- talks - lists.cs.princeton.edu

Niranjani Prasad will present her Pre FPO "Methods for Reinforcement Learning in Clinical Decision Support" on Friday, February 21, 2020 at 1pm in CS 402
by Nicki Mahler 17 Feb '20

17 Feb '20

Niranjani Prasad will present her Pre FPO "Methods for Reinforcement Learning in Clinical Decision Support" on Friday, February 21, 2020 at 1pm in CS 402. The members of her committee are as follows: Barbara Engelhardt (advisor); Readers: Ryan Adams, Finale Doshi-Velez (Harvard); Examiners: Sebastian Seung, Mengdi Wang (ORFE), and Barbara Engelhardt Everyone is invited to attend her talk. The talk abstract follows below: The management of routine interventions such as mechanical ventilation and sedation, or the ordering of blood tests, constitutes a major part of the care of critically ill patients. Timely and proportionate interventions are crucial to improving patient outcomes and reducing costs, but these procedures are often poorly understood, particularly for heterogeneous patient populations, and clinical opinion on best protocols vary. In my thesis, I focus on the development of a clinician-in-loop decision support system for weaning patients from mechanical ventilation, that leverages available information in the data-intensive ICU setting to predict time to extubation readiness and provide personalized recommendations for sedation and ventilator support. To this end, I employ off-policy reinforcement learning algorithms to learn an optimal sequence of treatment actions from suboptimal historical ICU data. I model patient admissions as Markov decision processes (MDPs), developing tailored representations of patient state, action space and reward function, and learn treatment policies using Fitted Q-iteration. I demonstrate that this framework shows promise in recommending protocols with positive clinical outcomes, in terms of managing patient stability and reintubation rates, when assessed against current practice. The second part of my thesis is directed towards the problem of effective reward design when applying reinforcement learning to clinical decision-making tasks. A key impediment to reinforcement learning in practice is in distilling multiple (and often conflicting) clinical imperatives into a single scalar feedback signal. To tackle this, I develop methods based on Pareto optimality to solve MDPs given a vector-valued reward function. I illustrate these methods in the context of optimizing the ordering of laboratory tests for patients in critical care. Extending this further, I introduce a mechanism for using available clinical data to restrict the candidate set of convex combinations of elements in the reward function, to those that yield a scalar reward which (a) reflects what we implicitly know from the data about reasonable behaviour for a task, and (b) allows for robust off-policy evaluation. Together, these help ensure that we learn policies that we trust to be implemented in high-risk settings.

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Reminder: Colloquium Speaker Sara Achour - tomorrow at 12:30pm
by Emily C. Lawrence 17 Feb '20

17 Feb '20

CS Colloquium Speaker Sara Achour, Massachusetts Institute of Technology Tuesday, February 18 - 12:30pm Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25917 | https://www.cs.princeton.edu/events/25917 ] Title: New Compilation Techniques for Reconfigurable Analog Devices Reconfigurable analog devices are a powerful new computing substrate especially appropriate for executing dynamical systems in an energy efficient manner. These devices leverage the physical behavior of transistors to directly implement computation. Under this paradigm, voltages and currents within the device implement continuously evolving variables in the computation. In this talk, I discuss compilation techniques for automatically configuring such devices to execute dynamical systems. I present Legno , the first compilation system that automatically targets a real reconfigurable analog device of this class. Legno synthesizes analog circuits from parametric and specialized analog blocks and accounts for analog noise, quantization error, operating range limitations, and manufacturing variations within the device. I evaluate Legno on applications from the biology, physics, and controls domains. The results demonstrate that these applications execute with acceptable error while consuming microjoules of energy. Bio: Sara Achour is a PhD candidate at the Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology (CSAIL MIT) and a NSF Fellowship recipient. Her research focuses on new techniques and tools, specifically new programming languages, compilers, and runtime systems, that enable end users to easily develop computations that exploit the potential of emerging nontraditional computing platforms.

