. It takes data batches {D i } t i = 1 , up to time t , along with any available contextual information c ∈ C (e.g. background knowledge), and outputs a distribution ζ ∈ ∆ (Z) over a space of possible reasons Z :

Z represents the finite space of possible reasons of the shift and ζ is a stochastic vector.

III. Adaptation . The adaptation component is a policy π that outputs a distribution over actions. Given a diagnosis vector ζ , actions a ∈ A are selected from a finite space A by:

t

Each action a modifies f . We denote the model used at time t , selected by action a , as f a . IV. Testing . The testing component H T evaluates each action a ∈ A on a relevant distribution and outputs a performance measure:

Objective . The goal of self-healing ML is to select the optimal action a ∗ that minimizes the expected loss E [ ℓ ( f a t ( x ) , y )] on the data distribution P t :

where ℓ ∶ Y × Y → R ≥ 0 is a loss function, and f a t denotes the model selected by action a to be used at time t . The action a is selected according to the adaptation policy π (⋅∣ ζ ) , which maps the diagnosis vector ζ (a distribution over possible reasons for degradation) to a distribution over actions.

Suppose the following motivating example to guide the notation above.

Illustrative example . Consider a deployed ML model f t for predicting diabetes. The monitoring component H M detects a significant drop in performance, with s t = 0 . 95 (Eq. 4). The diagnosis component H D outputs three most likely reasons z 1 , z 2 , z 3 ∈ Z , with ζ ( z 1 ) = 0 . 95 for data quality issues, ζ ( z 2 ) = 0 . 03 for concept drift, and ζ ( z 3 ) = 0 . 02 for model overfitting (Eq. 5). Based on, ζ , the adaptation policy π samples two actions a 1 , a 2 ∼ π (⋅∣ ζ ) (Eq. 6): a 1 : remove detected biologically implausible values (e.g. Age > 200) and retrain f t ; a 2 : include interaction terms between features to capture non-linearities. The testing component H T evaluates the adapted models f a 1 t and f a 2 t on new incoming data (Eq. 7) and selects a 1 due to lower estimated loss.

The primary insight of SHML is that the action a ∼ π (⋅∣ ζ ) should be based on the diagnosis ζ , which is a distribution over possible reasons z ∈ Z for model degradation. In contrast, standard approaches (Sec. 3.2) assume that π ⊥ ⊥ ζ . SHML formalizes this as an optimization problem over a space of adaptation actions-we aim to find the optimal actions to take each time the model f degrades, with these actions chosen by the policy π of the self-healing system H (Fig. 3). Different policies π 1 and π 2 might propose different actions in response to the same performance drop. While the diagnosis ζ informs the policy, we

Time

do not assume it is necessarily useful. Since ζ ∈ ∆ (Z) is a probability distribution, it can encode no knowledge by being uniform over the diagnosis space: ζ ( z ) = 1 ∣Z∣ , ∀ z ∈ Z . These components and their interactions between two time points t and t + 1 are shown in Fig. 3.

The effectiveness of the adaptation actions depends on the diagnosis, i.e. how well can we identify the root cause. Therefore, we turn to the diagnosis component next.

/light_bulb

Takeaway . SHML is a framework which selects actions based on the reason for model degradation. It contains four stages: monitoring, diagnosis, adaptation, and testing.

4 An analysis of the properties of self-healing diagnosis

Self-healing systems have the unique property of having a diagnosis stage. But what constitutes a good diagnosis? In this section, we analyze the properties of self-healing diagnosis and establish its connection to the performance of adaptation actions.

To effectively use diagnosis information to guide the search for adaptation actions, we require a way to quantify the usefulness of a diagnosis. We propose three desirable properties for such a measure: (i) concentration : it should favor diagnoses that provide more information, i.e. assign higher probabilities to fewer possible reasons; (ii) sensitivity : it should be sensitive to changes in the diagnosis distribution, such that small changes in probabilities would result in small changes in the measure; (iii) maximum uncertainty : it should reach its maximum value when the diagnosis

distribution is uniform, indicating no knowledge about the reason for degradation. Therefore, we propose using the entropy of the diagnosis vector as a useful proxy for quality which satisfies all three properties. Because entropy measures uncertainty, we refer to this as the certainty of the diagnosis .

Definition 1 (Certainty of the Diagnosis) . Let Z be the finite space of possible reasons for degradation and ∆ (Z) be the diagnosis space. The certainty of a diagnosis ζ ∈ ∆ (Z) in a self-healing machine learning system is measured by its entropy H ( ζ ) , defined as:

where ζ ( z ) is the probability of reason z under the distribution ζ .

The link between diagnosis quality and adaptation performance highlights the importance of obtaining informative diagnoses in SHML. Building upon these concepts, we define the optimal diagnosis:

Definition 2 (Optimal Diagnosis) . The optimal diagnosis ζ ∗ is defined as:

where ∆ (Z) is the diagnosis space, π (⋅∣ ζ ) is the conditional distribution over actions induced by diagnosis vector ζ , and R ( a ) denotes the risk of f a t associated with action a ∈ A .

This formalizes the intuition that the best diagnosis is the one that leads to the best adaptation actions, on average. To characterize the properties of this optimal diagnosis, we introduce an assumption about the structure of the adaptation policy:

Assumption 1 (Independent actions) . We assume that π (⋅∣ ζ ) has a hierarchical structure. First, a reason z ∈ Z is sampled according to the diagnosis ζ : z ∼ ζ . Then, an action is sampled conditioned on this reason, a ∼ π (⋅∣ z † ) , where z † ∈ ∆ (Z) such that z † ( z ) = 1 . π (⋅∣ ζ ) can then be described as the following mixture.

ζ are the mixture weights and { π (⋅∣ z † ) ∣ z ∈ Z} are the mixture components.

Under this hierarchical structure, we can prove a useful property of the optimal diagnosis:

Proposition 1. Under Assumption 1, the optimal diagnosis ζ ∗ has a zero entropy, i.e., H ( ζ ∗ ) = 0 .

Proof in Appendix E. To ensure that the optimal diagnosis is well-defined, we also prove its existence under mild assumptions:

Proposition 2 (Existence of Optimal Diagnosis) . Suppose that the action space A is a compact subspace of R n and R is continuous. Then there exists at least one optimal diagnosis ζ ∗ .

Proof. The expected risk E a ∼ π (⋅∣ ζ ) [ R ( a )] is a continuous function of ζ by the continuity of R and the compactness of A . Since the diagnosis space ∆ (Z) is also compact (being a probability simplex), the extreme value theorem guarantees the existence of a minimizer ζ ∗ .

/light_bulb

Takeaway . The existence of an optimal diagnosis establishes a foundation for designing algorithms that can accurately approximate it in practice. By identifying the underlying reasons for performance degradation, a high-quality diagnosis enables a self-healing system to take the most effective adaptation actions.

5 Building self-healing systems: H -LLM

This section outlines the challenges of building SHML systems (Sec. 5.1), describes how LLMs can address these challenges (Sec. 5.2), and introduces the first self-healing system, H -LLM (Sec. 5.3).

5.1 Unique challenges of building self-healing systems

Implementing SHML systems (Sec. 3.3) poses unique challenges in diagnosis and adaptation.

