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Che-Ping Tsai

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6 papers

2 author rows

ICLR Conference 2025 Conference Paper

AnoLLM: Large Language Models for Tabular Anomaly Detection

Che-Ping Tsai
Ganyu Teng
Phillip Wallis
Wei Ding

We introduce AnoLLM, a novel framework that leverages large language models (LLMs) for unsupervised tabular anomaly detection. By converting tabular data into a standardized text format, we further adapt a pre-trained LLM with this serialized data, and assign anomaly scores based on the negative log likelihood generated by the LLM. Unlike traditional methods that can require extensive feature engineering, and often lose textual information during data processing, AnoLLM preserves data integrity and streamlines the preprocessing required for tabular anomaly detection. This approach can effectively handle mixed-type data, especially those containing textual features. Our empirical results indicate that AnoLLM delivers the best performance on six benchmark datasets with mixed feature types. Additionally, across 30 datasets from the ODDS library, which are predominantly numerical, AnoLLM performs on par with top performing baselines.

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ICML Conference 2025 Conference Paper

Contextures: Representations from Contexts

Runtian Zhai
Kai Yang
Burak Varici
Che-Ping Tsai
J. Zico Kolter
Pradeep Ravikumar

Despite the empirical success of foundation models, we do not have a systematic characterization of the representations that these models learn. In this paper, we establish the contexture theory. It shows that a large class of representation learning methods can be characterized as learning from the association between the input and a context variable. Specifically, we show that many popular methods aim to approximate the top-d singular functions of the expectation operator induced by the context, in which case we say that the representation learns the contexture. We demonstrate the generality of the contexture theory by proving that representation learning within various learning paradigms – supervised, self-supervised, and manifold learning – can all be studied from such a perspective. We prove that representations that learn the contexture are optimal on those tasks that are compatible with the context. One important implication of our theory is that once the model is large enough to approximate the top singular functions, scaling up the model size yields diminishing returns, so further improvement requires better contexts. To this end, we study how to evaluate a context without knowing the downstream tasks. We propose a metric and show by experiments that it correlates well with the actual performance of the encoder on many real datasets.

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JMLR Journal 2023 Journal Article

Faith-Shap: The Faithful Shapley Interaction Index

Che-Ping Tsai
Chih-Kuan Yeh
Pradeep Ravikumar

Shapley values, which were originally designed to assign attributions to individual players in coalition games, have become a commonly used approach in explainable machine learning to provide attributions to input features for black-box machine learning models. A key attraction of Shapley values is that they uniquely satisfy a very natural set of axiomatic properties. However, extending the Shapley value to assigning attributions to interactions rather than individual players, an interaction index, is non-trivial: as the natural set of axioms for the original Shapley values, extended to the context of interactions, no longer specify a unique interaction index. Many proposals thus introduce additional possibly stringent axioms, while sacrificing the key axiom of efficiency, in order to obtain unique interaction indices. In this work, rather than introduce additional conflicting axioms, we adopt the viewpoint of Shapley values as coefficients of the most faithful linear approximation to the pseudo-Boolean coalition game value function. By extending linear to higher order polynomial approximations, we can then define the general family of faithful interaction indices. We show that by additionally requiring the faithful interaction indices to satisfy interaction-extensions of the standard individual Shapley axioms (dummy, symmetry, linearity, and efficiency), we obtain a unique Faithful Shapley Interaction index, which we denote Faith-Shap, as a natural generalization of the Shapley value to interactions. We then provide some illustrative contrasts of Faith-Shap with previously proposed interaction indices, and further investigate some of its interesting algebraic properties. We further show the computational efficiency of computing Faith-Shap, together with some additional qualitative insights, via some illustrative experiments. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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ICML Conference 2023 Conference Paper

Representer Point Selection for Explaining Regularized High-dimensional Models

Che-Ping Tsai
Jiong Zhang 0001
Hsiang-Fu Yu
Eli Chien
Cho-Jui Hsieh
Pradeep Ravikumar

We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples. Our workhorse is a novel representer theorem for general regularized high-dimensional models, which decomposes the model prediction in terms of contributions from each of the training samples: with positive (negative) values corresponding to positive (negative) impact training samples to the model’s prediction. We derive consequences for the canonical instances of $\ell_1$ regularized sparse models and nuclear norm regularized low-rank models. As a case study, we further investigate the application of low-rank models in the context of collaborative filtering, where we instantiate high-dimensional representers for specific popular classes of models. Finally, we study the empirical performance of our proposed methods on three real-world binary classification datasets and two recommender system datasets. We also showcase the utility of high-dimensional representers in explaining model recommendations.

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NeurIPS Conference 2023 Conference Paper

Sample based Explanations via Generalized Representers

Che-Ping Tsai
Chih-Kuan Yeh
Pradeep Ravikumar

We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel. A key contribution of the paper is to show that generalized representers are the only class of sample based explanations satisfying a natural set of axiomatic properties. We discuss approaches to extract global importances given a kernel, and also natural choices of kernels given modern non-linear models. As we show, many popular existing sample based explanations could be cast as generalized representers with particular choices of kernels and approaches to extract global importances. Additionally, we conduct empirical comparisons of different generalized representers on two image classification datasets.

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AAAI Conference 2020 Conference Paper

Order-Free Learning Alleviating Exposure Bias in Multi-Label Classification

Che-Ping Tsai
Hung-yi Lee

Multi-label classiﬁcation (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predeﬁned order of labels, which is not directly available in the MLC speciﬁcation. Besides, RNN thus trained tends to overﬁt the label combinations in the training set and have difﬁculty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predeﬁned label order and thus alleviates exposure bias. The experimental results on three multi-label classiﬁcation benchmark datasets show that our method outperforms competitive baselines by a large margin. We also ﬁnd the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability.

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