The Cram Method for Efficient Simultaneous Learning and Evaluation
- URL: http://arxiv.org/abs/2403.07031v1
- Date: Mon, 11 Mar 2024 04:19:05 GMT
- Title: The Cram Method for Efficient Simultaneous Learning and Evaluation
- Authors: Zeyang Jia, Kosuke Imai, Michael Lingzhi Li
- Abstract summary: We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation.
Because it utilizes the entire sample for both learning and evaluation, cramming is significantly more data-efficient than sample-splitting.
Our extensive simulation studies show that, when compared to sample-splitting, cramming reduces the evaluation standard error by more than 40%.
- Score: 0.9208007322096533
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce the "cram" method, a general and efficient approach to
simultaneous learning and evaluation using a generic machine learning (ML)
algorithm. In a single pass of batched data, the proposed method repeatedly
trains an ML algorithm and tests its empirical performance. Because it utilizes
the entire sample for both learning and evaluation, cramming is significantly
more data-efficient than sample-splitting. The cram method also naturally
accommodates online learning algorithms, making its implementation
computationally efficient. To demonstrate the power of the cram method, we
consider the standard policy learning setting where cramming is applied to the
same data to both develop an individualized treatment rule (ITR) and estimate
the average outcome that would result if the learned ITR were to be deployed.
We show that under a minimal set of assumptions, the resulting crammed
evaluation estimator is consistent and asymptotically normal. While our
asymptotic results require a relatively weak stabilization condition of ML
algorithm, we develop a simple, generic method that can be used with any policy
learning algorithm to satisfy this condition. Our extensive simulation studies
show that, when compared to sample-splitting, cramming reduces the evaluation
standard error by more than 40% while improving the performance of learned
policy. We also apply the cram method to a randomized clinical trial to
demonstrate its applicability to real-world problems. Finally, we briefly
discuss future extensions of the cram method to other learning and evaluation
settings.
Related papers
- Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning [97.2995389188179]
Recent research has begun to approach large language models (LLMs) unlearning via gradient ascent (GA)
Despite their simplicity and efficiency, we suggest that GA-based methods face the propensity towards excessive unlearning.
We propose several controlling methods that can regulate the extent of excessive unlearning.
arXiv Detail & Related papers (2024-06-13T14:41:00Z) - Real-Time Evaluation in Online Continual Learning: A New Hope [104.53052316526546]
We evaluate current Continual Learning (CL) methods with respect to their computational costs.
A simple baseline outperforms state-of-the-art CL methods under this evaluation.
This surprisingly suggests that the majority of existing CL literature is tailored to a specific class of streams that is not practical.
arXiv Detail & Related papers (2023-02-02T12:21:10Z) - Algorithms that Approximate Data Removal: New Results and Limitations [2.6905021039717987]
We study the problem of deleting user data from machine learning models trained using empirical risk minimization.
We develop an online unlearning algorithm that is both computationally and memory efficient.
arXiv Detail & Related papers (2022-09-25T17:20:33Z) - A Boosting Approach to Reinforcement Learning [59.46285581748018]
We study efficient algorithms for reinforcement learning in decision processes whose complexity is independent of the number of states.
We give an efficient algorithm that is capable of improving the accuracy of such weak learning methods.
arXiv Detail & Related papers (2021-08-22T16:00:45Z) - Scalable Personalised Item Ranking through Parametric Density Estimation [53.44830012414444]
Learning from implicit feedback is challenging because of the difficult nature of the one-class problem.
Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem.
We propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart.
arXiv Detail & Related papers (2021-05-11T03:38:16Z) - Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate
in Gradient Descent [20.47598828422897]
We propose textit-Meta-Regularization, a novel approach for the adaptive choice of the learning rate in first-order descent methods.
Our approach modifies the objective function by adding a regularization term, and casts the joint process parameters.
arXiv Detail & Related papers (2021-04-12T13:13:34Z) - DEALIO: Data-Efficient Adversarial Learning for Imitation from
Observation [57.358212277226315]
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms.
This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk.
We propose a more data-efficient IfO algorithm
arXiv Detail & Related papers (2021-03-31T23:46:32Z) - Effective Proximal Methods for Non-convex Non-smooth Regularized
Learning [27.775096437736973]
We show that the independent sampling scheme tends to improve performance of the commonly-used uniform sampling scheme.
Our new analysis also derives a speed for the sampling than best one available so far.
arXiv Detail & Related papers (2020-09-14T16:41:32Z) - Meta-learning with Stochastic Linear Bandits [120.43000970418939]
We consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm, where the regularization is a square euclidean distance to a bias vector.
We show both theoretically and experimentally, that when the number of tasks grows and the variance of the task-distribution is small, our strategies have a significant advantage over learning the tasks in isolation.
arXiv Detail & Related papers (2020-05-18T08:41:39Z) - Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling
Methods [2.741266294612776]
This study uses stacked generalization, which is a two-step process of combining machine learning methods, called meta or super learners, for improving the performance of algorithms.
Building a test harness that accounts for all permutations of algorithm sample set pairs demonstrates that the complex, intrinsic data structures are all thoroughly tested.
arXiv Detail & Related papers (2020-04-03T20:38:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.