Challenges in diagnosis . Discovering the reasons for model degradation poses significant practical challenges because: (i) the space of possible reasons Z is often poorly defined or intractable to

Table 2: Example diagnoses suggested by H -LLM. The system proposes diagnoses and suggests evidence for the diagnosis. A post-hoc relative confidence score, constructed using the 'evidence' column, helps to guide which diagnoses to pay most attention to while designing adaptation policies.

specify exhaustively in real-world scenarios; and (ii) assigning well-calibrated probabilities to reasons for model degradation is difficult due to both the epistemic and aleatoric uncertainty that exists in real-world environments. This makes it difficult to approximate the optimal diagnosis (Def. 2).

Challenges in adaptation . The adaptation policy π (Eq. 6) requires selecting optimal adaptation actions a based on the diagnosis ζ . This is challenging because (i) it requires reasoning about how actions interact with diagnoses; and (ii) the space of adaptation actions may be extremely large in practice, making it difficult to find the optimal action (Eq. 8).

5.2 Language models to empower self-healing

We posit that LLMs have the potential to satisfy many of the required properties of self-healing components because of the following capabilities: (i) Hypothesis proposers . LLMs are known to be 'phenomenal hypotheses proposers' [43] which are required to hypothesizing diagnoses of ML model performance degradation; (ii) Contextual understanding . LLMs have been pretrained with a vast corpus of information and hence have extensive prior knowledge around different contexts and settings [44, 45]; (iii) Language model agents . Language models can work as agents within a larger system [46, 47] which is required to actively interact with a deployed model, trigger and implement changes. We therefore see LLMs as capable proxies for different self-healing components.

5.3 Design of H -LLM

We instantiate the healing mechanism H with an LLM l , using its useful properties (Sec. 5.2) to address the practical challenges of designing SHML systems (Sec. 5.1).

H -LLM in a nutshell.

H -LLM is the first SHML algorithm that modulates the behavior of f following Fig. 3.

I. Monitoring . We use statistical drift detection algorithms to monitor model degradation from k previous time points [29, 39, 48]. Diagnosis is triggered if a shift is detected.

II. Diagnosis . Upon detection, we use a pre-defined prompt template to obtain information about the dataset before and after the diagnosis. The prompt template gives us numerical insights into how the dataset has changed and includes covariate information before and after the shift, together with other numerical details. We denote this prompt as an extractor function E ∶ D ∗ → D c to obtain an information vector v . Using v and a chain-of-thought (CoT) module with self-reflection, H -LLM generates k candidate reasons for degradation { z i } k i = 1 ∼ l (⋅∣ v ) via Monte Carlo (MC) sampling with associated confidence scores. As before, this is obtained by following pre-defined prompt templates conditioned on the obtained information (e.g. ' Suggest {self.n} possible reasons why the model might have failed on the basis of the issues presented ').

These candidates form an empirical diagnosis vector ˆ ζ , approximating the optimal diagnosis ζ ∗ . Table 2 illustrates diagnoses generated by H -LLM.

III. Adaptation . Conditioned on the empirical diagnosis distribution ˆ ζ , H -LLM generates m candidate adaptation actions { a j } j m = 1 ∼ l (⋅∣ ˆ ζ ) via CoT-based MC sampling. This approximates sampling from π (⋅∣ ζ ∗ ) (Def. 6). The actions sampled from l are textual representations, so we use an interpreter function to execute each a on f .

IV. Testing . The sampled actions are evaluated on an empirical dataset (Def. 7), and the empirically optimal action ˆ a ∗ = arg min j ∈[ m ] R ( a ) is implemented. Limited access to the shifted DGP complicates evaluating R ( a ) , but it can be approximated with empirical data ˆ D test by using a backtesting window , continuously incoming data , or historical data (Appendix B.4).

Goal . This procedure aims to approximate the optimal action (Def. 8). These actions are orchestrated by an orchestrator component in H -LLM which can navigate between these steps.

Appendix B provides an extended discussion of H -LLM, including the algorithm, prompts, examples, and outputs. The following table links H -LLM with the theoretical framework.

6 Experimental viability studies

The previous sections constituted the primary contribution of our paper-establishing SHML as a framework. The goal of this section is to provide a viability study by analyzing different components of SHML. We conduct six viability studies. 2

Experimental setup . We desire to meet two properties: (i) have full control of the DGP to vary experimental parameters; and (ii) we need to benchmark against existing adaptation methods (Sec. 2) which are predominantly tabular-based. Therefore, we simulate a diabetes prediction task [4951] based on the setup in Sec. 3.1. We predict diabetes Y t ∈ { 0 , 1 } at each time point t for a set of n observations, generated according to a (changing) pre-specified DGP log ( P ( Y t = 1 ∣ X t ) P ( Y t = 0 ∣ X t ) ) = α t + ∑ k ∈ K β t,k X t,k + ϵ t , where K includes relevant parameters such as Age or BMI, β t,k are timevarying covariates and ϵ t ∼ N( 0 , σ 2 ) is a noise component. For evaluating H -LLM actions, we use a backtesting window -a representative sample of the shifted distribution obtained after detecting the change but before deploying the adapted model (Sec. B.4). Details provided in Appendix C.

6.1 Viability study I: Adaptation in the presence of model degradation

Setup . We aim to empirically demonstrate the limitations of existing approaches in adapting to changing environments (Sec. 3.2). We benchmark H -LLM against four common drift adaptation methods: (i) new model retraining on post-drift data, (ii) partially updating models with new data; (iii) Ensemble methods by re-using old models, and (iv) No retraining of the models [1]. At time t , we introduce a sudden, single intervention by changing the DGP parameters and corrupting a percentage τ of k columns. Table 4 shows the performance of different methods across τ and k .

Discussion . The performance of f degrades if the corrupted columns are not handled appropriately, such as removing or inputting the corrupted data. Defaulting to standard techniques of adapting to a changed environment results in poor performance. H -LLM diagnoses issues by observing that some values have drifted too much from their original values and the DGP has changed. One of the proposed adaptation strategies is to remove samples which were estimated to be corrupted, and re-training the model on the remainder of the data. This results in superior performance.

Takeaway 1 . Diagnosing the root cause of degradation can guide better adaptation actions.

6.2 Viability study II: Adaptation across datasets

Setup . We aim to empirically analyze whether SHML can provide benefits across different datasets. We cover five different datasets: Airlines [52], Poker [53], Weather [54], Electricity [55], Forest Type

[56]. We simulate real-world unexpected degradations by assuming lagged labels and corrupting features at test time and evaluating models for different datasets (Table 5).

Discussion . Ground-truth labels are often not immediately available [38], a core feature of many streaming settings. We evaluate how H -LLM compares to existing approaches in such scenarios. Across five datasets with different characteristics, H -LLM consistently outperforms traditional adaptation methods by adapting f a t at each time point t . Therefore, SHML's ability to identify and decorrupt features provides a robust adaptation strategy across varied data distributions and schemas.

6.3 Viability study III: Monitoring

Setup . We use the same setup as experiment I and vary the drift detection threshold which influences the sensitivity of a detection system to changes in the DGP. Low values mean high sensitivity, and high values mean low sensitivity [57]. We measure average recovery time for H -LLM to return recover from degradation and post-intervention accuracy , H -LLMs average performance after intervention. Fig. 4 shows this relationship.

