Domain generalization for sequential data via invariant subspace recovery

Sharma, Ashutosh

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https://hdl.handle.net/2142/129294 Copy

Description

Title

Domain generalization for sequential data via invariant subspace recovery

Author(s)

Sharma, Ashutosh

Issue Date

2025-05-05

Director of Research (if dissertation) or Advisor (if thesis)

Zhao, Han

Department of Study

Siebel School Comp & Data Sci

Discipline

Computer Science

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Domain Generalization
• Kalman Filter, Invariant Subspace Recovery, Invariant Features, Sequential Data

Language

eng

Abstract

Recent works have explored continuous and discrete temporal domain generalization, which given input data from multiple temporally indexed domains, aims to train a model generalized across time. State of the art systems like DRAIN [1] model the temporal evolution of the input domain and the model dynamics jointly for training optimal predictors, but these approaches cannot generalize to multiple categorically indexed domains with temporally evolving data. In another line of work, ISR [2] trains invariant predictors for a set of categorical domains assuming i.i.d sampled observed data points for recovering the invariant subspace, but this cannot be applied directly to sequentially sampled data. In this work, we propose leveraging subspace recovery techniques for training invariant predictors over temporally evolving data. We formalize the data generation model as a Dynamic Bayesian Network where latent representation at any time causally depend only on the current time’s label & last time’s latent variable. Assuming access to only the observations (and labels for training environments) for each environment, we first estimate the parameters of the model from training data via Expectation-Maximization. We then derive an optimal online predictor for the test environment which forms the strong baseline for our work. We then estimate invariant feature subspace from the latent variable distribution of the training data and project the observed features to this space. These invariant features are then used for generating invariant predictions. Similar to [3], we also propose a set of linear unit tests benchmark for this novel setting. Our experiments on the benchmark show that our strong baseline outperforms i.i.d. classifier by 17% and our invariant predictor further improves the accuracy by 3% over our strong baseline, validating the efficacy of our method.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129294

Copyright and License Information

Copyright 2025 Ashutosh Sharma

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