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Colloquium Speaker Aviv Tamar, Thurs Feb 20 at 12:30pm
by Emily C. Lawrence 14 Feb '20

14 Feb '20

CS Colloquium Speaker Aviv Tamar, Technion - Israel Institute for Technology Thursday, February 20 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25919 | https://www.cs.princeton.edu/events/25919 ] Title: Robot Learning and Planning How can we build autonomous robots that operate in unstructured and dynamic environments such as homes or hospitals? This problem has been investigated under several disciplines, including planning (motion planning, task planning, etc.), and reinforcement learning. While both of these fields have witnessed tremendous progress, each have fundamental drawbacks: planning approaches require substantial manual engineering in mapping perception to a formal planning problem, while RL, which can operate directly on raw percepts, is data hungry, cannot generalize to new tasks, and is ‘black box’ in nature. In this talk, we present several studies that aim to mitigate these shortcomings by combining ideas from both planning and learning. We start by introducing value iteration networks, a type of differentiable planner that can be used within model-free RL to obtain better generalization. Next, we consider a practical robotic assembly problem, and show that motion planning, based on readily available CAD data, can be combined with RL to quickly learn policies for assembling tight fitting objects. We conclude with our recent work on learning to imagine goal-directed visual plans. Motivated by humans’ remarkable capability to predict and plan complex manipulations of objects, and recent advances such as GANs in imagining images, we present Visual Plan Imagination (VPI) — a new computational problem that combines image imagination and planning. In VPI, given off-policy image data from a dynamical system, the task is to ‘imagine’ image sequences that transition the system from start to goal. Key to our method is Causal InfoGAN , a deep generative model that can learn features that are compatible with strong planning algorithms. We demonstrate our approach on learning to imagine and execute robotic rope manipulation from image data. Bio: Aviv Tamar is an Assistant Professor at the Department of Electrical Engineering at Technion - Israel Institute for Technology. Previously, he was a post-doc at UC Berkeley with Prof. Pieter Abbeel , and prior to that, he completed his PhD with Prof. Shie Mannor at Technion . Aviv's research focuses on reinforcement learning, representation learning, and robotics. His work has been recognized by a NeurIPS Best Paper award, a Google Faculty Award, and the Alon fellowship for young researchers.

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Colloquium Speaker Zhihao Jia, Wed Feb 19 at 12:30pm
by Emily C. Lawrence 13 Feb '20

13 Feb '20

CS Colloquium Speaker Zhihao Jia, Stanford University Wednesday, February 19 - 12:30pm Computer Science - Room 105 Host: Kai Li [ https://www.cs.princeton.edu/events/25916 | https://www.cs.princeton.edu/events/25916 ] Title: Automated Discovery of Machine Learning Optimizations As an increasingly important workload, machine learning (ML) applications require different performance optimization techniques from traditional runtimes and compilers. In particular, to accelerate ML applications, it is generally necessary to perform ML computations on heterogeneous hardware and parallelize computations using multiple data dimensions, neither of which is even expressible in traditional compilers and runtimes. In this talk, I will describe my work on automated discovery of performance optimizations to accelerate ML computations. TASO, the Tensor Algebra SuperOptimizer , optimizes the computation graphs of deep neural networks (DNNs) by automatically generating potential graph optimizations and formally verifying their correctness. TASO outperforms rule-based graph optimizers in existing ML systems (e.g., TensorFlow , TensorRT , and TVM) by up to 3x by automatically discovering novel graph optimizations, while also requiring significantly less human effort. FlexFlow is a system for accelerating distributed DNN training. FlexFlow identifies parallelization dimensions not considered in existing ML systems (e.g., TensorFlow and PyTorch ) and automatically discovers fast parallelization strategies for a specific parallel machine. Companies and national labs are using FlexFlow to train production ML models that do not scale well in current ML systems, achieving over 10x performance improvement. I will also outline future research directions for further automating ML systems, such as codesigning ML models, software systems, and hardware backends for end-to-end ML deployment. Bio: Zhihao Jia is a Ph.D. candidate in the Computer Science department at Stanford University working with Alex Aiken and Matei Zaharia . His research interests lie in the intersection of computer systems and machine learning, with a focus on building efficient, scalable, and high-performance systems for ML computations.