Discussion . Intuitively, one might expect earlier drift detection (lower threshold) to consistently yield faster recovery and higher accuracy. In reality, concept drift algorithms often struggle with false positives which can result in worse model performance because of unnecessary re-training [5, 58]. Self-healing ML exhibits greater robustness to these false positives, as any action will be implemented only if it outperforms doing nothing. This contrasts with traditional systems which would automatically trigger the selected action. In Fig. 4, this represents the higher post-intervention accuracy with smaller thresholds.

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Takeaway 3 . SHML has greater robustness to implementing poor adaptation actions.

6.4 Viability study IV: Diagnosis

Setup . We evaluate how well self-healing systems identify the root causes of problems. We corrupt a proportion of observations ( corruption coefficient ) by multiplying their values by a factor ( outlier factor ) and see if the H -LLM detects issues related to these factors. We output a probability distribution over diagnoses of which variable is corrupted. Knowing the true corrupted variable, we measure the difference between the distributions using KLDivergence, with lower values indicating closer matches to true corruption. A uniform diagnosis baseline represents random guessing. Fig. 5 shows these differences.

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Uninformed diagnosis

Discussion . As the outlier factor and corruption coefficient increase, making data issues more apparent, H -LLM assigns higher probabilities to the corrupted variables. Thus, the diagnosis accuracy improves as the problem becomes more evident.

Takeaway 4 . The quality of the diagnosis improves when the issues become more apparent.

6.5 Viability study V: Adaptation

Setup . We study the sensitivity of SHML adaptation actions by examining how well actions perform based on (i) the number of corrupted values and (ii) the size of the backtesting dataset. Fig. 6 shows this relationship.

Discussion . As more values are corrupted, adaptation actions become more concentrated and less effective. With a larger backtesting dataset,

2%

5% 10% 20% 50% 75%

Range of values corrupted

50%

60%

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Highest dot is the best

discovered action

Highest dot is the best

discovered action

Highest dot is the best

discovered action

Accuracy by corruption

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10 2

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Size of backtesting window

actions are more spread out. This suggests (i) action evaluation is more reliable with non-corrupted data and (ii) larger backtesting windows help in selecting better adaptations.

Takeaway 5 . A large test dataset and high-quality data can improve adaptation action selection.

6.6 Viability study VI: Testing

Setup . We study the importance of the testing component (Eq. 7) by evaluating H -LLM suggested actions with and without the testing phase (backtesting window) and comparing their accuracies. Fig. 7 shows this relationship.

Discussion . Having a dataset to evaluate actions significantly improves the self-healing process. We see that better data quality results in more reliable adaptation policies.

Takeaway 6 . The testing component is important to effectively evaluate the proposed actions.

6.7 Other studies

We provide further experiments in Appendix D. Our framework shows strong performance with lower warm-start parameters and increasing benefit as data degradation becomes more severe (Sec. D.4). A component-wise ablation analysis (Sec. D.6) reveals each stage of SHML is essential. Extended benchmarks (Sec. D.5) and model agnostic evaluations (Sec. D.7) demonstrate consistent improvements across different adaptation approaches and ML architectures.

7 Discussion

Algorithms hold significant decision-making power in high-stakes applications, yet little has been done to ensure their optimal performance. This work presents a major leap towards that goal. By enabling systems to autonomously diagnose and adapt to new environments, we aim to create a wave of self-healing systems beneficial to both the ML community and society. Our theoretical framework (Sec. 3.3, 4) builds the foundation for the development of self-healing theory, such as optimal adaptation or diagnosis methods, and our viability study shows the potential benefits of SHML. Our largest contribution is formalizing this field-we hope to spur new theoretical developments and encourage the adoption of such systems in critical domains like medicine [13-18] and finance [20].

Limitations . SHML's success relies on accurate root cause identification and finding effective adaptation policies which could pose challenges in some complex, real-world settings (Sec. 5.1). Furthermore, the prioritization of adaptation strategies is also not trivial. Currently, H -LLM primarily looks for subgroup-level issues. We see future work tackling all areas of self-healing ML: finding better diagnosis strategies, improving adaptation selection, and enabling better testing of actions in the presence of changing environments.

Broader impact . Because SHML could empower many positive technologies, it could also be misused to amplify the impact of more problematic systems, such as surveillance technologies.

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Appendix: Self-Healing Machine Learning: A framework for autonomous adaptation in real-world environments

Table of Contents

A Extended related work

In this section, we describe and contrast our work with other related areas.

A.1 Comparison to other fields

Concept drift adaptation. Concept drift adaptation algorithms, a key component of self- healing ML systems, primarily handle drifts by re-training models on new data [1-5] or older, pre-trained stored models [1, 6-8]. These approaches can be implicit, like continuous retraining, or explicit, based on drift detection in data or model error [5, 23, 27]. Drift detection methods compare distributions, analyze data sequentially, or use statistical process control [59]. For instance, the DDM algorithm [23] has in-control, warning, and out-of-control states.

Specialized drift handling. Techniques have been developed for various drift scenarios. For recurring drifts, methods store and reuse historical models [6, 7, 9]. Streaming data is handled by blind approaches, like sliding windows [10] or adaptive decision trees [11], and informed approaches with explicit drift detection [21-24]. Resampling can repair adaptation errors [27], while dynamic classifier selection finds the best model for each input [60]. Methods have been proposed for robustness to noise [12], specific drift types [61], and other issues [62, 63]. Recent work explores understanding distribution shifts through latent variable models [34] and other techniques [35]. Some adaptive methods of re-training the model also include adding more hidden layer to a learner upon detection of a drift [62, 63]. Another area of research closely linked within the field is dynamic selection which attempts to find the most suitable classifier conditional on the covariates [60].

On 'repairing concept drift' . There have been other methods propose that implicitly try to adapt by detecting changes [27]. However, these adaptations are still based on the observed empirical distributions as opposed to observing the reason for degradation. By periodically sampling the accuracy of inactive classifers, the authors identify cases where change was missed or misclassifed. However, this falls under the broader umbrella of trying out many pre-determined actions without directly reasoning about the reason for model degradation.

Continual learning . One might get the impression that self-healing machine learning might bear close resemblance to continual learning. Continual learning focuses on developing models that learn continuously from a stream of data, acquiring, retaining, and transferring knowledge across tasks over time [64]. This contrasts strongly with self-healing machine learning. Below, we outline seven criteria by which self-healing machine learning and continual learning differ .

Differences between continual learning and self-healing machine learning .

Objective . The objectives of the two fields are different. Continual learning aims to learn sequentially from a stream of tasks while mitigating catastrophic forgetting. SHML focuses on autonomously diagnosing and recovering from performance degradation within a single task due to distribution shifts.

Knowledge retention . Acore goal of continual learning is to preserve previously acquired knowledge while learning new tasks. SHML does not explicitly aim to retain prior knowledge or acquire new knowledge, but rather to maintain stable performance on the current task by adapting to the reason for degradation.

Stability-Plasticity Dilemma . Continual learning grapples with the trade-off between being plastic enough to learn new tasks and stable enough to remember old ones. In contrast, there is no such dilemma within SHML.

Task expansion . Continual learning seeks to expand the model's capabilities by increasing the number of tasks it can perform. In contrast, SHML operates on a single well-defined task-ensuring the optimal performance of a model, typically by minimizing empirical risk- and does not aim to increase the number of tasks. Instead, the focus is on ensuring optimal performance under a single task.

Adaptation mechanism . The underlying logic or mechanism of adaptation is different. Continual learning typically adapts by modifying model architecture, updating parameters via constrained optimization, using memory replay. In contrast, SHML explicitly adapts by diagnosing the root cause of performance drops and conditioning an adaptation action on the basis of that diagnosis. This explicit mechanism which is conditioned on is not a part of a continual learning system.

