Yingzhen Li

36 papers · 2015–2025 · 10 conferences · across top CS/AI conferences

Achievements

+14 more ↓

🌍 Conference Polyglot (10) 🏃 Academic Marathon (10) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🐝 Cross-Pollinator (9)

🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 🐣 Hot Topic Early Bird 👥 Mega-Team (25) 🔬 Deep Specialist (15) 👑 Triple Crown 🏆 Grand Slam 🗃️ Keyword Collector (101) ⚡ Prolific Year (6) 🚀 Conference Pioneer 💎 Century Club (36) 🔥 Unstoppable (11) 📈 Trend Setter ❓ The Questioner

Conferences

NIPS (11) ICML (10) ICLR (8) AAAI (1) ACL (1) AISTATS (1) EACL (1) EMNLP (1) IJCNLP (1) NAACL (1)

Top co-authors

José Miguel Hernández-Lobato (6) Ehsan Shareghi (5) Cheng Zhang (4) Zijing Ou (4) Carles Balsells-Rodas (3) Victor Prokhorov (3) Richard Turner (3) Wenbo Gong (3) Nigel Collier (3) Sam Devlin (2)

Keywords

variational inference (10) bayesian neural network (6) variational autoencoder (5) energy-based model (3) uncertainty quantification (3) expectation propagation (3) uncertainty estimation (2) probabilistic modeling (2) alpha divergence (2) approximate inference (2) continual learning (2) representation learning (2) latent representation (2) gaussian process (2) unsupervised learning (2) bayesian inference (2) markov chain monte carlo (2) sparse representation (2) generative model (2) sentence encoding (2)

Papers

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching ICLR 2025 Causal Discovery from Conditionally Stationary Time Series ICML 2025 C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion ICLR 2024 On the Identifiability of Switching Dynamical Systems ICML 2024 Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces NIPS 2024 Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024 Robust and Adaptive Deep Learning via Bayesian Principles AAAI 2023 Calibrating Transformers via Sparse Gaussian Processes ICLR 2023 Energy Discrepancies: A Score-Independent Loss for Energy-Based Models NIPS 2023 Markovian Gaussian Process Variational Autoencoders ICML 2023 ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure ICLR 2023 Learning Neural Set Functions Under the Optimal Subset Oracle NIPS 2022 Scalable Infomin Learning NIPS 2022 Repairing Neural Networks by Leaving the Right Past Behind NIPS 2022 Sparse Uncertainty Representation in Deep Learning with Inducing Weights NIPS 2021 Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders ACL 2021 Meta-Learning Divergences for Variational Inference AISTATS 2021 Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification EACL 2021 Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders IJCNLP 2021 Active Slices for Sliced Stein Discrepancy ICML 2021 Sliced Kernelized Stein Discrepancy ICLR 2021 On the Expressiveness of Approximate Inference in Bayesian Neural Networks NIPS 2020 A Causal View on Robustness of Neural Networks NIPS 2020 Bayesian Learning for Neural Dependency Parsing NAACL 2019 Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck NIPS 2019 On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation EMNLP 2019 Meta-Learning For Stochastic Gradient MCMC ICLR 2019 Are Generative Classifiers More Robust to Adversarial Attacks? ICML 2019 Variational Implicit Processes ICML 2019 Variational Continual Learning ICLR 2018 Gradient Estimators for Implicit Models ICLR 2018 Dropout Inference in Bayesian Neural Networks with Alpha-divergences ICML 2017 Deep Gaussian Processes for Regression using Approximate Expectation Propagation ICML 2016 Rényi Divergence Variational Inference NIPS 2016 Black-Box Alpha Divergence Minimization ICML 2016 Stochastic Expectation Propagation NIPS 2015