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1. Non-IID Transfer Learning on Graphs

   https://doi.org/10.1609/AAAI.V37I9.26231  

   Wu, Jun; He, Jingrui; Ainsworth, Elizabeth (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks. 

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2. Distributionally Robust Submodular Maximization

   Staib, M.; Wilder, B.; Jegelka, S. (April 2019, Proceedings of the International Workshop on Artificial Intelligence and Statistics)

   ubmodular functions have applications throughout machine learning, but in many settings, we do not have direct access to the underlying function f . We focus on stochastic functions that are given as an expectation of functions over a distribution P. In practice, we often have only a limited set of samples fi from P . The standard approach indirectly optimizes f by maximizing the sum of fi. However, this ignores generalization to the true (unknown) distribution. In this paper, we achieve better performance on the actual underlying function f by directly optimizing a combination of bias and variance. Algorith- mically, we accomplish this by showing how to carry out distributionally robust optimiza- tion (DRO) for submodular functions, pro- viding efficient algorithms backed by theoret- ical guarantees which leverage several novel contributions to the general theory of DRO. We also show compelling empirical evidence that DRO improves generalization to the un- known stochastic submodular function. 

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3. The Price of Implicit Bias in Adversarially Robust Generalization

   Tsilivis, Nikolaos; Frank, Natalie; Srebro, Nathan; Kempe, Julia (April 2025, The International Conference on Learning Representations (ICLR 2025))

   We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization. In classification settings under adversarial perturbations with linear models, we study what type of regularization should ideally be applied for a given perturbation set to improve (robust) generalization. We then show that the implicit bias of optimization in robust ERM can significantly affect the robustness of the model and identify two ways this can happen; either through the optimization algorithm or the architecture. We verify our predictions in simulations with synthetic data and experimentally study the importance of implicit bias in robust ERM with deep neural networks. 

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4. POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition

   Saito, Yuta; Yao, Yihan; Joachims, Thorsten (June 2025, International Conference on Representation Learning (ICLR))

   We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods – most of which rely crucially on reward-regression models or importance-weighted policy gradients – fail due to excessive bias or variance. To overcome these issues in OPL, we propose a novel two-stage algorithm, called Policy Optimization via Two-Stage Policy Decomposition (POTEC). It leverages clustering in the action space and learns two different policies via policy- and regression-based approaches, respectively. In particular, we derive a novel low-variance gradient estimator that enables to learn a first-stage policy for cluster selection efficiently via a policy-based approach. To select a specific action within the cluster sampled by the first-stage policy, POTEC uses a second-stage policy derived from a regression-based approach within each cluster. We show that a local correctness condition, which only requires that the regression model preserves the relative expected reward differences of the actions within each cluster, ensures that our policy-gradient estimator is unbiased and the second-stage policy is optimal. We also show that POTEC provides a strict generalization of policyand regression-based approaches and their associated assumptions. Comprehensive experiments demonstrate that POTEC provides substantial improvements in OPL effectiveness particularly in large and structured action spaces. 

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5. Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation

   https://doi.org/10.1109/IEEECONF51394.2020.9443420  

   Choudhuri, Sandipan; Paul, Riti; Sen, Arunabha; Li, Baoxin; Venkateswara, Hemanth (January 2020, 2020 54th Asilomar Conference on Signals, Systems, and Computers)

   Matthews, MB (Ed.)

   The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most realworld scenarios where data collection and their annotation involve huge cost. Various domain adaptation techniques exist in literature that bridge this distribution discrepancy. However, a majority of these models require the label sets of both the domains to be identical. To tackle a more practical and challenging scenario, we formulate the problem statement from a partial domain adaptation perspective, where the source label set is a super set of the target label set. Driven by the motivation that image styles are private to each domain, in this work, we develop a method that identifies outlier classes exclusively from image content information and train a label classifier exclusively on class-content from source images. Additionally, elimination of negative transfer of samples from classes private to the source domain is achieved by transforming the soft class-level weights into two clusters, 0 (outlier source classes) and 1 (shared classes) by maximizing the between-cluster variance between them. 

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