Shift assumptions . Continual learning primarily handles shifts across distinct tasks, where the input or output distribution changes between tasks. In contrast, SHML considers shifts within the same task, where the joint distribution might change.

Theoretical formalism . Continual learning is often formalized as a sequence of constrained optimization problems to mitigate interference between tasks. In contrast, SHML is formalized as finding an optimal policy that can propose actions on the basis of diagnoses.

A.2 Comparison on a component level

Here, we focus on some related work within each sub-component. Table 6 provides key related work within each column. We do not focus separately on adaptation and testing because adaptation is covered above, whereas testing is simply a stage which helps to evaluate proposed actions.

Monitoring . Related work within monitoring largely relates to different statistical techniques for discovering the presence of shifts/drifts or model degradation. We see them as an integral part of SHML. However, they are also actively used by other adaptation methods to trigger adaptation systems.

Diagnosis . The diagnosis component is a core component of SHML. Two primary works are closely related. The first work, 'why did the distribution change?' [36], attempts to factorize the change of the joint distribution into conditional distributions of each variable and attribute some changes to one of the marginals. This is achieved by modeling the change and relationship between variables as a causal mechanism. The second work, 'why did the model fail?' [37] attributes model performance degradation via a causal mechanism. They assume that distribution shifts are induced due to an intervention in the causal mechanism which results in model performance changes, and uses Shapley values to attribute changes to specific distributions. These two methods are fundamentally different from SHML in multiple respects. First and most important, these works do not propose any actions on the basis of these failures or shifts. The primary goal of both works is to understand why a distribution has changed or a model has failed, attributing it to a causal mechanism, instead of adapting the model to perform optimally. Second, the theoretical formalism introduced is substantially different and comes with different properties. Both works operate within the directed acyclic graph (DAG) framework, whereas we operate under a diagnosis component which is defined as a vector over a space of possible reasons. Other key differences relate to the adaptation mechanism, shift assumptions, adaptation assumptions, level of granularity of the diagnosis, level of granularity of the adaptation, or testing.

Recent work has already started coming out on understanding distribution shifts [35]. It is known that understanding why a distribution shift happens is important for mitigating that shift [35]. Some other people have looked at modeling shifts via latent variable models without relying on access to labels at test time [34]. However, as before, these methods do not share the objective of finding optimal actions for adaptation.

Self-healing systems outside ML . Self-healing systems have been proposed outside of machine learning [68-72]. We view these as inspirations for our work but consider them disparate and separate because none of them touch upon the core problem of machine learning model degradation, and have not been applied in practice.

A.3 Unique properties of self-healing machine learning

The core of self-healing machine learning revolves around two primary components: the deployed ML model f and the healing system H . Here, we provide additional clarity on these components and their interactions:

Definition of the Deployed Model f The model f represents the deployed machine learning model that we aim to heal. It is the function that makes predictions on input data and whose performance we're trying to maintain and improve. In our viability studies, we demonstrate this framework using logistic regression models as f , though the approach generalizes to any predictive model.

Relationship Between f and π While f is the model making predictions, π is the adaptation policy-a function that determines what actions to take to modify f based on the diagnosed reasons for its performance degradation. The healing system H follows policy π to output actions a (such as a 1 : retrain a model or a 2 : remove corrupted features) which are then implemented onto f . Therefore, H follows policy π which helps to determine optimal actions a that change/modulate the deployed ML model f .

Practical Implementation In our viability studies with H-LLM, the policy π is instantiated with an LLM (GPT-4) which uses the diagnosed reasons for model failures (also achieved with an LLM) to propose concrete actions. For instance, if f is a diabetes prediction model and π diagnoses that f 's performance has degraded due to concept drift, π might suggest an action to retrain f with more recent data or to adjust feature weights.

B H -LLM

This section provides more details on H -LLM.

B.1 Algorithm and details H-LLM

The algorithm of H -LLM is presented in Algorithm 1.

Extended discussion .

Monitoring . We use statistical drift detection algorithms to monitor model degradation from k previous time points [29, 39, 48]. Diagnosis is triggered if a shift is detected. For our practical implementation, we use the Drift Detection Method, a popular method for binary drift detection classification.

Diagnosis . Upon detection, H -LLM uses an extractor function E ∶ D ∗ →D c to transform the dataset information into an information vector

v . This extractor function is a mapping from the dataset to information about the dataset. It takes information before the shift happened and calculates summary statistics, such as the mean, average, standard deviation, percentiles, etc., within each column, as well as the performance of a deployed model f under various data slices. For instance, this would also involve looping over all variables, binning them into 10 discrete values and calculating the average model performance across each bin. This is done to ensure that the information contained within the information vector are both summary statistics, i.e. how the

Algorithm 1: H -LLM

data has changed, as well as specific performance metrics within data slices. The information is used to generate specific diagnoses as to what has happened. We observe, for instance, that summary statistics are extremely helpful if there are any larger deviations from average, as the diagnosis module within H -LLM picks up on these clues. This information is provided as textual information to the next step which is the diagnosis phase.

The information vector is used as a textual representation within the next LLM call to generate concrete hypotheses / diagnoses about the reason for the f failure. This is where additional context c could be added, if available, such as the presence of any particular exogeneous events that could have affected model performance and could guide the diagnosis search. In the future, we envision that the additional context could be acquired by the system itself. This is used in a chain-of-thought module with self-reflection, where k candidates for degradation are generated along with associated scores. We employ different 'diagnosis' modules within H -LLM. For instance, there is a specific diagnosis module that only attempts to find which covariates are responsible for degradation. The system level instruction could be as follows: 'Find covariates that are responsible for the model degrading'. However, we also supplement this with more broader reasons for degradation, such as 'Find and hypothesize reasons that could have resulted in model degradation , given the information provided'. We provide three prompt templates used to hypothesize issues in Section B.2.1. We sample such prompts m times using MC sampling. The chain-of-thought and self-reflection is implemented by calling H -LLM multiple times to re-consider the evidence and hypotheses. Table 2 illustrates diagnoses generated by H -LLM.

III. Adaptation . Conditioned on the empirical diagnosis distribution ˆ ζ , H -LLM generates m candidate adaptation actions { a j } m j = 1 ∼ l (⋅∣ ˆ ζ ) via CoT-based MC sampling. Specifically, we focus on three kinds of adaptation actions.

Generic adaptation actions

Adaptation actions by removing corrupted data

Adaptation actions by training multiple models for subsets of the data

This is reflected in three different prompt templates in Appendix B.2.2.

Generic adaptation actions . The first attempt is to find generic adaptation actions that the diagnosis module suggests on the basis of the identified evidence. These are often quite generic, for instance, 'add new covariates that could control for the seasonality'. In many such cases, within the confines of our experiments, we do not have the ability to resolve the issues on the basis of the proposed solutions. Therefore, we add two more directly actionable adaptation actions that are also attempted by H -LLM after the generic adaptation actions have been attempted.

Adaptation actions by removing corrupted data . Another concrete adaptation action is that we instruct H -LLM to hypothesize specific data slices that might have been corrupted. This could be, for instance, biologically implausible values (negative insulin, age > 200, implausible hba1c levels), mismatches (e.g. height, weight do not match BMI), sudden shifts in the data (ages change from averages of 30 to 60), and other. The adaptation module then proposes which data slices to remove to achieve superior performance. These suggested data slices are then removed and re-trained in the next batch.

Adaptation actions by training multiple models . The final concrete adaptation action is to propose specific data slices where the model might have drifted within that slice. This is done because instead of global drifts , models sometimes drift locally and require complete re-training of the new dataset.

Example outputs of such strategies are presented in Appendix B.3. We note, however, that, in reality, there might be many possible adaptation actions, such as re-training the model on combinations of old and historical data, re-using old models, re-using parts of old models, creating custom ensembles, changing models altogether, changing hyperparameters or adding regularization terms, building different models for different samples based on their difficulty, switching between symbolic and predictive ML models in the face of high uncertainty, and many more. Our approach is to introduce only the primary few ways with the hope of extending this in the future.

As before, because the actions sampled from l are textual representations, we use an interpreter function to execute each a on f .

IV. Testing . The sampled actions are evaluated on an empirical dataset (Def. 7), and the empirically optimal action ˆ a ∗ = arg min j ∈[ m ] R ( a ) is implemented. Limited access to the shifted DGP complicates evaluating R ( a ) , but it can be approximated with empirical data ˆ D test by using a backtesting window , continuously incoming data , or historical data . In all of our experiments, we use a backtesting window. However, other strategies could be attempted. The different strategies are explained in greater detail in Appendix B.4.

Goal . This procedure aims to approximate the optimal action (Def. 8). We remark that there might be better adaptation policies that could be suggested on the basis of evidence. Likewise, there might be better diagnosis modules available. We see H -LLM as a first attempt to integrate self-healing into ML.

B.2 Prompt templates used

The following are some of the primary prompt templates used within H -LLM.

B.2.1 Prompts related to diagnosis

Code Listing 2: Generic diagnosis prompt for searching combinations of covariates responsible for degradation

B.2.2 Prompt templates related to adaptation

B.3 Example outputs of H-LLM

Code Listing 8: Generic issue response which identifies overall issues within the dataset.

Code Listing 9: Example response about which subgroups to remove

B.4 Evaluation strategies of self-healing algorithms

Self-healing relies on a testing phase, i.e. the ability to test whether the proposed actions perform well on a test dataset. However, given that the distribution has shifted and the historical data no longer represents the new distribution, one might ask: how can we test models on this new distribution? The primary alternative used in our experiments is a backtesting window which we define formally below.

Definition 3 (Backtesting Window) . Let {P t } t ∈ T be a sequence of probability measures on X × Y , and suppose a distributional shift occurs at time t ∗ ∈ T , i.e., P t ∗ ≠ P t ∗ -1 . Let t ′ > t ∗ be the time at which the self-healing system detects the shift. The backtesting window is the time interval [ t ∗ , t ′ ] satisfying the following properties:

We notice that the backtesting window is a unique property that arises uppon sudden shifts in the data generating process. Specifically, because we assume only two data generating processes and a transition between them at time point t , then all points k where k > t will be from the new DGP and all points k < t will be from the old DGP. Since a drift detection algorithm requries some time to detect the drift, by the time a drift has been detected, we have some collected data from the new distribution which we call the backtesting window . We can therefore optimize our actions on this specific window of the dataset.

Clearly, this does not hold when the assumptions about the nature of the shift change. In such a case, we could always use continuously incoming streaming data. Upon the arrival of each new batch, we can test each proposed action and validate it, consistently upgrading and using the actions that perform well on the most recent batch of data. This strategy assumes that the labels are almost immediately available at prediction time. If not, another strategy employed could be to test such actions on the mot recent available data with labels.

Other approaches could include generating synthetic data to imitate the new shift with labels or using historical data by de-biasing it. However, these are experimental approaches which need further validation.

B.5 Computational notes

Computational overhead. SHML methods have larger overhead than reason-agnostic approaches due to the self-healing system (LLM pipeline) identifying model failure reasons. Practically, it takes 20-40 seconds to implement a full pipeline and correct a model upon drift detection. This overhead is negligible for real-world systems given the benefits. Overhead may vary across systems.

Sample efficiency. No differences exist as failure detection doesn't depend on sample size, but on self-healing pipeline complexity.

C Case study design

Code can be found at: https://github.com/pauliusrauba/Self_Healing_ML or https:// github.com/vanderschaarlab/Self_Healing_ML

C.1 Details on the experimental setup

Experimental setup . To evaluate the performance of self-healing systems, we require to manipulate the data generating process (DGP) and ask 'what-if' questions. Real-world datasets, while valuable, do not offer control over the DGP and come with pre-embedded biases that can implicitly affect detection systems [73]. In contrast, by using synthetic data to control the DGP, we can run controlled in silico experiments and perform viability studies [74]. Furthermore, the overwhelming majority of model adaptation methods are designed for tabular data (refer to Sec. 2 and Sec. A) which includes our benchmarks (see Sec. 6.1). Therefore, we simulate a diabetes prediction task [49-51]. We perfectly mimic the introduced setup in Sec. 3.1. Our goal is to predict the presence of diabetes Y t ∈ { 0 , 1 } at each time point t for a set of n observations, generated according to a (changing) pre-specified DGP log ( P ( Y t = 1 ∣ X t ) P ( Y t = 0 ∣ X t ) ) = α t +∑ k ∈ K β t,k X t,k + ϵ t , where K includes relevant covariates such as Age or BMI, β t,k are time-varying covariates and ϵ t ∼ N( 0 , σ 2 ) is a noise component.

We generated synthetic data for the diabetes prediction task. Each feature is sampled from a normal distribution with specified parameters:

Hemoglobin A1c (HbA1c) levels are sampled from a normal distribution: HbA1c ∼ N( 5 . 7 , 0 . 5 2 ) .

Fasting Glucose levels are sampled from a normal distribution: Fasting Glucose ∼ N( 100 , 15 2 ) .

Age is sampled from a normal distribution: Age ∼ N( 50 , 12 2 ) .

Body Mass Index (BMI) is sampled from a normal distribution: BMI ∼ N( 25 , 4 2 ) .

Blood Pressure is sampled from a normal distribution: Blood Pressure ∼ N( 120 , 15 2 ) .

Cholesterol levels are sampled from a normal distribution: Cholesterol ∼ N( 200 , 40 2 ) .

Insulin levels are sampled from a normal distribution: Insulin ∼ N( 85 , 45 2 ) .

Physical Activity is sampled from a normal distribution: Physical Activity ∼ N( 3 , 1 2 ) .

The observations X are constructed as a matrix where each row is an instance of the generated features. The outcomes are then determined by running the model through a logistic regression and obtaining a binary outcome value.

C.2 Details on viability studies

C.2.1 Viability Study I

Viability Study I . To simulate covariate shift and introduce data corruption, we follow these steps:

Generate two datasets with different coefficients and noise parameters:

The first dataset with n 1 = 100 , 000 samples, coefficients β 1 = [ 0 . 3 , 0 . 0075 , -0 . 01 , 0 . 05 , 0 . 04 , -0 . 03 , -0 . 02 , -0 . 1 ] , and noise ϵ 1 ∼ N( 0 , 0 . 2 2 ) .

The second dataset with n 2 = 100 , 000 samples, coefficients β 2 = [-0 . 3 , -0 . 0075 , 0 . 2 , -0 . 05 , -0 . 015 , -0 . 001 , 0 . 02 , -2 ] , and noise ϵ 2 ∼ N( 0 , 0 . 2 2 ) .

