Andrew Jesson - RankMe

39 papers A* 13A 1Journal 22Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. ICLR Andrew Jesson, Nicolas Beltran-Velez, David M. Blei
2024	J	`jnl`	Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. CoRR Andrew Jesson, Nicolas Beltran-Velez, David M. Blei
2024	A*	`conf`	Estimating the Hallucination Rate of Generative AI. NeurIPS Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	Estimating the Hallucination Rate of Generative AI. CoRR Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	A*	`conf`	Hypothesis Testing the Circuit Hypothesis in LLMs. NeurIPS Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei
2024	J	`jnl`	Hypothesis Testing the Circuit Hypothesis in LLMs. CoRR Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei
2024	J	`jnl`	Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale. CoRR Andrew Jesson, Yiding Jiang
2024	A*	`conf`	ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. ICML Andrew Jesson, Chris Lu, Gunshi Gupta, Nicolas Beltran-Velez, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
2023	A*	`conf`	B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. ICML Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
2023	J	`jnl`	B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. CoRR Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
2023	J	`jnl`	BatchGFN: Generative Flow Networks for Batch Active Learning. CoRR Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
2023	A*	`conf`	Differentiable Multi-Target Causal Bayesian Experimental Design. ICML Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	J	`jnl`	Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	J	`jnl`	DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design. CoRR Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
2023	A*	`conf`	DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. ICML Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
2023	A	`conf`	Partial identification of dose responses with hidden confounders. UAI Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg
2023	J	`jnl`	ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. CoRR Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
2023	J	`jnl`	Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. Trans. Mach. Learn. Res. Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal
2023	J	`jnl`	Using uncertainty-aware machine learning models to study aerosol-cloud interactions. CoRR Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas
2022	A*	`conf`	GeneDisco: A Benchmark for Experimental Design in Drug Discovery. ICLR Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
2022	A*	`conf`	Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
2022	J	`jnl`	Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
2022	A*	`conf`	Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2022	J	`jnl`	Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2021	A*	`conf`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	GeneDisco: A Benchmark for Experimental Design in Drug Discovery. CoRR Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
2021	J	`jnl`	Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. CoRR Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal
2021	A*	`conf`	Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
2021	J	`jnl`	Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
2021	J	`jnl`	Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific. CoRR Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier
2020	J	`jnl`	Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
2020	A*	`conf`	Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
2019	—	`conf`	Task Adaptive Metric Space for Medium-Shot Medical Image Classification. MICCAI (1) Xiang Jiang, Liqiang Ding, Mohammad Havaei, Andrew Jesson, Stan Matwin
2018	J	`jnl`	Adversarially Learned Mixture Model. CoRR Andrew Jesson, Cécile Low-Kam, Florian Soudan, Nicolas Chapados
2018	J	`jnl`	CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance. CoRR Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
2018	J	`jnl`	On the Importance of Attention in Meta-Learning for Few-Shot Text Classification. CoRR Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin
2017	—	`conf`	Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss. BrainLes@MICCAI Andrew Jesson, Tal Arbel
2017	—	`conf`	CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance. MICCAI (3) Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados