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Researcher profile

Mohan Kankanhalli

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Learning to Predict Trustworthiness with Steep Slope Loss

    2021 · arXiv (Cornell University)

    Understanding the trustworthiness of a prediction yielded by a classifier is critical for the safe and effective use of AI models. Prior efforts have been proven to be reliable on small-scale datasets. In this work, …

  2. Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation

    2023 · arXiv (Cornell University)

    Graph unlearning has emerged as a pivotal method to delete information from a pre-trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges. An unlearning method …

  3. Group $K$-Means

    2015 · arXiv (Cornell University)

    We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary. Although theoretically low approximation errors can be achieved …

  4. Reasoning LLMs are Wandering Solution Explorers

    2025 · arXiv (Cornell University)

    Large Language Models (LLMs) have demonstrated impressive reasoning abilities through test-time computation (TTC) techniques such as chain-of-thought prompting and tree-based reasoning. However, we argue that current reasoning LLMs (RLLMs) lack the ability to systematically explore …

  5. Distill to Delete: Unlearning in Graph Networks With Knowledge Distillation

    2025 · IEEE Transactions on Neural Networks and Learning Systems

    Graph unlearning has emerged as a pivotal method to delete information from an already trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges. An unlearning …

  6. Aspect-Aware Latent Factor Model

    2018

    Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual …

  7. A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

    2018

    Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems …