Shengjia Zhao

25 papers · 2016–2026 · 7 conferences · across top CS/AI conferences

Achievements

+13 more ↓

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (6)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (8) 🤝 Dynamic Duo (23) 👑 Triple Crown 🏆 Grand Slam 🏆 Keyword Champion 💎 Century Club (24) 🚀 Conference Pioneer 📈 Trend Setter ⚡ Prolific Year (5) 🗃️ Keyword Collector (97) 🔥 Unstoppable (7)

Conferences

NIPS (7) ICML (5) AISTATS (4) ICLR (4) AAAI (2) UAI (2) COLT (1)

Top co-authors

Stefano Ermon (23) Jiaming Song (10) Yang Song (2) Hongyu Ren (2) Tengyu Ma (2) Aditya Grover (2) Chenlin Meng (2) Willie Neiswanger (2) Ashish Sabharwal (2) Roshni Sahoo (2)

Keywords

generative model (4) probability calibration (2) adaptive sampling (2) variance reduction (2) domain adaptation (2) representation learning (2) decision making (2) amortized inference (2) variational autoencoder (2) algorithmic fairness (1) transfer learning (1) variational inference (1) adversarial robustness (1) bayesian inference (1) neural decoding (1) imitation learning (1) sequential decision making (1) conformal prediction (1) multi-task learning (1) uncertainty quantification (1)

Papers

Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces AAAI 2026 Low-Degree Multicalibration COLT 2022 Generalizing Bayesian Optimization with Decision-theoretic Entropies NIPS 2022 Comparing Distributions by Measuring Differences that Affect Decision Making ICLR 2022 Modular Conformal Calibration ICML 2022 Local calibration: metrics and recalibration UAI 2022 Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration NIPS 2021 Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration AISTATS 2021 Improved Autoregressive Modeling with Distribution Smoothing ICLR 2021 Reliable Decisions with Threshold Calibration NIPS 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 Learning Controllable Fair Representations AISTATS 2019 Adaptive Hashing for Model Counting UAI 2019 Learning Neural PDE Solvers with Convergence Guarantees ICLR 2019 Adaptive Antithetic Sampling for Variance Reduction ICML 2019 InfoVAE: Balancing Learning and Inference in Variational Autoencoders AAAI 2019 Amortized Inference Regularization NIPS 2018 Bias and Generalization in Deep Generative Models: An Empirical Study NIPS 2018 Learning Hierarchical Features from Deep Generative Models ICML 2017 A-NICE-MC: Adversarial Training for MCMC NIPS 2017 Adaptive Concentration Inequalities for Sequential Decision Problems NIPS 2016