Ruqi Zhang
27 papers · 2016–2026 · 9 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (9) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (9) π£ Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(31)
π
Conference Polyglot
(9)
π
Triple Crown
π
Grand Slam
π₯
Mega-Team
(25)
π
Keyword Champion
(3)
π
Century Club
(26)
β‘
Prolific Year
(5)
ποΈ
Keyword Collector
(70)
π₯
Unstoppable
(7)
Conferences
ICLR (6)
ICML (5)
NIPS (5)
AISTATS (4)
AAAI (3)
ACL (1)
EMNLP (1)
ICCV (1)
IJCAI (1)
Top co-authors
Keywords
markov chain monte carlo
(9)
discrete sampling
(3)
gradient-based sampling
(3)
langevin dynamics
(3)
large language model
(3)
variational inference
(3)
discrete distribution
(3)
bayesian inference
(3)
energy-based model
(2)
uncertainty quantification
(2)
gibbs sampling
(2)
graphical model
(2)
bayesian sampling
(2)
metropolis-hastings algorithm
(2)
convergence guarantee
(2)
chain-of-thought reasoning
(1)
kl divergence
(1)
mathematical reasoning
(1)
logical reasoning
(1)
few-shot learning
(1)
Papers
Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
AAAI 2026
Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner
EMNLP 2025
ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time
ICLR 2025
Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization
ICML 2025
Optimal Stochastic Trace Estimation in Generative Modeling
AISTATS 2025
Controlled LLM Decoding via Discrete Auto-regressive Biasing
ICLR 2025
Adaptive Draft-Verification for Efficient Large Language Model Decoding
AAAI 2025
Scalable and Efficient Probabilistic Inference for Bayesian Deep Learning and Generative Modeling
AAAI 2025
CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought
ACL 2025
Entropy-MCMC: Sampling from Flat Basins with Ease
ICLR 2024
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
ICLR 2024
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
NIPS 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
Rethinking Data Distillation: Do Not Overlook Calibration
ICCV 2023
DISCS: A Benchmark for Discrete Sampling
NIPS 2023
Efficient Informed Proposals for Discrete Distributions via Newtonβs Series Approximation
AISTATS 2023
Calibrating the Rigged Lottery: Making All Tickets Reliable
ICLR 2023
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
ICML 2023
A Langevin-like Sampler for Discrete Distributions
ICML 2022
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
NIPS 2022
Low-Precision Stochastic Gradient Langevin Dynamics
ICML 2022
Meta-Learning Divergences for Variational Inference
AISTATS 2021
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
ICLR 2020
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
NIPS 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
AISTATS 2020
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
NIPS 2019
Large Scale Sparse Clustering
IJCAI 2016