Yian Ma
29 papers · 2015–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (13) π Renaissance Researcher (5) π Interdisciplinary Bridge π Conference Polyglot (6)
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Conference Polyglot
(6)
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Academic Marathon
(10)
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Cross-Pollinator
(12)
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Triple Crown
π₯
Mega-Team
(40)
ποΈ
Keyword Collector
(84)
π₯
Unstoppable
(6)
β
The Questioner
π
Trend Setter
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Century Club
(29)
β‘
Prolific Year
(5)
Conferences
ICML (12)
NIPS (6)
AISTATS (4)
ICLR (4)
UAI (2)
JMLR (1)
Top co-authors
Keywords
markov chain monte carlo
(5)
variational inference
(4)
bayesian inference
(4)
langevin dynamics
(4)
stochastic gradient descent
(3)
regret bound
(3)
stochastic gradient
(2)
multi-armed bandit
(2)
thompson sampling
(2)
log-concave sampling
(2)
convergence analysis
(1)
optimal transport
(1)
uncertainty quantification
(1)
posterior sampling
(1)
domain generalization
(1)
causal discovery
(1)
kullback-leibler divergence
(1)
reinforcement learning
(1)
importance sampling
(1)
posterior distribution
(1)
Papers
Discovering Latent Causal Graphs from Spatiotemporal Data
ICML 2025
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
AISTATS 2025
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
AISTATS 2025
Learning to Steer Learners in Games
ICML 2025
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
ICLR 2025
ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models
ICLR 2025
Reverse Diffusion Monte Carlo
ICLR 2024
Demystifying SGD with Doubly Stochastic Gradients
ICML 2024
Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk
NIPS 2024
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
NIPS 2024
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
AISTATS 2024
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
AISTATS 2024
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
ICLR 2024
On Convergence of Federated Averaging Langevin Dynamics
UAI 2024
Discovering Mixtures of Structural Causal Models from Time Series Data
ICML 2024
Faster Sampling via Stochastic Gradient Proximal Sampler
ICML 2024
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
ICML 2024
Disentangled Multi-Fidelity Deep Bayesian Active Learning
ICML 2023
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
NIPS 2023
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
ICML 2023
On the Convergence of Black-Box Variational Inference
NIPS 2023
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
NIPS 2023
Underspecification Presents Challenges for Credibility in Modern Machine Learning
JMLR 2022
Variational refinement for importance sampling using the forward Kullback-Leibler divergence
UAI 2021
On Approximate Thompson Sampling with Langevin Algorithms
ICML 2020
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
ICML 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
ICML 2020
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
ICML 2018
A Complete Recipe for Stochastic Gradient MCMC
NIPS 2015