pdf What do engineering students go on to do? “This allowed him to collaborate across academic institutions (with faculty from the University of Washington), academic levels (mentoring undergraduates), and with academia as well as industry (Karan was a member of In8 Inc., and a founding member of Google AI Princeton). I'm very fortunate to be advised by Elad Hazan. bibtex | pdf with Elad Hazan, Cyril Zhang with Elad Hazan, Holden Lee, Cyril Zhang, Yi Zhang function unscram(){ bibtex. Despite the non-convex optimization problem, our algorithm comes with provable guarantees: it has near-optimal regret bounds compared to the best LDS in hindsight, while overparameterizing the model by a small logarithmic factor. International Conference on Machine Learning (ICML), 2019 | arXiv. We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems. email: via Spectral Filtering Karan Singh, a doctoral student in computer science, is among four winners of the Porter Ogden Jacobus Fellowship, Princeton University’s top honor for graduate students. this. We study the control of linear dynamical systems with adversarial disturbances, as opposed to statistical noise. ... Karan Singh. I did my bachelors at IIT Kanpur, where I worked on sketch-based algorithms for machine learning, and space lower bounds in the streaming model. Six departments offer BSE degrees; one also offers A.B. We give an efficient algorithm that achieves $T^{\frac{2}{3}}$ in this setting. My recent efforts seek to address the challenges that accompany continuous states, partial observability and model misspecification. Our result generalizes upon previous work in two main aspects: the algorithm can accommodate adversarial noise in the dynamics, and can handle general convex costs. pdf | bibtex | arXiv. At Google AI Princeton, ... Udaya Ghai, Tomer Koren, Karan Singh, Cyril Zhang, Yi Zhang, and visiting professor Sham Kakade. The Price of Differential Privacy for Online Learning Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? this, and hence February 17, 2020, Karan Singh. This work studies a broad class of objectives that are defined solely as functions of the state-visitation frequencies that are induced by how the agent behaves. International Conference on Machine Learning (ICML), 2019 Neural Information Processing Systems (NIPS), 2017 Spotlight Denise Valenti this | }. $\sqrt{T}$, where $T$ is the time horizon. By Github, Logarithmic Regret for Online Control Sixteen groups create community around special interests, Life outside the EQuad is key to your growth, Learn to push the boundaries of your field and lead projects, Doctoral programs in six departments cover 40 specialties, Meet faculty and grad students, learn about applying, Every Ph.D. student receives full funding, Several groups cater specifically to engineering grad students, An array of opportunities, plus proximity to NYC, Philly, Advancing human health, energy, materials science, and industrial processes, Fundamental insights into the built and natural environments, and interactions between the two, Leading the field through foundational theory, applications, and societal impact, Improving human health, energy systems, computing and communications, and security, Solving problems in energy, combustion, fluids, lasers, materials science, robotics and control systems, and nuclear security, Developing mathematical and computational tools for making decisions under uncertainty, Decarbonizing the world while increasing energy access worldwide, Promoting informed discussion of digital technologies and their role in society, Enabling students across Princeton to realize their aspirations for addressing societal problems, Multidisciplinary research driving advances in materials science and photonics, Understanding and solving problems in living systems, Making metropolitan areas healthy, sustainable, and resilient, Spanning engineering, science, math and social sciences, Design and analysis to protect digital tools and infrastructure, Engaging in broader implications of technology, Advancing innovative, safe, and ethical use of robotic systems, School of Engineering and Applied Science, Operations Research and Financial Engineering, Andlinger Center for Energy and the Environment, Keller Center for Innovation in Engineering Education, Princeton Institute for the Science and Technology of Materials, Princeton University School of Engineering and Applied Science. Princeton University, Fall 2017 Karan Singh, a doctoral student in computer science, is among four winners of the Porter Ogden Jacobus Fellowship, Princeton University’s top honor for graduate students. We show that the optimal regret in this setting can be significantly smaller, scaling as $polylog(T)$. Minimizing the standard notion of regret is computationally intractable. | with Elad Hazan, Sham Kakade, Abby Van Soest Instructors: Prof. Sanjeev Arora, Prof. Elad Hazan, COS 324: Introduction to Machine Learning We provide an efficient algorithm to optimize such such intrinsically defined objectives, when given access to a black box planning oracle. Site Map, Declaring Computer Science for AB Students, Declaring Computer Science for BSE Students, Independent Work Seminar Offerings - Fall 2020, John Wilander: CITP Seminar: A Modern, Privacy-Preserving Web, Meredith Morris: Collaboration as a Lens for Inclusive Technical Innovation, Vanessa Teague: CITP Seminar: Some Election Integrity Problems are Surprisingly Easy to Solve and Others are Very Very Hard, Board approves 4 Computer Science faculty appointments, Tool helps clear biases from computer vision. Elad Hazan Professor at Princeton University and Director Google AI Princeton Verified email at cs.princeton.edu. Reinforcement Learning Optimization … During his Ph.D. work, Karan was instrumental in co-founding a new sub-discipline called non-stochastic control, allowing for the development of new methods for control that are more robust, efficient and general than classic methodology, and that has the potential to make a significant difference in applications, especially in robotics, said Elad Hazan, a professor of computer science. In particular, the regret bounds scale as $O\left(\sqrt{T}+{\epsilon}^{-1}\right)$. My research is focused on provably efficient robust algorithms for control and reinforcement learning, aided by the lens of dynamical systems and the algorithmic toolkit of optimization and (non-stochastic) online learning. “Quite often, distilling down a problem to its mathematical essence enables the exchange and inter-operability of techniques and ideas from diverse fields of study. I'm very fortunate to be advised by Elad Hazan. | International Conference on Machine Learning (ICML), 2017 Photo by Denise Applewhite, Office of Communications. © 2020 The Trustees of Princeton University. The Nonstochastic Control Problem This is a recurring theme in my thesis, which combines tools from control theory, dynamical systems, harmonic analysis and theoretical computer science.”. Instructors: Prof. Elad Hazan, Prof. Yoram Singer. Dynamic Task Allocation for Crowdsourcing In 2018, Singh was awarded the SEAS Award for Excellence. Spectral Filtering for General Linear Dynamical Systems with Elad Hazan, Sham Kakade 'emailScramble', '[email protected]', I'm a final year PhD candidate in Computer Scienceat Princeton University. He received the Spotlight Prize at the New York Academy of Sciences’ 12th Annual Machine Learning Symposium and the ICML 2017 Travel Award. arXiv. It is truly inspiring to think of his journey from India to his achievements at the forefront of artificial intelligence research.”, (This story was adapted from a longer version, with details of the other winners, published on the Princeton University homepage.). The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a symmetric transition matrix, using a novel convex relaxation to allow for the efficient identification of phases. [3,4,5,2,6,20,21,1,19,7,8,10,11,12,13,9,17,16,15,14]); | However, only the former have enjoyed widespread adoption in training large-scale deep models. The possibility of achieving sub-linear regret was previously posed as an open question. This includes several well studied and fundamental frameworks such as the Kalman filter and the linear quadratic regulator. Select this result to view Karan G Singh's phone number, address, and more. bibtex A minimal adaptation of His dissertation, “Provably Efficient Algorithms for Reinforcement Learning & Control,” seeks ways to improve feedback-driven interactive learning algorithms. At this time, I'm a long-term research intern at Google AI. The Jacobus Fellows will be honored at Alumni Day ceremonies Saturday, Feb. 22, at Jadwin Gymnasium. Neural Information Processing Systems (NIPS), 2018 Oral Presentation In the full-information setting, our results demonstrate that $\epsilon$-differential privacy may be ensured for free.

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