v1v2 (latest)

Neural Design for Genetic Perturbation Experiments

26 July 2022

Papers citing "Neural Design for Genetic Perturbation Experiments"

29 / 29 papers shown

Title
Efficient Data Selection for Training Genomic Perturbation Models G. Panagopoulos J. Lutzeyer Sofiane Ennadir Michalis Vazirgiannis Jun Pang 480 0 0 18 Mar 2025
Neural Pseudo-Label Optimism for the Bank Loan Problem Aldo Pacchiano Shaun Singh Edward Chou Alexander C. Berg Jakob N. Foerster 36 7 0 03 Dec 2021
GeneDisco: A Benchmark for Experimental Design in Drug Discovery Arash Mehrjou Ashkan Soleymani Andrew Jesson Pascal Notin Y. Gal Stefan Bauer Patrick Schwab 78 21 0 22 Oct 2021
Learning Neural Causal Models with Active Interventions Nino Scherrer O. Bilaniuk Yashas Annadani Anirudh Goyal Patrick Schwab Bernhard Schölkopf Michael C. Mozer Yoshua Bengio Stefan Bauer Nan Rosemary Ke CML 98 44 0 06 Sep 2021
Matching a Desired Causal State via Shift Interventions Jiaqi Zhang C. Squires Caroline Uhler 82 16 0 05 Jul 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings Jordan T. Ash Surbhi Goel A. Krishnamurthy Sham Kakade 64 88 0 17 Jun 2021
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning Scott Sussex Andreas Krause Caroline Uhler CML 62 20 0 28 May 2021
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL Aldo Pacchiano Christoph Dann Claudio Gentile Peter L. Bartlett 75 49 0 24 Dec 2020
Neural Contextual Bandits with Deep Representation and Shallow Exploration Pan Xu Zheng Wen Handong Zhao Quanquan Gu OffRL 78 78 0 03 Dec 2020
Online Model Selection for Reinforcement Learning with Function Approximation Jonathan Lee Aldo Pacchiano Vidya Muthukumar Weihao Kong Emma Brunskill OffRL 50 37 0 19 Nov 2020
Quantile Bandits for Best Arms Identification Mengyan Zhang Cheng Soon Ong 31 13 0 22 Oct 2020
Towards Tractable Optimism in Model-Based Reinforcement Learning Aldo Pacchiano Philip J. Ball Jack Parker-Holder K. Choromanski Stephen J. Roberts OffRL 41 12 0 21 Jun 2020
Model Selection in Contextual Stochastic Bandit Problems Aldo Pacchiano My Phan Yasin Abbasi-Yadkori Anup B. Rao Julian Zimmert Tor Lattimore Csaba Szepesvári 178 94 0 03 Mar 2020
Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder Aldo Pacchiano K. Choromanski Stephen J. Roberts 104 164 0 03 Feb 2020
Discriminative Active Learning Daniel Gissin Shai Shalev-Shwartz 58 178 0 15 Jul 2019
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch Joost R. van Amersfoort Y. Gal FedML 87 629 0 19 Jun 2019
Batch Active Learning Using Determinantal Point Processes Erdem Biyik Kenneth Wang Nima Anari Dorsa Sadigh 71 62 0 19 Jun 2019
Worst-Case Regret Bounds for Exploration via Randomized Value Functions Daniel Russo OffRL 48 88 0 07 Jun 2019
Adversarial Active Learning for Deep Networks: a Margin Based Approach Mélanie Ducoffe F. Precioso GAN AAML 138 276 0 27 Feb 2018
Deep Active Learning over the Long Tail Yonatan Geifman Ran El-Yaniv 3DPC 65 143 0 02 Nov 2017
Budgeted Experiment Design for Causal Structure Learning AmirEmad Ghassami Saber Salehkaleybar Negar Kiyavash Elias Bareinboim CML 92 64 0 11 Sep 2017
Faster Greedy MAP Inference for Determinantal Point Processes Insu Han P. Kambadur KyoungSoo Park Jinwoo Shin 49 25 0 09 Mar 2017
Corralling a Band of Bandit Algorithms Alekh Agarwal Haipeng Luo Behnam Neyshabur Robert Schapire 149 157 0 19 Dec 2016
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes Tarun Kathuria Amit Deshpande Pushmeet Kohli GP 52 103 0 13 Nov 2016
Learning Optimal Interventions Jonas W. Mueller David N. Reshef George Du Tommi Jaakkola 31 9 0 16 Jun 2016
Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek Oren Rippel Kevin Swersky Ryan Kiros N. Satish N. Sundaram Md. Mostofa Ali Patwary P. Prabhat Ryan P. Adams BDL UQCV 95 1,045 0 19 Feb 2015
Determinantal point processes for machine learning Alex Kulesza B. Taskar 252 1,140 0 25 Jul 2012
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization Thomas Desautels Andreas Krause J. W. Burdick 104 473 0 27 Jun 2012
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design Niranjan Srinivas Andreas Krause Sham Kakade Matthias Seeger 152 1,624 0 21 Dec 2009