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Jennifer Gossels will present her FPO, "Joint Optimization for Robust Network Design and Operation" on Wednesday, 2/19/2020 at 9:30am in CS 402
by Nicki Mahler 12 Feb '20

12 Feb '20

Jennifer Gossels will present her FPO, "Joint Optimization for Robust Network Design and Operation" on Wednesday, 2/19/2020 at 9:30am in CS 402. The members of her committee are as follows: Jennifer Rexford (adviser), Readers: Kai Li and Gagan Choudhury (AT &T Labs); Examiners: Michael Freedman, Wyatt Lloyd, and Jennifer Rexford A copy of her thesis, is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend her talk. The talk abstract follows below: The Internet is an essential part of modern life, and Internet Service Provider (ISP) backbone networks are integral to our Internet experience. Therefore, ISPs must build networks that limit congestion, even when some equipment fails. This network design problem is complicated, because an optimal network design must consider the eventual runtime configuration. An ISP makes network design decisions, such as purchasing and placing equipment, on long timescales (months or years) and network operation decisions, such as routing packets, on short timescales (seconds). Design and operation interact such that the ISP must solve the network operation problem as a sub-problem of network design, rendering the network design problem difficult to formulate and computationally complex. Today ISPs resort to a variety of simplifications; they fail to take advantage of the reconfigurability offered by modern optical equipment or the opportunity to mix more- and less-powerful switches throughout their networks. In this dissertation, we show how ISPs can incorporate each of these factors into their network design and operation models to produce less expensive networks without compromising robustness. In Chapter 2, we explain how reconfigurable optical switches fundamentally change the network design and operation problems by shifting the boundary between what is fixed at design time and what is reconfigured at runtime. Then, we present an optimal formulation for this new problem and heuristics to help our solution scale. In Chapter 3, we describe a failure recovery protocol that allows ISPs to realize many of the benefits of outfitting their networks with a homogeneous collection of powerful, “smart” switches, while instead using a combination of these expensive boxes and less expensive, “dumb” switches. We make three contributions in each chapter. First, we formulate the network design optimization by extending the multicommodity flow framework to leverage colorless and directionless Reconfigurable Optical Add/Drop Multiplexers (Chapter 2) or heterogeneous nodes (Chapter 3). Second, we devise heuristics to scale our designs to larger topologies. Finally, we evaluate our ideas on a realistic backbone topology and traffic demands.

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Colloquium Speaker Sara Achour, Tues. Feb 18 at 12:30pm
by Emily C. Lawrence 12 Feb '20

12 Feb '20

CS Colloquium Speaker Sara Achour, Massachusetts Institute of Technology Tuesday, February 18 - 12:30pm Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25917 | https://www.cs.princeton.edu/events/25917 ] Title: New Compilation Techniques for Reconfigurable Analog Devices Reconfigurable analog devices are a powerful new computing substrate especially appropriate for executing dynamical systems in an energy efficient manner. These devices leverage the physical behavior of transistors to directly implement computation. Under this paradigm, voltages and currents within the device implement continuously evolving variables in the computation. In this talk, I discuss compilation techniques for automatically configuring such devices to execute dynamical systems. I present Legno , the first compilation system that automatically targets a real reconfigurable analog device of this class. Legno synthesizes analog circuits from parametric and specialized analog blocks and accounts for analog noise, quantization error, operating range limitations, and manufacturing variations within the device. I evaluate Legno on applications from the biology, physics, and controls domains. The results demonstrate that these applications execute with acceptable error while consuming microjoules of energy. Bio: Sara Achour is a PhD candidate at the Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology (CSAIL MIT) and a NSF Fellowship recipient. Her research focuses on new techniques and tools, specifically new programming languages, compilers, and runtime systems, that enable end users to easily develop computations that exploit the potential of emerging nontraditional computing platforms.

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Colloquium Speaker: Kevin Ellis TODAY at 12:30pm
by Emily C. Lawrence 11 Feb '20

