Pushmeet Kohli
Pushmeet Kohli is a principal scientist and research team leader at DeepMind. Before joining DeepMind, Pushmeet was the director of research at the Cognition group at Microsoft. During his 10 years at Microsoft, Pushmeet worked in Microsoft labs in Seattle, Cambridge and Bangalore and took a number of roles and duties including being technical advisor to Rick Rashid, the Chief Research Officer of Microsoft.
Pushmeet’s research revolves around Intelligent Systems and Computational Sciences, and he publishes in the fields of Machine Learning, Computer Vision, Information Retrieval, and Game Theory. His current research interests include 3D Reconstruction and Rendering, Probabilistic Programming, Interpretable and Verifiable Knowledge Representations from Deep Models. He is also interested in Conversation agents for Task completion, Machine learning systems for Healthcare and 3D rendering and interaction for augmented and virtual reality.
Pushmeet has won a number of awards and prizes for his research. His PhD thesis, titled "Minimizing Dynamic and Higher Order Energy Functions using Graph Cuts", was the winner of the British Machine Vision Association’s “Sullivan Doctoral Thesis Award”, and was a runner-up for the British Computer Society's “Distinguished Dissertation Award”. Pushmeet’s papers have appeared in Computer Vision (ICCV, CVPR, ECCV, PAMI, IJCV, CVIU, BMVC, DAGM), Machine Learning, Robotics and AI (NIPS, ICML, AISTATS, AAAI, AAMAS, UAI, ISMAR), Computer Graphics (SIGGRAPH, Eurographics), and HCI (CHI, UIST) conferences. They have won awards in ICVGIP 2006, 2010, ECCV 2010, ISMAR 2011, TVX 2014, CHI 2014, WWW 2014 and CVPR 2015. His research has also been the subject of a number of articles in popular media outlets such as Forbes, Wired, BBC, New Scientist and MIT Technology Review. Pushmeet is a part of the Association for Computing Machinery's (ACM) Distinguished Speaker Program.
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Enhancing diagnostic accuracy of medical AI systems via selective deferral to clinicians
Dj Dvijotham
Melih Barsbey
Sumedh Ghaisas
Robert Stanforth
Nick Pawlowski
Patricia Strachan
Zahra Ahmed
Yoram Bachrach
Laura Culp
Mayank Daswani
Jan Freyberg
Atilla Kiraly
Timo Kohlberger
Scott Mayer McKinney
Basil Mustafa
Krzysztof Geras
Jan Witowski
Zhi Zhen Qin
Jacob Creswell
Shravya Shetty
Terry Spitz
Taylan Cemgil
Nature Medicine (2023)
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AI systems trained using deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings1,2. While these results are impressive, they don’t accurately reflect the impact of deployment of such systems in a clinical context. Due to the safety-critical nature of this domain and the fact that AI systems are not perfect and can make inaccurate assessments, they are predominantly deployed as assistive tools for clinical experts3. Although clinicians routinely discuss the diagnostic nuances of medical images with each other, weighing human diagnostic confidence against that of an AI system remains a major unsolved barrier to collaborative decision-making4. Furthermore, it has been observed that diagnostic AI models have complementary strengths and weaknesses compared to clinical experts. Yet, complementarity and the assessment of relative confidence between the members of a diagnostic team has remained largely unexploited in how AI systems are currently used in medical settings5.
In this paper, we study the behavior of a team composed of diagnostic AI model(s) and clinician(s) in diagnosing disease. To go beyond the performance level of a standalone AI system, we develop a novel selective deferral algorithm that can learn to decide when to rely on a diagnostic AI model and when to defer to a clinical expert. Using this algorithm, we demonstrate that the composite AI+human system has enhanced accuracy (both sensitivity and specificity) relative to a human-only or an AI-only baseline. We decouple the development of the deferral AI model from training of the underlying diagnostic AI model(s). Development of the deferral AI model only requires i) the predictions of a model(s) on a tuning set of medical images (separate from the diagnostic AI models’ training data), ii) the diagnoses made by clinicians on these images and iii) the ground truth disease labels corresponding to those images.
