Shengjia Zhao
25 papers · 2016–2026 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (53) π Conference Polyglot Β· Silver π Academic Marathon Β· Half π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Triple Crown
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Keyword Champion
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Dynamic Duo
(23)
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Prolific Year Β· Productive
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Unstoppable Β· Bronze
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Century Club Β· Bronze
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Conference Pioneer
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Trend Setter
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Keyword Collector
(118)
Top co-authors
Research topics
Machine Learning
(23)
Optimization & Theory
(16)
Core Methods
(11)
Artificial Intelligence
(9)
Deep Learning
(9)
Core AI
(8)
Application Areas
(7)
Learning Types Β· Machine Learning
(7)
Models
(7)
Mathematics & Optimization
(5)
Bayesian & Probabilistic
(4)
Learning Types Β· Deep Learning
(4)
Optimization
(4)
Architectures
(2)
Mathematics
(2)
Keywords
generative model
(4)
representation learning
(3)
adaptive sampling
(2)
variational autoencoder
(2)
decision loss
(2)
decision making
(2)
multi-class classification
(2)
sample complexity
(2)
amortized inference
(2)
variance reduction
(2)
neural network
(2)
probability calibration
(2)
domain adaptation
(2)
image generation
(2)
bayesian inference
(1)
algorithmic fairness
(1)
transfer learning
(1)
variational inference
(1)
adversarial robustness
(1)
neural decoding
(1)
Papers
Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces
AAAI 2026
Comparing Distributions by Measuring Differences that Affect Decision Making
ICLR 2022
Generalizing Bayesian Optimization with Decision-theoretic Entropies
NeurIPS 2022
Local calibration: metrics and recalibration
UAI 2022
Low-Degree Multicalibration
COLT 2022
Modular Conformal Calibration
ICML 2022
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
NeurIPS 2021
Improved Autoregressive Modeling with Distribution Smoothing
ICLR 2021
Reliable Decisions with Threshold Calibration
NeurIPS 2021
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
AISTATS 2021
A Framework for Sample Efficient Interval Estimation with Control Variates
AISTATS 2020
A Theory of Usable Information under Computational Constraints
ICLR 2020
Domain Adaptive Imitation Learning
ICML 2020
Individual Calibration with Randomized Forecasting
ICML 2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling
AISTATS 2020
Adaptive Antithetic Sampling for Variance Reduction
ICML 2019
Adaptive Hashing for Model Counting
UAI 2019
InfoVAE: Balancing Learning and Inference in Variational Autoencoders
AAAI 2019
Learning Controllable Fair Representations
AISTATS 2019
Learning Neural PDE Solvers with Convergence Guarantees
ICLR 2019
Amortized Inference Regularization
NeurIPS 2018
Bias and Generalization in Deep Generative Models: An Empirical Study
NeurIPS 2018
A-NICE-MC: Adversarial Training for MCMC
NeurIPS 2017
Learning Hierarchical Features from Deep Generative Models
ICML 2017
Adaptive Concentration Inequalities for Sequential Decision Problems
NeurIPS 2016