Tianlong Chen

Self-Damaging Contrastive Learning

2021 · arXiv (Cornell University)

The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear …

Bringing Your Own View

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and …

Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

2022 · arXiv (Cornell University)

Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs than standard training. In this …

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

2022 · TU/e Research Portal

Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this paper, we focus on sparse training and …

Accessing the topological properties of human brain functional sub-circuits in Echo State Networks

2024 · arXiv (Cornell University)

Recent years have witnessed an emerging trend in neuromorphic computing that centers around the use of brain connectomics as a blueprint for artificial neural networks. Connectomics-based neuromorphic computing has primarily focused on embedding human brain …

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

2025 · arXiv (Cornell University)

Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approaches lack formal guarantees for key metrics such as the false discovery rate (FDR) …

Symbolic analysis of Grover search algorithm via Chain-of-Thought reasoning and quantum-native tokenization

2026 · npj Quantum Information

Understanding the high-level conceptual structure of quantum algorithms from their low-level circuit representations is a critical task for verification, debugging, and education. While traditional numerical simulators can calculate output probabilities, they do not explicitly surface …

Graph Domain Adaptation via Theory-Grounded Spectral Regularization.

2023 · PubMed

, respectively. In a nut-shell, our study paves the way toward explicitly constructing and training GNNs that can capture more transferable representations across graph domains. Codes are released at https://github.com/Shen-Lab/GDA-SpecReg.

Graph Contrastive Learning with Augmentations

2020 · arXiv (Cornell University)

Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training …