Split the first dataset into training and testing sets, using a 70/30 split.

Combine the testing set of the first dataset with the entire second dataset to form the complete testing set. The second testing set therefore contains a shift where the transitions between the DGPs happen.

In addition to the shift in the DGP, we introduce outliers in the second dataset by multiplying selected features by an outlier factor that control. By default, it is set to

This outlier factor corrupts the number of columns corrupted by k , and corrupts a percentage of values within the column, denoted as τ .

The shift index is determined as the starting point of the second dataset in the combined testing set.

We measure and report the performance of the model during the second data generating process.

Summary of parameters :

n 1 = 100 , 000 : Number of samples in the first dataset.

n 2 = 100 , 000 : Number of samples in the second dataset.

β 1 = [ 0 . 3 , 0 . 0075 , -0 . 01 , 0 . 05 , 0 . 04 , -0 . 03 , -0 . 02 , -0 . 1 ] : Coefficients for the first dataset.

β 2 = [-0 . 3 , -0 . 0075 , 0 . 2 , -0 . 05 , -0 . 015 , -0 . 001 , 0 . 02 , -2 ] : Coefficients for the second dataset.

ϵ 1 ∼ N( 0 , 0 . 2 2 ) : Noise for the first dataset.

ϵ 2 ∼ N( 0 , 0 . 2 2 ) : Noise for the second dataset.

Seed for reproducibility: 42.

Proportion of outliers introduced: 20%.

Features corrupted varies.

Viability Study I. Summary of the benchmarks . Below we describe the key benchmarks.

Benchmark 1. New model retraining . We use the Drift Detection Method (DDM) to monitor changes in the data distribution and retrain the model when a drift is detected. The procedure includes:

Split the test data into multiple batches.

Train the model on the initial training dataset.

For each batch in the test set:

Predict the outcomes and calculate the accuracy.

Update the drift detector with the prediction error (1 - accuracy).

If drift is detected:

-Retrain the model on the most recent batch.

Benchmark 2. Ensemble method . This algorithm uses an ensemble of models to improve robustness against data shifts. It combines the predictions of multiple models, each trained on different segments of the data. The procedure involves:

Initialize an ensemble with a single model trained on the initial training dataset.

Split the test data into multiple batches.

For each batch in the test set:

Aggregate predictions from all models in the ensemble, weighted by their current accuracies.

Make final predictions based on the weighted aggregation.

Calculate the accuracy and update the drift detector with the prediction error.

If drift is detected:

-Train a new model on the current batch and add it to the ensemble.

-Update the weights of all models based on their accuracies.

This method maintains a diverse set of models that can adapt to different aspects of the data distribution, enhancing overall performance and stability.

Benchmark 3. Partial updating . The model is retrained using a sliding window of the most recent data batches. This allows continuous adaptation to recent changes in the data distribution. The steps are:

Split the test data into multiple batches.

Train the model on the initial training dataset.

Maintain a buffer to store the most recent batches.

For each batch in the test set:

Predict the outcomes and calculate the accuracy.

Update the buffer with the current batch.

If the buffer exceeds a predefined size (window size), remove the oldest batch.

Retrain the model using the data in the buffer.

Our method. H -LLM . In this example, we use H -LLM to identify corrupted columns and values and identify whether they need removal. The overall setup is as follows:

Split the test data into multiple batches.

Train the model on the initial training dataset.

Maintain buffers to store the most recent batches and a backtesting window.

For each batch in the test set:

Predict the outcomes and calculate the accuracy.

Update the buffers with the current batch.

Update the drift detector with the prediction error.

If drift is detected:

-Use the self-healing mechanism to inspect the most recent and previous batches.

-Propose multiple adaptation strategies

-Select the best adaptation strategy on a backtesting window.

-Retrain the model on the inspected and backtesting data to recover from the detected drift.

In all cases, the optimal strategy was removing a corrupted batch of data, where the amount of corrupted values or their extent varied.

Comments on the experimental setup of viability study I . The goal of this setup is to showcase that blindly retraining the model or using pre-determined actions is not necessarily optimal. In this case, the strategy required is to understand that the model requires full re-training and some values have been corrupted which require careful dealing, such as adjustments or removal.

C.2.2 Viability Studies III - VI

.

Viability Study III . We employ the Drift Detection Method (DDM) and vary the sensitivity parameter indicated on the x-axis. We then calculate the recovery time - how much time it takes to detect the shift-, as well as the post-intervention accuracy. As discussed in the main paper, this is purely determined by the DDM. For each detected drift, we fully run H -LLM to detect issues and propose adaptation strategies that are tested on a backtesting window. If none of them beat the performance of the current model, the existing model f is deployed.

Viability Study IV . We evaluate how well self-healing systems identify the root causes of problems. We corrupt a proportion of observations ( corruption coefficient ) by multiplying their values by a factor ( outlier factor ) and see if the H -LLM detects issues related to these factors. We output a probability distribution over diagnoses of which variable is corrupted. Knowing the true corrupted variable, we measure the difference between the distributions using KL-Divergence, with lower values indicating better matches between true and estimated corruption. A uniform diagnosis baseline represents random guessing. Here is an example of what it means for the 'true probabilities' to be corrupted when the corrupted column is 'Age'.

Recall that H -LLM produces normalized probability guesses, as shown in Sec. B.3. Therefore, the obtained predicted guesses of which variable is corrupted in this setup looks as follows:

When the corruption coefficient is higher, the output looks as follows:

Therefore, the KL divergence is computed between these two probability distributions. The KL is the highest when the outputted probability distribution is uniform (first example) and the lowest when it perfectly matches the reference/true probability distribution. It has been shown that with certain techniques, LLMs can generally output calibrated confidence scores or probabilities [75].

The reason why the KL-divergence decreases is because the predicted probabilties put greater relative value on the true corrupted value (i.e. the 'Age' column in this example) as (i) the outlier factor increases and as (ii) the percent of values corrupted increase.

Viability Study V . We study the sensitivity of SHML adaptation policies by examining how well actions perform based on (i) the number of corrupted values and (ii) the size of the backtesting dataset. Fig. 6 shows this relationship. The corruption coefficient is described in the overall experimental setup. The size of the backtesting window is the size of the dataset used to evaluat the proposed actions. Recall that H -LLM has three adaptation actions in place: (i) generic; (ii) filtering corrupted data slices; and (iii) training slice-specific models (Appendix B). For this experiment, we focus on actions proposed by the second adaptation strategy: filtering corrupted data slices. Each adaptation action is an identified data slice by H -LLM that might be corrupted, the removal of which might improve performance. The following is an example of proposed adaptation actions by the removal of the following queries (each query is a separate candidate adaptation action):

Such actions are proposed for each range of values corrupted and evaluated accordingly.

Viability study VI . We study the importance of the testing component (Eq. 7) by evaluating H -LLM suggested actions with and without the testing phase (backtesting window) and comparing their accuracies. Fig. 7 shows this relationship. The action with the backtesting window is the action which has received the highest empirical performance on the backtesting window. In contrast, the action proposed by 'no backtesting window' is the action that is selected as the most likely one by H -LLM without any empirical validation. 'Most likely' implies that after a few iteration loops, this was the action that was listed as the first action to perform. This showcases the usefulness of having a way to filter out actions with some specific actions. We mimic the setup from study IV where each action is a specific subgroup to filter out to achieve better performance due to the corrupted nature of the data.

