conftrace_

Yujia Zheng

26 papers · 2021–2026 · 10 conferences · across top CS/AI conferences

Achievements

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+11 more ↓ 🌍 Conference Polyglot (8) 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸƒ Academic Marathon (5)
πŸ—ΊοΈ Taxonomy Completionist (27) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (8) 🀝 Dynamic Duo (22) πŸ‘‘ Triple Crown πŸ† Grand Slam ❓ The Questioner πŸ”₯ Unstoppable (5) πŸ’Ž Century Club (24) πŸ—ƒοΈ Keyword Collector (68) ⚑ Prolific Year (10)

Conferences

ICML (6) NIPS (6) ICLR (5) AISTATS (3) AAAI (1) ACL (1) CVPR (1) EACL (1) EMNLP (1) JMLR (1)

Papers

Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders ACL 2026 Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs EACL 2026 Causal Representation Learning from Multimodal Biomedical Observations ICLR 2025 Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning ICLR 2025 Learning Vision and Language Concepts for Controllable Image Generation ICML 2025 Nonparametric Identification of Latent Concepts ICML 2025 Nonparametric Factor Analysis and Beyond AISTATS 2025 Type Information-Assisted Self-Supervised Knowledge Graph Denoising AISTATS 2025 SmartCLIP: Modular Vision-language Alignment with Identification Guarantees CVPR 2025 A General Representation-Based Approach to Multi-Source Domain Adaptation ICML 2025 Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems EMNLP 2025 Identification of Intermittent Temporal Latent Process ICLR 2025 A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables ICLR 2024 Identifying Selections for Unsupervised Subtask Discovery NIPS 2024 Causal Temporal Representation Learning with Nonstationary Sparse Transition NIPS 2024 Local Causal Discovery with Linear non-Gaussian Cyclic Models AISTATS 2024 Causal Representation Learning from Multiple Distributions: A General Setting ICML 2024 Detecting and Identifying Selection Structure in Sequential Data ICML 2024 Causal-learn: Causal Discovery in Python JMLR 2024 On the Identifiability of Sparse ICA without Assuming Non-Gaussianity NIPS 2023 Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks ICLR 2023 Generalizing Nonlinear ICA Beyond Structural Sparsity NIPS 2023 On the Identifiability of Nonlinear ICA: Sparsity and Beyond NIPS 2022 Partial disentanglement for domain adaptation ICML 2022 Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions NIPS 2021 Cold-start Sequential Recommendation via Meta Learner AAAI 2021