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Yingzhen Li

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

Achievements

🌍 Conference Polyglot (10) πŸƒ Academic Marathon (10) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (9) πŸ—ΊοΈ Taxonomy Completionist (41) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 🐣 Hot Topic Early Bird πŸ‘₯ Mega-Team (25) πŸ”¬ Deep Specialist (15) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (101) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ’Ž Century Club (36) πŸ”₯ Unstoppable (11) πŸ“ˆ 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

JosΓ© Miguel HernΓ‘ndez-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
RΓ©nyi Divergence Variational Inference NIPS 2016
Black-Box Alpha Divergence Minimization ICML 2016
Stochastic Expectation Propagation NIPS 2015