C.3 Other experimental details

We note that all experiments were performed using two compute resources: a server with NVIDIA RTX A4000 GPU and 18-Core Intel Core i9-10980XE, as well as an Apple M1 Pro 32GB RAM. We exemplify H -LLM with GPT-4 via an API.

D Extended experiments

This section provides a few additional experiments or more detail regarding the experiments presented in the main paper.

D.1 Monitoring

Setup . We vary the warm-star criterion within drift detection methods to evaluate the recovery time and post-intervention accuracy of H -LLM. The warm start parameter is the minimum number of samples required to conclude that a drift has been detected and trigger re-training or selfhealing.

Discussion . Fig. 8 showcases the relationship between the warm-start parameter and the average recovery tiem and post-intervention accuracy. You see the massive increase in average covery time that jumps when the warm-start is set at a relatively high threshold. This results from a drift detection algoritihm detecting a false positive drift just before the actual drift. However, given the wwarm-star parameter, there was a significant delay in re-triggering the self-healing system. This suggests self-healing systems benefit from lower warm-start parameters in case the drift detection algorithms are sensitive to false positives. This corresponds with a relative drop in the post-intervention accuracy because of the longer time it took to trigger self-healing.

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Takeaway . Self-healing systems benefit from lower warm-start parameters in case drift detection systems are sensitive to false positive drifts.-intervention accuracy with smaller thresholds.

D.2 Diagnosis

Setup . In this experiment, instead of corrupting a single variable which is responsible for model degradation, we corrupt n variables to evaluate how well H -LLM can diagnose multiple corrupted values at once. With each corrupted columns, the true corrupted probability changes. For instance, if there are four columns and there is a single corrupted column, the true corruption vector is [1, 0, 0, 0]. If there are four corrupted columns, then it is [0.25, 0.25, 0.25, 0.25]. We use these probabilities and compare them to the corruption probabilities outputted by H -LLM. This is shown in Fig. 9.

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Discussion . This showcases that the more columns are corrupted, the better the predictive diagnosis becomes. For instance, once all columns are corrupted, H -LLM outputs a uniform diagnosis because it has no information given the evidence observed. This exactly corresponds to the true corruption probability, outputting a KL of 0. We notice that the KL generally decreases with the number of corrupted columns for this reason.

Takeaway . Greater uncertainty results in more uniform diagnosis. However, less uncertainty can make it difficult to directly pinpoint the exact cause, causing more uncertainty.

D.3 Adaptation experiment

This section expands on the adaptation experiments by providing more variables and values by corruption coefficient and the number of columns corrupted.

D.4 Effects of Self-Healing across corruption levels

We systematically analyze how self-healing effectiveness varies with corruption levels across our five datasets (Airlines, Poker, Weather, Electricity, and Forest Type). For each dataset, we vary both the corruption value τ and the number of corrupted columns k , measuring accuracy with and without the self-healing mechanism. Figure 10 shows that self-healing's impact grows with corruption severity. Specifically, as either τ or k increases, the gap between baseline and self-healed performance widens. This pattern holds consistently across all datasets, though with varying magnitudes. These results demonstrate that self-healing becomes more crucial as data degradation becomes more severe, providing a safety mechanism for maintaining model performance under challenging conditions.

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D.5 Extended benchmarks

We extend our comparison on the diabetes prediction task to include additional adaptation methods and adaptive algorithms. Table 9 presents results from ten different approaches, including standard adaptations (no retraining, partial updates, new model training, ensemble methods), streaming-specific algorithms (ADWIN Bagging, Hoeffding Tree), and our SHML approach.

The results show that while specialized streaming algorithms (e.g., Hoeffding Tree at 0.70 accuracy) outperform basic adaptations, they still fall short of SHML's performance (0.76 accuracy).

D.6 Component-wise Ablation Analysis

To understand the importance of each SHML component, we conduct an ablation study by systematically removing each component and observing the impact. Table 10 shows the results of this analysis.

The ablation reveals that each component is crucial for effective self-healing. Removing monitoring (52% accuracy) prevents the system from triggering adaptation. Without diagnosis, the system proposes non-sensical actions, leading to baseline performance. Removing action generation or testing similarly degrades performance to baseline levels, though testing removal shows slightly better performance (62%) as some reasonable actions are still attempted, albeit without proper validation.

This analysis empirically validates our framework's design, showing that effective self-healing requires all four components working in concert.

D.7 Model agnosticism

We evaluate SHML's effectiveness across ten different ML models to demonstrate its model-agnostic nature. Table 11 shows results for models ranging from simple (e.g., Decision Trees) to complex (e.g., XGBoost), comparing various adaptation strategies. SHML consistently outperforms baseline approaches across all model types, with improvements ranging from 11 percentage points (Naive Bayes) to 31 percentage points (LDA). This consistent improvement demonstrates that SHML's benefits are not tied to any particular model architecture but rather stem from its ability to reason about and address degradation causes.

E Optimal diagnosis

Here, we prove that under the stated assumptions, the optimal diagnosis has zero entropy.

Proposition 3. Under Assumption 1, the optimal diagnosis ζ ∗ has a zero entropy, i.e., H ( ζ ∗ ) = 0 .

Proof. By Definition 2,

Let z ∗ ∈ Z such that

Then

Therefore

As A is finite, we write the expected value as follows.

By Assumption 1, this can be rewritten as:

We change the order of summation to arrive at the following.

The inner sum can now be rewritten as an expectation.

Thus we can rewrite the minimization problem as follows.

and by the definition of entropy and ( z ∗ ) † we get

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: The paper presents a novel paradigm called self-healing machine learning. This is established in Section 3.3 and Sec. 4, where we develop the theoretical underpinnings of this field. We also make significant practical contributions by proposing the first-ever selfhealing algorithm presented in Sec. 5. We discuss the positive impacts of this technology in the introduction and the discussion section, where we argue it can have transformative effects in a variety of real-world situations. We clearly show the viability of self-healing machine learning in Sec. 6.

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Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Self-healing machine learning inherently assumes that diagnoses can be performed well and that actions can be proposed on the basis of these diagnoses. This poses challenges we discuss in Sec. 5.1. We attempt to overcome these limitations by using language models and incorporating their unique properties into H -LLM. We discuss further challenges of self-healing systems in the discussion section (Sec. 7)

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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

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Justification: In order to establish the relationship between diagnosis and the actions, we define the certainty diagnosis (Def. 1) and the optimal diagnosis (Def. 2). Furthermore, we assume independent actions (Assumption 1). Under this, we establish two propositions about the entropy of an optimal diagnosis (Proposition 1) and its existence (Proposition 2). One of the proofs is in the main paper and the other one can be found in Appendix E.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper titled \"Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments\" introduces the concept of self-healing machine learning (SHML). This framework aims to address performance degradation in machine learning models due to distributional shifts. Unlike traditional concept drift adaptation methods, SHML focuses on diagnosing the reasons for model degradation and proposing targeted corrective actions. The paper presents a theoretical foundation for SHML, an algorithm (H-LLM) that uses large language models for diagnosis and adaptation, and empirical evaluations demonstrating the effectiveness of the approach.\n\nSTRENGTHS:\n1. The SHML framework is a novel approach to handling model degradation by diagnosing and addressing the root causes, rather than using reason-agnostic methods.\n2. The paper provides a solid theoretical foundation for SHML and demonstrates its practical viability through empirical evaluations.\n3. The concepts are well-explained, and the structure of the paper is logical and easy to follow. Figures and tables enhance the understanding of the proposed methods.\n4. SHML has significant potential in high-stakes applications where maintaining optimal model performance is critical, such as healthcare and finance.\n\nWEAKNESSES:\n1. While the paper demonstrates promising results, the empirical evaluation is limited to a simulated diabetes prediction task. Additional experiments in diverse real-world environments would strengthen the claims.\n2. The comparison with existing concept drift adaptation methods is not exhaustive. A broader set of baseline comparisons would provide a clearer picture of the advantages and limitations of SHML.\n3. The paper lacks detailed information on the implementation of H-LLM, especially regarding the practical challenges of deploying such a system in real-world scenarios.\n4. Although the authors mention the availability of the code upon acceptance, more details on the experimental setup and data used would help in reproducing the results.\n\nQUESTIONS:\n1. Can you provide more detailed results and analysis of SHML in a wider range of real-world environments beyond the simulated diabetes prediction task?\n2. How does SHML compare to other state-of-the-art concept drift adaptation methods in terms of computational overhead and sample efficiency?\n3. Can you elaborate on the specific contributions of each component of the SHML framework to the overall performance improvement?\n\nLIMITATIONS:\nThe authors acknowledge the limitations of SHML, including the challenges in accurately diagnosing the root causes of performance degradation and the potential computational overhead of the approach. They also discuss the need for sufficient parallel processing capacity to handle the increased demands of multiple diagnostic and adaptation actions. The broader societal impact of SHML, including potential misuse in high-stakes applications, is also briefly addressed.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new concept of self-healing machine learning, or SHML. The idea is based on understanding and addressing the reasons of performance drops in ML systems, thereby going beyond most common approaches that are labelled as reason-agnostic. The approach is based on a pipeline well illustrated in Fig 2 that includes four steps: monitoring, diagnosis, adaptation and testing. Formal definitions are provided. Experimental support is provided to demonstrate some aspects of the new idea and its potential advantages.\n\nSTRENGTHS:\nI have identified the following strength: \n- The paper starts from an interesting assumption that the reasons for performance drop should be considered and investigated. \n- The paper is well organised and easy to read. \n- The method is described with precise formalism.\n\nWEAKNESSES:\nI have identified the following weaknesses: \n1. While the main assumption is move on from reason-agnostic methods to a system that attempts to understand the reasons, the paper does not offer a good classification of such reasons, mainly because it main a meta-level approach, in which reasons are to be learned. This could be less effective than pre-defining monitoring and diagnosis methods according to known causes.  \n2. As a follow-up from the previous point, the diagnostic part that generates hypotheses via a LLM may or may not be correct and the adaptation may or may not be effective. I find it hard to get a feeling of how well the system would work on a large set of benchmarks and problems from the evidence and experiments presented in the paper. \n3. Most of the interesting details of the paper are actually in the appendix.\n4. I disagree that this is the first non-reason-agnostic method, as the authors overview and list other approaches that do focus on the causes of performance drop. I agree that this may be the first approach to tackle the issue comprehensively. Nevertheless, the novelty, knowledge gap, and improvements of the approach with respect to existing approaches is not well summarised in the introduction/related work, which is rather short and it refers to the appendix for further detail.\n\nQUESTIONS:\n1. Is it possible to define a medium to large set of conditions in known benchmarks that could be use to extensively test the SHML?\n2. One challenge in a system with multiple sequential steps is that the failure of one component affects all the following. E.g. failure in diagnosis will affect adaptation and testing.  How can we assess how well the system work when failure in one component compromises the correct functioning of others?\n\nLIMITATIONS:\nI find that the limitation part (before the conclusion and in 5.2)  is too short . Yes, I agree that the correct identification of the root causes of performance drop is challenging in real work scenarios, but the issue needs to be expanded and discussed more. For example, why is it difficult? Are some reasons that are more difficult than others to identify? Monitoring could also be challenging. Adaptation could be challenging. \n\nThe impact is sufficient covered.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a self-healing framework for machine learning models called Self-Healing Machine Learning (SHML). Unlike previous methods, SHML autonomously diagnoses the causes of model degradation and suggests corrective actions based on these diagnoses. The authors formalize SHML as an optimization problem, aiming to minimize expected risk by adapting to changes in the data-generating process (DGP).\nA theoretical framework for self-healing systems is introduced, exemplified by H-LLM, which leverages large language models for self-diagnosis and self-adaptation. Empirical analyses of SHML's components demonstrate its potential and effectiveness.\nThe paper underscores the importance of ensuring optimal performance in algorithms used in high-stakes applications. By enabling systems to autonomously adapt to new environments, SHML aims to advance self-healing systems, benefiting both the machine learning community and society. The theoretical framework lays the groundwork for developing optimal adaptation and diagnosis methods. The authors hope this work will stimulate further theoretical developments and encourage the adoption of self-healing systems in critical fields such as medicine and finance.\n\nSTRENGTHS:\nFrom the perspective of originality, this paper lowers the barrier for others to implement adaptation actions in machine learning models. Its importance lies in addressing the growing challenge of maintaining machine learning models, especially given their increasing usage. A framework capable of diagnosing drift or degradation and autonomously solving these issues is crucial. This paper effectively addresses this need, outperforming existing approaches in both presentation and results.\nThe organization of the paper is good, with a well-structured presentation. The detailed inclusion of examples, definitions, assumptions, theoretical explanations, and results in the main text and appendix is thorough and effective (but the appendix is probably required to really understand)\n\nWEAKNESSES:\nThe main weakness of this paper is its tendency to use overly long sentences and complex phrases, which can hinder readability and clarity. Another significant weakness lies in the viability section; many crucial details have been relegated to the appendix. This makes it difficult to fully understand and trust the framework based on the main text alone. Additionally, while the concept of \"self-healing machine learning\" is compelling, the section dedicated to it could be more concise and focused. Overall, improving the clarity of these sections would greatly enhance the paper's impact.\n\nQUESTIONS:\n1. Is the diagnostic step continuously operational during the entire period of model usage?\n2. How does the performance of the framework compare to benchmarks when applied to large-scale models?\n\nLIMITATIONS:\nAuthors mention a limitation in section \"SHML’s success relies on accurate root cause identification and finding effective adaptation policies which could pose challenges in some complex, real-world settings\" (Sec. 5.1).\nThis could be further explored.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

comments: SUMMARY:
Model performance degradation on unseen data is a classic problem. Existing approaches solve the problem through a deterministic strategy: change model, retraining, etc. This paper proposes an adaptive way to decide the action after model degradation automatically and introduces a self-healing framework. The evaluation thoroughly analyzes the intuition of SHML and its limitations.

STRENGTHS:
1. Autonomous healing for model performance degradation is an important problem.
2. The automatic adaptation idea is novel and interesting.
3. The evaluation is strong and thorough, which covers the details to help readers further understand the scope and the logic of the proposed method.

WEAKNESSES:
1. The writing is hard to follow in section 3. What is the practical meaning of f? How is it related to the policy? 
2. The assumption of the requirement for optimal adaptation actions is strong. Is there any practical case to support this assumption?

QUESTIONS:
Please refer to the weaknesses

LIMITATIONS:
Limitations are well-discussed in the main text.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13