Looking at the plot of your model’s validation error alongside that of your teammate leaves you scratching your head. Unfortunately, several sources contribute to run-to-run variation even when working with identical model code and training data. Alternatively, if the sets are fixed, the shuffling within the training set affects the order in which samples are iterated over, thereby affecting how the model learns. Reproducibility ensures correctness. We will be discussing different bias patterns and their underlying causes in a later section. A subreddit dedicated to learning machine learning. Welcome to the Community Portal for the UCI ML Repository. This may help in increasing the PPV and in effectively choosing research areas. Changes in machine learning frameworks: This may lead to different behaviours across versions and more discrepancies across frameworks. Chambers, and Russell E. Glasgow. According to a 2016 survey on reproducibility conducted by Nature (results in Figure 2), among the 1,576 researchers from various fields who responded to it, over half thought that there is a significant reproducibility crisis in science and some failed to even reproduce their own research [16]. PPV can be calculated as \(\frac{R(1-\beta n)}{R+1-[1-\alpha]n – R\beta n}\). The probabilities expressed under the i.i.d assumption are shown in Table 6 with \(n\), being the number of independent studies. Pete Warden [8] describes the typical life cycle of a machine learning model in image classification as follows: Warden describes this as a rather optimistic scenario, and emphasizes how hard it is for someone to step in, and reproduce each of the steps exactly. Understanding these patterns will help us spot and objectively evaluate biased results/conclusions. Automatically record the code, environment, parameters, model binaries, and evaluation metrics every time you run an experiment. Figure 1 shows the steps involved in a data analysis project and the questions a researcher should ask to help ensure various kinds of reproducibility. To simplify the calculations, we assume that all the studies addressing the same sets of research questions are independent and that there is no bias. However, when another researcher, Stephane Doyen, tried to replicate the result he found no impact on the volunteers’ behaviors. A recurrent challenge in machine learning research is to ensure that the presented and published results are reliable, robust, and reproducible [ 4, 5, 6, 7 ]. The next day you return to a bizarre surprise: the model is reporting 52.8% validation error after 10,000 batches of training. As shown in Figure 3, they observed different performances for each of these algorithms across different environments. fMRI measures the activity of a brain region by detecting the amount of blood flowing through it – a region which was active during a cognitive task is likely to have increased blood flow. In addition, there are no universal best practices on how to archive a training process so it can be successfully rerun in the future. This means that it needs to include reviews of articles which oppose the authors’ hypothesis. “Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses.” PloS one 9, no. We need to have robust conclusions. Bias is modeled using a parameter \(u\) which is the probability of producing a research finding even though it is not statistically significant purely because of bias. which is the probability of producing a research finding even though it is not statistically significant purely because of bias. Table 4 summarizes the results of their analysis. From the table, we can easily compute the PPV as a ratio of highlighted values. You will not forget to commit your changes because it just happens. To simplify the calculations, we assume that all the studies addressing the same sets of research questions are independent and that there is no bias. You start by training a model from the existing architecture so you’ll have a baseline to compare against. It could not replicate 47 out of 53 studies [18]. Inferential reproducibility is quite important to both scientific audiences and the general public. Based on these variables, Table 2 shows the probabilities of different combinations of research conclusions versus ground truth. This is the case since smaller studies imply smaller statistical power, and the PPV decreases as the power decreases. Next, we discuss the different types of reproducibility, and for each one, we discuss its importance, barriers to enforcement, and suggestions to help achieve it. Engstrom, Logan, Andrew Ilyas, and Anish Athalye. Given its complexity, fully enabling reproducibility will require effort across the stack - from infrastructure-level developers all the way up to ML framework authors. First, we focus on Production Reproducibility: the ability to faithfully reproduce ML behaviors that occur in production, a task with some similar and some different challenges from ML reproduction in development or research environments. While evaluating the statistical significance of a given set of results, we must “correct” for the fact that multiple teams are working on the same problem. Figure 2: Reproducible defined. These conclusions are usually listed in the abstract, introduction and discussion sections. In this blog post, we will introduce the different types of reproducibility and delve into the reasons for the formulation of these questions at each step. Reproducibility is important not just because it ensures that the results are correct, but also because it ensures transparency and gives us confidence in understanding exactly what was done. It was found that only 6% of the presenters shared their code. Press J to jump to the feed. The inference time on the existing ML model is too slow, so the team wants you to analyze the performance tradeoffs of a few different architectures. Log in sign up. Statistical power is given by \(1 – \beta\), and it is the probability of finding a true relationship. Following are the main points from her checklist: The checklist can help verify several components of a study we are reproducing, placing particular emphasis on empirical methods. Sometimes, even the same labs can’t reproduce their previous findings. Without it, data scientists risk claiming gains from changing one parameter without realizing that hidden sources of randomness are the real source of improvement. https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/, https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf. Deep learning has brought impressive advances in our ability to extract information from data. It is quite easy to forget to adjust the significance of the reported results to account for the multitude of hypotheses that have been tested. Using baselines to prove a new technique is better – We need to obtain the same accuracy for the baseline as the original research if we want to prove that our approach is an improvement. Suppl 1 (2009): S125. 