hunch_net hunch_net-2009 hunch_net-2009-376 knowledge-graph by maker-knowledge-mining
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Introduction: I’d like to point out Yisong Yue ‘s post on Self-improving systems , which is a nicely readable description of the necessity and potential of interactive learning to deal with the information overload problem that is endemic to the modern internet.
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same-blog 1 0.99999994 376 hunch net-2009-11-06-Yisong Yue on Self-improving Systems
Introduction: I’d like to point out Yisong Yue ‘s post on Self-improving systems , which is a nicely readable description of the necessity and potential of interactive learning to deal with the information overload problem that is endemic to the modern internet.
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Introduction: I would like to point out 3 graduates this season as having my confidence they are capable of doing great things. Daniel Hsu has diverse papers with diverse coauthors on {active learning, mulitlabeling, temporal learning, …} each covering new algorithms and methods of analysis. He is also a capable programmer, having helped me with some nitty-gritty details of cluster parallel Vowpal Wabbit this summer. He has an excellent tendency to just get things done. Nicolas Lambert doesn’t nominally work in machine learning, but I’ve found his work in elicitation relevant nevertheless. In essence, elicitable properties are closely related to learnable properties, and the elicitation complexity is related to a notion of learning complexity. See the Surrogate regret bounds paper for some related discussion. Few people successfully work at such a general level that it crosses fields, but he’s one of them. Yisong Yue is deeply focused on interactive learning, which he has a
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Introduction: A new direction of research seems to be arising in machine learning: Interactive Machine Learning. This isn’t a familiar term, although it does include some familiar subjects. What is Interactive Machine Learning? The fundamental requirement is (a) learning algorithms which interact with the world and (b) learn. For our purposes, let’s define learning as efficiently competing with a large set of possible predictors. Examples include: Online learning against an adversary ( Avrim’s Notes ). The interaction is almost trivial: the learning algorithm makes a prediction and then receives feedback. The learning is choosing based upon the advice of many experts. Active Learning . In active learning, the interaction is choosing which examples to label, and the learning is choosing from amongst a large set of hypotheses. Contextual Bandits . The interaction is choosing one of several actions and learning only the value of the chosen action (weaker than active learning
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Introduction: Researchers are typically confronted with big problems that they have no idea how to solve. In trying to come up with a solution, a natural approach is to decompose the big problem into a set of subproblems whose solution yields a solution to the larger problem. This approach can go wrong in several ways. Decomposition failure . The solution to the decomposition does not in fact yield a solution to the overall problem. Artificial hardness . The subproblems created are sufficient if solved to solve the overall problem, but they are harder than necessary. As you can see, computational complexity forms a relatively new (in research-history) razor by which to judge an approach sufficient but not necessary. In my experience, the artificial hardness problem is very common. Many researchers abdicate the responsibility of choosing a problem to work on to other people. This process starts very naturally as a graduate student, when an incoming student might have relatively l
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Introduction: and I can’t help but remember him. I first met Sam as an undergraduate at Caltech where he was TA for Hopfield ‘s class, and again when I visited Gatsby , when he invited me to visit Toronto , and at too many conferences to recount. His personality was a combination of enthusiastic and thoughtful, with a great ability to phrase a problem so it’s solution must be understood. With respect to my own work, Sam was the one who advised me to make my first tutorial , leading to others, and to other things, all of which I’m grateful to him for. In fact, my every interaction with Sam was positive, and that was his way. His death is being called a suicide which is so incompatible with my understanding of Sam that it strains my credibility. But we know that his many responsibilities were great, and it is well understood that basically all sane researchers have legions of inner doubts. Having been depressed now and then myself, it’s helpful to understand at least intellectually
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Introduction: I’d like to point out Yisong Yue ‘s post on Self-improving systems , which is a nicely readable description of the necessity and potential of interactive learning to deal with the information overload problem that is endemic to the modern internet.
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Introduction: and I can’t help but remember him. I first met Sam as an undergraduate at Caltech where he was TA for Hopfield ‘s class, and again when I visited Gatsby , when he invited me to visit Toronto , and at too many conferences to recount. His personality was a combination of enthusiastic and thoughtful, with a great ability to phrase a problem so it’s solution must be understood. With respect to my own work, Sam was the one who advised me to make my first tutorial , leading to others, and to other things, all of which I’m grateful to him for. In fact, my every interaction with Sam was positive, and that was his way. His death is being called a suicide which is so incompatible with my understanding of Sam that it strains my credibility. But we know that his many responsibilities were great, and it is well understood that basically all sane researchers have legions of inner doubts. Having been depressed now and then myself, it’s helpful to understand at least intellectually
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