Yarin Gal
Identifiers
- name variant Yarin Gal 0.60 · backfill
Papers (44)
- Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community cs.CL · 2026 · author #5
- Building Reliable Long-Form Generation via Hallucination Rejection Sampling cs.CL · 2026 · author #5
- The Neural Tangent Kernel for Classification cs.LG · 2026 · author #4
- Selective Safety Steering via Value-Filtered Decoding cs.LG · 2026 · author #4
- Muon is Not That Special: Random or Inverted Spectra Work Just as Well cs.LG · 2026 · author #8
- Training Transformers for KV Cache Compressibility cs.LG · 2026 · author #4
- Uncertainty Quantification for LLM Function-Calling cs.CL · 2026 · author #6
- Richer Bayesian Last Layers with Subsampled NTK Features cs.LG · 2026 · author #5
- AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents cs.LG · 2024 · author #13
- Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs cs.CL · 2024 · author #6
- The Curse of Recursion: Training on Generated Data Makes Models Forget cs.LG · 2023 · author #4
- Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation cs.CL · 2023 · author #2
- Generalizing from a few environments in safety-critical reinforcement learning cs.LG · 2019 · author #4
- Towards Inverse Reinforcement Learning for Limit Order Book Dynamics cs.LG · 2019 · author #5
- An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval astro-ph.EP · 2019 · author #7
- Differentially Private Continual Learning stat.ML · 2019 · author #2
- A Unifying Bayesian View of Continual Learning stat.ML · 2019 · author #2
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation cs.CV · 2018 · author #2
- On the Importance of Strong Baselines in Bayesian Deep Learning cs.LG · 2018 · author #3
- Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control cs.LG · 2018 · author #3
- Bayesian Deep Learning for Exoplanet Atmospheric Retrieval astro-ph.EP · 2018 · author #7
- Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam stat.ML · 2018 · author #5
- Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks stat.ML · 2018 · author #1
- Towards Robust Evaluations of Continual Learning stat.ML · 2018 · author #2
- Loss-Calibrated Approximate Inference in Bayesian Neural Networks stat.ML · 2018 · author #3
- Understanding Measures of Uncertainty for Adversarial Example Detection stat.ML · 2018 · author #2
- BRUNO: A Deep Recurrent Model for Exchangeable Data stat.ML · 2018 · author #4
- Vprop: Variational Inference using RMSprop stat.ML · 2017 · author #4
- Real Time Image Saliency for Black Box Classifiers stat.ML · 2017 · author #2
- Concrete Dropout stat.ML · 2017 · author #1
- Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics cs.CV · 2017 · author #2
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? cs.CV · 2017 · author #2
- Dropout Inference in Bayesian Neural Networks with Alpha-divergences cs.LG · 2017 · author #2
- Deep Bayesian Active Learning with Image Data cs.LG · 2017 · author #1
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks stat.ML · 2015 · author #1
- Dirichlet Fragmentation Processes stat.ML · 2015 · author #2
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference stat.ML · 2015 · author #1
- Dropout as a Bayesian Approximation: Appendix stat.ML · 2015 · author #1
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning stat.ML · 2015 · author #1
- Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs stat.ML · 2015 · author #1
- Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data stat.ML · 2015 · author #1
- Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature cs.CL · 2014 · author #1
- Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial stat.ML · 2014 · author #1
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models stat.ML · 2014 · author #1
Mentions
- 2606.10159 #5 · arxiv_oai · confidence 0.70 Yarin Gal
- 1509.04781 #2 · backfill · confidence 0.70 Yarin Gal
- 1506.02158 #1 · backfill · confidence 0.70 Yarin Gal
- 1506.02157 #1 · backfill · confidence 0.70 Yarin Gal
- 1506.02142 #1 · backfill · confidence 0.70 Yarin Gal
- 2606.03628 #5 · arxiv_oai · confidence 0.70 Yarin Gal
- 1503.02424 #1 · backfill · confidence 0.70 Yarin Gal
- 1503.02182 #1 · backfill · confidence 0.70 Yarin Gal
- 1402.7265 #1 · backfill · confidence 0.70 Yarin Gal
- 1402.1412 #1 · backfill · confidence 0.70 Yarin Gal
- 1402.1389 #1 · backfill · confidence 0.70 Yarin Gal
- 2602.01279 #5 · arxiv_oai · confidence 0.70 Yarin Gal
- 2305.17493 #4 · arxiv_oai · confidence 0.70 Yarin Gal
- 2605.17606 #4 · arxiv_oai · confidence 0.70 Yarin Gal
- 2406.15927 #6 · arxiv_oai · confidence 0.70 Yarin Gal
Frequent Coauthors
- Zoubin Ghahramani 7 shared papers
- Sebastian Farquhar 4 shared papers
- Adam D. Cobb 3 shared papers
- Alex Kendall 3 shared papers
- Mark van der Wilk 3 shared papers
- Angelos Filos 2 shared papers
- Daniel Angerhausen 2 shared papers
- Frank Soboczenski 2 shared papers
- Giada N. Arney 2 shared papers
- Jishnu Mukhoti 2 shared papers
- Jonathan Plenk 2 shared papers
- Kamil Ciosek 2 shared papers
- Lewis Smith 2 shared papers
- Lin Li 2 shared papers
- Michael D. Himes 2 shared papers
- Mohammad Emtiyaz Khan 2 shared papers
- Molly D. O'Beirne 2 shared papers
- Shawn D. Domagal-Goldman 2 shared papers
- Simone Zorzan 2 shared papers
- Voot Tangkaratt 2 shared papers