2018 © Machine Learning | Carnegie Mellon University. The analysis also showed that highly cited articles and articles in reputed venues could also present overestimated effects. In fact, this checklist was adopted as a mandatory requirement for papers submitted to NeurIPS 2019. A very common problem is that the source code used in a study is not open-sourced. Table 6: The effect of multiple studies on the probability of having a given relationship between the truth and the research findings [1]. Fanelli, Daniele, Rodrigo Costas, and John PA Ioannidis. [15] on the association between various bias patterns and overestimation of effect sizes. An example of this is the dropout layer which drops neurons with probability p, leading to a different architecture every time. US effect: Certain sociological factors might cause authors from the US to overestimate effect sizes. The engineer who developed the original model is on leave for a few months, but not to worry, you’ve got the model source code and a pointer to the dataset. Their results are highlighted in Table 5. We will also show how bias often leads to an increase in the number of false findings [1]. Ioannidis relies on post predictive value (PPV) which defines the probability of a finding being true, after a finding has been claimed based on achieving formal statistical significance. Then, voxelwise statistics were computed using a general linear model (GLM). A t-contrast was used to test for regions with significant signal change during the photo condition compared to rest but no corrections were performed. Early-extreme: This pattern is related to the previous one in that early studies usually show extreme effects in any direction as such findings are often controversial and have an early window of opportunity for publication before more mature studies are conducted. She publishes her results, with code and weights of the model. The reproducibility challenge. For reproducibility, we have to record the full story and keep track of all changes. Instead, you see this: You resist the urge to shout at your computer. This shift not only multiplies the sources of non-determinism but also increases the need for both fault tolerance and iterative model development. Both of these fields have long histories and have multiple companies and/or research groups working on similar problems, possibly leading to the aforementioned reproducibility issues. Let’s delve into some of the math behind these claims, and then we will show a few real-life cases which support the corollaries he derives from the analysis. fMRI breaks the brain up into thousands of minuscule cubes called voxels and records the activity for each voxel. An example showing the correlation between “Age of miss America” and “Murders by steam, hot vapour, and hot objects”  is shown in Figure 9. We need to provide a clear description about the data collection process, and how samples were allocated for training, testing, and validation. Welcome to the ML Reproducibility Challenge 2020! Though the difference has narrowed, you’re still seeing a 7% gap in classification error! Reproducibility, obtaining similar results as presented in a paper using the same code and data, is necessary to verify the reliability of … Table 1 shows the notation used by Ioannidis. Track everything you need for every experiment run. This means that findings are reported to be significant when in fact they have occurred by chance. In the same vein, when Bayer attempted to replicate some drug target studies, 65% of the studies could not be replicated [18]. The interpretation of the results obtained using clustering techniques can also be highly subjective and different people can perceive them in different ways. After she is satisfied with her code, she might do a mass file transfer to her GPU cluster to kick off a full training run. Next, they tried to pull up a few open source implementations of the same algorithm, and compared their performances on the same environment, in their default settings. Table 3 shows the effects of bias on the various probabilities which were introduced in Table 2. The situation is not any better in machine learning. From the table, we can easily compute the PPV as a ratio of highlighted values. Quite surprisingly, they discovered that the pressure to publish does not lead to bias. In fact, ML researchers have found it difficult to reproduce many key results and it is leading to a new conscientiousness about research methods and publication protocols. Power is also proportional to the effect size, and thus, the larger the effect size, the higher the power, and thus higher PPV. “What does research reproducibility mean?.” Science translational medicine 8.341 (2016): 341ps12-341ps12. Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. For instance. It is common in academia for multiple teams to focus on similar or the same problems. According to Ioannidis [1], bias is the amalgamation of many design, data, analysis, and presentation factors that may lead to the production of research findings when they should not have been produced. This is also seen in Figure 10, for different levels of power and for different pre-study odds.