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Takashi Ishida

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3 papers

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TMLR Journal 2025 Journal Article

Importance Weighting for Aligning Language Models under Deployment Distribution Shift

Thanawat Lodkaew
Tongtong Fang
Takashi Ishida
Masashi Sugiyama

Aligning language models (LMs) with human preferences remains challenging partly because popular approaches, such as reinforcement learning from human feedback and direct preference optimization (DPO), often assume that the training data is sufficiently representative of the environment in which the model will be deployed. However, real-world applications frequently involve distribution shifts, e.g., changes in end-user behavior or preferences during usage or deployment, which pose a significant challenge to LM alignment approaches. In this paper, we propose an importance weighting method tailored for DPO, namely IW-DPO, to address distribution shifts in LM alignment. IW-DPO can be applied to joint distribution shifts in the prompts, responses, and preference labels without explicitly assuming the type of distribution shift. Our experimental results on various distribution shift scenarios demonstrate the usefulness of IW-DPO.

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NeurIPS Conference 2018 Conference Paper

Binary Classification from Positive-Confidence Data

Takashi Ishida
Gang Niu
Masashi Sugiyama

Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confidence), one can successfully learn a binary classifier, which we name positive-confidence (Pconf) classification. Our work is related to one-class classification which is aimed at "describing" the positive class by clustering-related methods, but one-class classification does not have the ability to tune hyper-parameters and their aim is not on "discriminating" positive and negative classes. For the Pconf classification problem, we provide a simple empirical risk minimization framework that is model-independent and optimization-independent. We theoretically establish the consistency and an estimation error bound, and demonstrate the usefulness of the proposed method for training deep neural networks through experiments.

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NeurIPS Conference 2017 Conference Paper

Learning from Complementary Labels

Takashi Ishida
Gang Niu
Weihua Hu
Masashi Sugiyama

Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification. A complementary label specifies a class that a pattern does not belong to. Collecting complementary labels would be less laborious than collecting ordinary labels, since users do not have to carefully choose the correct class from a long list of candidate classes. However, complementary labels are less informative than ordinary labels and thus a suitable approach is needed to better learn from them. In this paper, we show that an unbiased estimator to the classification risk can be obtained only from complementarily labeled data, if a loss function satisfies a particular symmetric condition. We derive estimation error bounds for the proposed method and prove that the optimal parametric convergence rate is achieved. We further show that learning from complementary labels can be easily combined with learning from ordinary labels (i. e. , ordinary supervised learning), providing a highly practical implementation of the proposed method. Finally, we experimentally demonstrate the usefulness of the proposed methods.

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