Andrew Gordon Wilson
Professor of Computer Science and Data Science
Works on Bayesian deep learning, Gaussian processes and generalization in neural networks, with numerical linear algebra for scalable inference.
Andrew Gordon Wilson's lab at NYU works on probabilistic & causal inference, learning theory & optimization and efficient training & inference. 58 papers since 2023, 53 at top venues, mostly ICLR and ICML. 7 PhD students. 11 alumni, most often to Meta and Anthropic. Applications close .
At a glance
- papers, 2023–2026
- 15/ year
- at T1 venues
- 91%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 11
Papers by yearfrom DBLP, 2023–2026
- 9 papers2026 · T1 7 · collecting
- 13 papers2025 · T1 12
- 16 papers2024 · T1 15
- 20 papers2023 · T1 19
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, AISTATS, Findings of EMNLP
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- Efficient training & inference7 papers
- Probabilistic & causal inference6 papers
- bayesian deep1 paper
- numerical linear1 paper
Paper list
From DBLP.
20269 papers
- ICLR (T1)A Unification of Discrete, Gaussian, and Simplicial Diffusion (opens in a new tab)
- ICLR (T1)Diverse Dictionary Learning (opens in a new tab)
- ICLR (T1)Shrinking Proteins with Diffusion (opens in a new tab)
- Trans. Mach. Learn. Res.Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables (opens in a new tab)
- ICLR (T1)Understanding and Relaxing the Limitations of Transformers for Linear Algebra (opens in a new tab)
- ICLR (T1)Understanding the Implicit Biases of Design Choices for Time Series Foundation Models (opens in a new tab)
- ICLR (T1)Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility (opens in a new tab)
- Trans. Mach. Learn. Res.When Does Multimodality Lead to Better Time Series Forecasting? (opens in a new tab)
- ICLR (T1)Zero-shot Forecasting by Simulation Alone (opens in a new tab)
202513 papers
- ICLR (T1)Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences (opens in a new tab)
- ICLR (T1)Compute-Optimal LLMs Provably Generalize Better with Scale (opens in a new tab)
- ICML (T1)Customizing the Inductive Biases of Softmax Attention using Structured Matrices (opens in a new tab)
- ICML (T1)Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization (opens in a new tab)
- AISTATS (T1)Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable (opens in a new tab)
- NeurIPS (T1)Hyperparameter Transfer Enables Consistent Gains of Matrix-Preconditioned Optimizers Across Scales (opens in a new tab)
- ICML (T1)Position: Deep Learning is Not So Mysterious or Different (opens in a new tab)
- ICML (T1)Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection (opens in a new tab)
- Trans. Mach. Learn. Res.Reliable and Responsible Foundation Models (opens in a new tab)
- ICML (T1)Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks (opens in a new tab)
- NeurIPS (T1)Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation is Wasteful (opens in a new tab)
- ICML (T1)Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra (opens in a new tab)
202416 papers
- ICLR (T1)A Study of Bayesian Neural Network Surrogates for Bayesian Optimization (opens in a new tab)
- Trans. Mach. Learn. Res.Chronos: Learning the Language of Time Series (opens in a new tab)
- ICML (T1)Compute Better Spent: Replacing Dense Layers with Structured Matrices (opens in a new tab)
- ICML (T1)Controllable Prompt Tuning For Balancing Group Distributional Robustness (opens in a new tab)
- ICLR (T1)Fine-Tuned Language Models Generate Stable Inorganic Materials as Text (opens in a new tab)
- JMLR (T1)Fortuna: A Library for Uncertainty Quantification in Deep Learning (opens in a new tab)
- NeurIPS (T1)Large Language Models Must Be Taught to Know What They Don't Know (opens in a new tab)
- AISTATS (T1)Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors (opens in a new tab)
- ICML (T1)Modeling Caption Diversity in Contrastive Vision-Language Pretraining (opens in a new tab)
- ICML (T1)Non-Vacuous Generalization Bounds for Large Language Models (opens in a new tab)
- ICML (T1)Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI (opens in a new tab)
- ICML (T1)Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning (opens in a new tab)
202320 papers
- NeurIPS (T1)A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning (opens in a new tab)
- ICLR (T1)A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks (opens in a new tab)
- Findings of EMNLPAutomated Few-Shot Classification with Instruction-Finetuned Language Models (opens in a new tab)
- NeurIPS (T1)Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks (opens in a new tab)
- AISTATS (T1)Bayesian Optimization with Conformal Prediction Sets (opens in a new tab)
- NeurIPS (T1)CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra (opens in a new tab)
- ICML (T1)Function-Space Regularization in Neural Networks: A Probabilistic Perspective (opens in a new tab)
- ICLR (T1)How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization (opens in a new tab)
- NeurIPS (T1)Large Language Models Are Zero-Shot Time Series Forecasters (opens in a new tab)
- ICLR (T1)Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations (opens in a new tab)
- ICLR (T1)Learning Multimodal Data Augmentation in Feature Space (opens in a new tab)
- NeurIPS (T1)Protein Design with Guided Discrete Diffusion (opens in a new tab)
Where alumni went11 listed, 64% to industry
Industry 7Academia 4
Most often to
- Meta
- Anthropic
- OpenAI
Also to
- Columbia University
- NYU
- Purdue University
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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