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Adrian Weller

98 papers · 2013–2025 · 14 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (28) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (14) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (23) 🀝 Dynamic Duo (15) πŸ‘‘ Triple Crown 🌱 Topic Pioneer 🧬 Topic Evolution πŸ† Grand Slam πŸ”¬ Deep Specialist (18) πŸ† Keyword Champion (7) πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ”₯ Unstoppable (13) ❓ The Questioner (3) πŸ’Ž Century Club (98) πŸ—ƒοΈ Keyword Collector (76) πŸ“ˆ Trend Setter

Conferences

NIPS (23) ICML (19) ICLR (16) AISTATS (12) AAAI (10) IJCAI (4) UAI (4) COLING (2) CVPR (2) EMNLP (2) ACL (1) ECCV (1) ICCV (1) JMLR (1)

Top co-authors

Weiyang Liu (15) Valerii Likhosherstov (14) Umang Bhatt (12) Bernhard SchΓΆlkopf (12) Krzysztof Choromanski (10) Tamas Sarlos (10) Mark Rowland (9) Zhen Liu (8) Krzysztof Marcin Choromanski (8) Krzysztof M Choromanski (7)

Research topics

Applications (1)

Keywords

graphical model (9) representation learning (9) kernel approximation (7) variational inference (5) partition function (5) map inference (5) algorithmic fairness (4) neural network (4) large language model (3) gaussian kernel (3) softmax kernel (3) approximate inference (3) feature attribution (3) stochastic gradient descent (3) reinforcement learning (3) combinatorial optimization (3) adversarial learning (3) uncertainty quantification (3) linear programming relaxation (3) shortcut learning (2)

Papers

Linear Transformer Topological Masking with Graph Random Features ICLR 2025
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention ICML 2025
Gridded Transformer Neural Processes for Spatio-Temporal Data ICML 2025
LLMs on interactive feature collections with implicit dynamic decision strategy COLING 2025
On Evaluating LLMs’ Capabilities as Functional Approximators: A Bayesian Evaluation Framework COLING 2025
Certification for Differentially Private Prediction in Gradient-Based Training ICML 2025
Orthogonal Finetuning Made Scalable EMNLP 2025
Learning Personalized Decision Support Policies AAAI 2025
Mitigating Shortcut Learning with InterpoLated Learning ACL 2025
Variance-Reducing Couplings for Random Features ICLR 2025
Can Large Language Models Understand Symbolic Graphics Programs? ICLR 2025
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning ICLR 2025
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers AISTATS 2024
Large Language Models Must Be Taught to Know What They Don’t Know NIPS 2024
Approximately Equivariant Neural Processes NIPS 2024
ALVIN: Active Learning Via INterpolation EMNLP 2024
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization ICLR 2024
Repelling Random Walks ICLR 2024
General Graph Random Features ICLR 2024
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models ICLR 2024
Confidential-DPproof: Confidential Proof of Differentially Private Training ICLR 2024
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees ICLR 2023
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap ICLR 2023
Approximating Full Conformal Prediction at Scale via Influence Functions AAAI 2023
Robust Explanation Constraints for Neural Networks ICLR 2023
Pairwise Similarity Learning is SimPLE ICCV 2023
Efficient Graph Field Integrators Meet Point Clouds ICML 2023
Is Learning Summary Statistics Necessary for Likelihood-free Inference? ICML 2023
On the informativeness of supervision signals UAI 2023
Mnemonist: Locating Model Parameters that Memorize Training Examples UAI 2023
Human-in-the-Loop Mixup UAI 2023
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel NIPS 2023
Diffused Redundancy in Pre-trained Representations NIPS 2023
Quasi-Monte Carlo Graph Random Features NIPS 2023
Use perturbations when learning from explanations NIPS 2023
Certification of Distributional Individual Fairness NIPS 2023
Learning to Receive Help: Intervention-Aware Concept Embedding Models NIPS 2023
Controlling Text-to-Image Diffusion by Orthogonal Finetuning NIPS 2023
Simplex Random Features ICML 2023
Iterative Teaching by Data Hallucination AISTATS 2023
Towards More Robust Interpretation via Local Gradient Alignment AAAI 2023
On the Expressive Flexibility of Self-Attention Matrices AAAI 2023
Do Invariances in Deep Neural Networks Align with Human Perception? AAAI 2023
Towards Robust Metrics for Concept Representation Evaluation AAAI 2023
Towards Principled Disentanglement for Domain Generalization CVPR 2022
Scalable Infomin Learning NIPS 2022
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off NIPS 2022
A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets NIPS 2022
Chefs' Random Tables: Non-Trigonometric Random Features NIPS 2022
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates AAAI 2022
On the Fairness of Causal Algorithmic Recourse AAAI 2022
CrossWalk: Fairness-Enhanced Node Representation Learning AAAI 2022
Structural Causal 3D Reconstruction ECCV 2022
Hybrid Random Features ICLR 2022
SphereFace2: Binary Classification is All You Need for Deep Face Recognition ICLR 2022
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers ICML 2022
Measuring Representational Robustness of Neural Networks Through Shared Invariances ICML 2022
On the Utility of Prediction Sets in Human-AI Teams IJCAI 2022
Iterative Teaching by Label Synthesis NIPS 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization ICML 2021
Getting a CLUE: A Method for Explaining Uncertainty Estimates ICLR 2021
Sub-Linear Memory: How to Make Performers SLiM NIPS 2021
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices AISTATS 2021
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch NIPS 2021
Learning with Hyperspherical Uniformity AISTATS 2021
Orthogonal Over-Parameterized Training CVPR 2021
Rethinking Attention with Performers ICLR 2021
Stochastic Flows and Geometric Optimization on the Orthogonal Group ICML 2020
Ode to an ODE NIPS 2020
Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks IJCAI 2020
Evaluating and Aggregating Feature-based Model Explanations IJCAI 2020
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding UAI 2019
Orthogonal Estimation of Wasserstein Distances AISTATS 2019
One-Network Adversarial Fairness AAAI 2019
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning ICML 2019
Unifying Orthogonal Monte Carlo Methods ICML 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NIPS 2019
Train and Test Tightness of LP Relaxations in Structured Prediction JMLR 2019
The Geometry of Random Features AISTATS 2018
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models ICML 2018
Gauged Mini-Bucket Elimination for Approximate Inference AISTATS 2018
Geometrically Coupled Monte Carlo Sampling NIPS 2018
Bucket Renormalization for Approximate Inference ICML 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization ICML 2018
Blind Justice: Fairness with Encrypted Sensitive Attributes ICML 2018
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings NIPS 2017
Uprooting and Rerooting Higher-Order Graphical Models NIPS 2017
Lost Relatives of the Gumbel Trick ICML 2017
Conditions beyond treewidth for tightness of higher-order LP relaxations AISTATS 2017
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning IJCAI 2017
From Parity to Preference-based Notions of Fairness in Classification NIPS 2017
Uprooting and Rerooting Graphical Models ICML 2016
Clamping Improves TRW and Mean Field Approximations AISTATS 2016
Tightness of LP Relaxations for Almost Balanced Models AISTATS 2016
Train and Test Tightness of LP Relaxations in Structured Prediction ICML 2016
Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs AISTATS 2015
Clamping Variables and Approximate Inference NIPS 2014
Bethe Bounds and Approximating the Global Optimum AISTATS 2013