Our extensive analysis shows that the selective deferral (SD) system exceeds the performance of either clinicians or AI alone in multiple clinical settings: breast and lung cancer screening. For breast cancer screening, double-reading with arbitration (two readers interpreting each mammogram invoking an arbitrator if needed) is a “gold standard” for performance, never previously exceeded using AI6. The SD system exceeds the accuracy of double-reading with arbitration in a large representative UK screening program (25% reduction in false positives despite equivalent true-positive detection and 66% reduction in the requirement for clinicians to read an image), as well as exceeding the performance of a standalone state-of-art AI system (40% reduction in false positives with equivalent detection of true positives). In a large US dataset the SD system exceeds the accuracy of single-reading by board-certified radiologists and a standalone state-of-art AI system (32% reduction in false positives despite equivalent detection of true positives and 55% reduction in the clinician workload required). The SD system further outperforms both clinical experts alone, and AI alone for the detection of lung cancer in low-dose Computed Tomography images from a large national screening study, with 11% reduction in false positives while maintaining sensitivity given 93% reduction in clinician workload required. Furthermore, the SD system allows controllable trade-offs between sensitivity and specificity and can be tuned to target either specificity or sensitivity as desired for a particular clinical application, or a combination of both.
The system generalizes to multiple distribution shifts, retaining superiority to both the AI system alone and human experts alone. We demonstrate that the SD system retains performance gains even on clinicians not present in the training data for the deferral AI. Furthermore, we test the SD system on a new population where the standalone AI system’s performance significantly degrades. We showcase the few-shot adaptation capability of the SD system by demonstrating that the SD system can obtain superiority to both the standalone AI system and the clinician on the new population after being trained on only 40 cases from the new population.
Our comprehensive assessment demonstrates that a selective deferral system could significantly improve clinical outcomes in multiple medical imaging applications, paving the way for higher performance clinical AI systems that can leverage the complementarity between clinical experts and medical AI tools.
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Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
Aditya Paliwal
Felix Gimeno
Vinod Gopal Nair
Yujia Li
Miles Lubin
International Conference on Learning Representations (ICLR) (2020)
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We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.
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Learning Transferable Graph Exploration
Yujia Li
Chenglong Wang
Rishabh Singh
Po-Sen Huang
Neural Processing Information Systems (NeurIPS) (2019)
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This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a distribution of environments. At test time, presented with an unseen environment from the same distribution, the policy aims to generalize the exploration strategy to visit the maximum number of unique states in a limited number of steps. We particularly focus on environments with graph-structured state-spaces that are encountered in many important real-world applications like software testing and map building. We formulate this task as a reinforcement learning problem where the `exploration' agent is rewarded for transitioning to previously unseen environment states and employ a graph-structured memory to encode the agent's past trajectory. Experimental results demonstrate that our approach is extremely effective for exploration of spatial maps; and when applied on the challenging problems of coverage-guided software-testing of domain-specific programs and real-world mobile applications, it outperforms methods that have been hand-engineered by human experts.
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We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural “oracle”. We evaluate NDPS on the task of learning to drive a simulated car in the TORCS car-racing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.
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Relational inductive biases, deep learning, and graph networks
Peter Battaglia
Jessica Blake Chandler Hamrick
Victor Bapst
Alvaro Sanchez
Vinicius Zambaldi
Andrea Tacchetti
David Raposo
Adam Santoro
Ryan Faulkner
Caglar Gulcehre
Francis Song
Andy Ballard
Justin Gilmer
Ashish Vaswani
Kelsey Allen
Charles Nash
Victoria Jayne Langston
Chris Dyer
Nicolas Heess
Daan Wierstra
Matt Botvinick
Yujia Li
Razvan Pascanu
arXiv (2018)
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The purpose of this paper is to explore relational inductive biases in modern AI, especially
deep learning, describing a rough taxonomy of existing approaches, and introducing a common
mathematical framework for expressing and unifying various approaches. The key theme running through this work is structure—how the world is structured, and how the structure of different computational strategies determines their strengths and weaknesses.
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As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), this paper introduces a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of generalizations: to previously unseen instructions and to longer sequences of instructions. For generalization over unseen instructions, we propose a new analogy-making objective which encourages learning correspondences between similar subtasks using neural networks. For generalization over sequential instructions, we present a hierarchical deep RL architecture where a meta controller learns to use the acquired skills while executing the instructions. To deal with delayed reward, we propose a new neural architecture in the meta controller that learns when to update the subtask, which makes learning more stable. Experimental results on a stochastic 3D visual domain show that analogy-making can be successfully applied to various generalization scenarios, and our hierarchical architecture generalizes well to longer instructions as well as unseen instructions.
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