We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajectory forecasting, our algorithm, DARKO, goes further to reason about semantic states (will I pick up an object?), and future goal states that are far in terms of both space and time. DARKO learns and forecasts from first-person visual observations of the user's daily behaviors via an Online Inverse Reinforcement Learning (IRL) approach. Classical IRL discovers only the rewards in a batch setting, whereas DARKO discovers the states, transitions, rewards, and goals of a user from streaming data. Among other results, we show DARKO forecasts goals better than competing methods in both noisy and ideal settings, and our approach is theoretically and empirically no-regret.
The theory of learning to making optimal predictions from streaming data is well studied @cite_9 but is rarely used in computer vision, compared to the more prevalent application of supervised learning theory. We believe, however, that the utility of online learning theory is likely to increase as the amount of available data for processing is ever increasing. While the concept of online learning has been applied to inverse reinforcement learning @cite_23 , the work was primarily theoretic in nature and has found limited application.
@cite_9 @cite_23
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In this paper, we consider the problem of preserving privacy in the context of online learning. Online learning involves learning from data in real-time, due to which the learned model as well as its predictions are continuously changing. This makes preserving privacy of each data point significantly more challenging as its effect on the learned model can be easily tracked by observing changes in the subsequent predictions. Furthermore, with more and more online systems (e.g. search engines like Bing, Google etc.) trying to learn their customers’ behavior by leveraging their access to sensitive customer data (through cookies etc.), the problem of privacy preserving online learning has become critical. We study the problem in the framework of online convex programming (OCP)—a popular online learning setting with several theoretical and practical implications—while using differential privacy as the formal measure of privacy. For this problem, we provide a generic framework that can be used to convert any given OCP algorithm into a private OCP algorithm with provable privacy as well as regret guarantees (utility), provided that the given OCP algorithm satisfies the following two criteria: 1) linearly decreasing sensitivity, i.e., the effect of the new data points on the learned model decreases linearly, 2) sub-linear regret. We then illustrate our approach by converting two popular OCP algorithms into corresponding differentially private algorithms while guaranteeing O( p T ) regret for strongly convex functions. Next, we consider the practically important class of online linear regression problems, for which we generalize the approach by (2010a) to provide a differentially private algorithm with just poly-log regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for the offline learning problem as well. For the offline learning problem, our approach guarantees better error bounds and is more practical than the existing state-of-the-art methods (, 2011; , 2009). Researchers have demonstrated state-of-the-art performance in sequential decision making problems (e.g., robotics control, sequential prediction) with deep neural network models. One often has access to near-optimal oracles that achieve good performance on the task during training. We demonstrate that AggreVaTeD --- a policy gradient extension of the Imitation Learning (IL) approach of (Ross & Bagnell, 2014) --- can leverage such an oracle to achieve faster and better solutions with less training data than a less-informed Reinforcement Learning (RL) technique. Using both feedforward and recurrent neural network predictors, we present stochastic gradient procedures on a sequential prediction task, dependency-parsing from raw image data, as well as on various high dimensional robotics control problems. We also provide a comprehensive theoretical study of IL that demonstrates we can expect up to exponentially lower sample complexity for learning with AggreVaTeD than with RL algorithms, which backs our empirical findings. Our results and theory indicate that the proposed approach can achieve superior performance with respect to the oracle when the demonstrator is sub-optimal. The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining behavior which maximizes return under the estimated model. Following Dearden, Friedman and Andre (1999), it is proposed that the learning process estimates online the full posterior distribution over models. To determine behavior, a hypothesis is sampled from this distribution and the greedy policy with respect to the hypothesis is obtained by dynamic programming. By using a different hypothesis for each trial appropriate exploratory and exploitative behavior is obtained. This Bayesian method always converges to the optimal policy for a stationary process with discrete states. Online learning is the process of answering a sequence of questions given knowledge of the correct answers to previous questions and possibly additional available information. Answering questions in an intelligent fashion and being able to make rational decisions as a result is a basic feature of everyday life. Will it rain today (so should I take an umbrella)? Should I fight the wild animal that is after me, or should I run away? Should I open an attachment in an email message or is it a virus? The study of online learning algorithms is thus an important domain in machine learning, and one that has interesting theoretical properties and practical applications. This dissertation describes a novel framework for the design and analysis of online learning algorithms. We show that various online learning algorithms can all be derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Online learning is performed in a sequence of consecutive rounds, where at each round the learner is given a question and is required to provide an answer to this question. After predicting an answer, the correct answer is revealed and the learner suffers a loss if there is a discrepancy between his answer and the correct one. The algorithmic framework for online learning we propose in this dissertation stems from a connection that we make between the notions of regret in online learning and weak duality in convex optimization. Regret bounds are the common thread in the analysis of online learning algorithms. A regret bound measures the performance of an online algorithm relative to the performance of a competing prediction mechanism, called a competing hypothesis. The competing hypothesis can be chosen in hindsight from a class of hypotheses, after observing the entire sequence of question- answer pairs. Over the years, competitive analysis techniques have been refined and extended to numerous prediction problems by employing complex and varied notions of progress toward a good competing hypothesis. We propose a new perspective on regret bounds which is based on the notion of duality in convex optimization. Regret bounds are universal in the sense that they hold for any possible fixed hypothesis in a given hypothesis class. We therefore cast the universal bound as a lower bound