11 Feb '20

CS Colloquium Speaker Kevin Ellis, Massachusetts Institute of Technology Tuesday, February 11 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths [ https://www.cs.princeton.edu/events/25918 | https://www.cs.princeton.edu/events/25918 ] Title: Building Machines that Discover Generalizable, Interpretable Knowledge Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill. Machines which can similarly master a diverse span of problems are surely far off. Here, however, I will argue that program induction--an emerging AI technique--will play a role in building this more human-like AI. Program induction systems represent knowledge as programs, and learn by synthesizing code. Across three case studies in vision, natural language, and learning-to-learn, this talk will present program induction systems that take a step toward machines that can: acquire new knowledge from modest amounts of experience; strongly generalize that knowledge to extrapolate beyond their training; learn to represent their knowledge in an interpretable format; and are applicable to a broad spread of problems, from drawing pictures to discovering equations. Driving these developments is a new neuro-symbolic algorithm for Bayesian program synthesis. This algorithm integrates maturing program synthesis technologies with several complementary AI traditions (symbolic, probabilistic, and neural). Building a human-like machine learner is a very distant, long-term goal for the field. In the near-term program induction comes with a roadmap of practical problems to push, such as language learning, scene understanding, and programming-by-examples, which this talk explores. But it's worth keeping these long-term goals in mind as well. Bio: Kevin Ellis works across artificial intelligence, program synthesis, and machine learning. He develops learning algorithms that teach machines to write code, and applies these algorithms to problems in artificial intelligence. His work has appeared in machine learning venues ( NeurIPS , ICLR, IJCAI) and cognitive science venues ( CogSci , TOPICS). He has collaborated with researchers at Harvard, Brown, McGill, Siemens, and MIT, where he is a final-year graduate student advised by Josh Tenenbaum and Armando Solar- Lezama .

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Reminder: Colloquium Speaker Kevin Ellis tomorrow at 12:30pm
by Emily C. Lawrence 10 Feb '20

10 Feb '20

CS Colloquium Speaker Kevin Ellis, Massachusetts Institute of Technology Tuesday, February 11 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths [ https://www.cs.princeton.edu/events/25918 | https://www.cs.princeton.edu/events/25918 ] Title: Building Machines that Discover Generalizable, Interpretable Knowledge Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill. Machines which can similarly master a diverse span of problems are surely far off. Here, however, I will argue that program induction--an emerging AI technique--will play a role in building this more human-like AI. Program induction systems represent knowledge as programs, and learn by synthesizing code. Across three case studies in vision, natural language, and learning-to-learn, this talk will present program induction systems that take a step toward machines that can: acquire new knowledge from modest amounts of experience; strongly generalize that knowledge to extrapolate beyond their training; learn to represent their knowledge in an interpretable format; and are applicable to a broad spread of problems, from drawing pictures to discovering equations. Driving these developments is a new neuro-symbolic algorithm for Bayesian program synthesis. This algorithm integrates maturing program synthesis technologies with several complementary AI traditions (symbolic, probabilistic, and neural). Building a human-like machine learner is a very distant, long-term goal for the field. In the near-term program induction comes with a roadmap of practical problems to push, such as language learning, scene understanding, and programming-by-examples, which this talk explores. But it's worth keeping these long-term goals in mind as well. Bio: Kevin Ellis works across artificial intelligence, program synthesis, and machine learning. He develops learning algorithms that teach machines to write code, and applies these algorithms to problems in artificial intelligence. His work has appeared in machine learning venues ( NeurIPS , ICLR, IJCAI) and cognitive science venues ( CogSci , TOPICS). He has collaborated with researchers at Harvard, Brown, McGill, Siemens, and MIT, where he is a final-year graduate student advised by Josh Tenenbaum and Armando Solar- Lezama .

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Colloquium Speaker Kira Radinsky, TODAY at 12:30pm
by Emily C. Lawrence 10 Feb '20

10 Feb '20

CS Colloquium Speaker Dr. Kira Radinsky, Diagnostic Robotics Monday, February 10 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25906 | https://www.cs.princeton.edu/events/25906 ] Learning to predict the future in healthcare Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree. But in order to take action, build a plan, we need to see the complete picture, and empower decision makers with tools to make those changes. This decade, we have finally reached a critical amount of data to facilitate the creation of such tools. My work is inspired by Mark Twain’s quote, who once said: “The past does not repeat itself, but it rhymes." Although future events have unique circumstances, they typically follow familiar past patterns. Over the past few years, I devoted my life to development of prediction techniques. My system inferred that Cholera outbreaks in land-locked areas are more likely to occur following storms, especially when preceded by a long drought. Another inference is that genocide events tend to occur following events where local opinion makers describe minority groups as pests. These types of patterns are composed of several abstractions, over variable-term temporal extents and selected from a large number of possible causes. The algorithms I developed deal with the complexity of discovering such patterns. Large-scale digital histories, social and real-time media, and human web behavior are harvested and augmented with human knowledge mined from the web to afford real-time estimations of likelihoods of future events. Most recently, these algorithms have accurately predicted the first Cholera outbreak reported in Cuba in fifty years. These types of actionable predictions, that enable preventative measures, have drawn the attention of a UN genocide-prevention organization and the Gates foundations and illustrate the vast potential for real impact on the state of humanity. In the last few years I have been focusing on applying similar techniques for the healthcare and Pharma, leveraging large amount of data obtained from both medical records, EMR and other medical research results data in a quest to create an AI system for automated medical research and breakthroughs. Bio: Dr. Kira Radinsky is the chairman and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. Dr. Radinsky has founded SalesPredict , acquired by eBay in 2016 and served as eBay Chief Scientist (IL). She gained international recognition for her work at the Technion and Microsoft Research for developing predictive algorithms that recognized the early warning signs of globally impactful events, as disease epidemics and political unrests. In 2013, she was named one of MIT Technology Review’s 35 Young Innovators Under 35 and in 2015 Forbes included her as "30 Under 30 Rising Stars in Enterprise Tech”. She is a frequent presenter at global tech and industry conferences, including TEDx , Wired, Strata Data Science, Techcrunch and publishes in HBR. Radinsky also serves as a board member in Israel Security Authorities and technology board of HSBC bank. She also holds a visiting professor position at the Technion focusing on the application of predictive data mining in medicine.

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Reminder: Colloquium Speaker Kira Radinsky, tomorrow at 12:30pm
by Emily C. Lawrence 09 Feb '20

09 Feb '20

CS Colloquium Speaker Dr. Kira Radinsky, Diagnostic Robotics Monday, February 10 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25906 | https://www.cs.princeton.edu/events/25906 ] Learning to predict the future in healthcare Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree. But in order to take action, build a plan, we need to see the complete picture, and empower decision makers with tools to make those changes. This decade, we have finally reached a critical amount of data to facilitate the creation of such tools. My work is inspired by Mark Twain’s quote, who once said: “The past does not repeat itself, but it rhymes." Although future events have unique circumstances, they typically follow familiar past patterns. Over the past few years, I devoted my life to development of prediction techniques. My system inferred that Cholera outbreaks in land-locked areas are more likely to occur following storms, especially when preceded by a long drought. Another inference is that genocide events tend to occur following events where local opinion makers describe minority groups as pests. These types of patterns are composed of several abstractions, over variable-term temporal extents and selected from a large number of possible causes. The algorithms I developed deal with the complexity of discovering such patterns. Large-scale digital histories, social and real-time media, and human web behavior are harvested and augmented with human knowledge mined from the web to afford real-time estimations of likelihoods of future events. Most recently, these algorithms have accurately predicted the first Cholera outbreak reported in Cuba in fifty years. These types of actionable predictions, that enable preventative measures, have drawn the attention of a UN genocide-prevention organization and the Gates foundations and illustrate the vast potential for real impact on the state of humanity. In the last few years I have been focusing on applying similar techniques for the healthcare and Pharma, leveraging large amount of data obtained from both medical records, EMR and other medical research results data in a quest to create an AI system for automated medical research and breakthroughs. Bio: Dr. Kira Radinsky is the chairman and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. Dr. Radinsky has founded SalesPredict , acquired by eBay in 2016 and served as eBay Chief Scientist (IL). She gained international recognition for her work at the Technion and Microsoft Research for developing predictive algorithms that recognized the early warning signs of globally impactful events, as disease epidemics and political unrests. In 2013, she was named one of MIT Technology Review’s 35 Young Innovators Under 35 and in 2015 Forbes included her as "30 Under 30 Rising Stars in Enterprise Tech”. She is a frequent presenter at global tech and industry conferences, including TEDx , Wired, Strata Data Science, Techcrunch and publishes in HBR. Radinsky also serves as a board member in Israel Security Authorities and technology board of HSBC bank. She also holds a visiting professor position at the Technion focusing on the application of predictive data mining in medicine.

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