Efficient Online Learning via Randomized Rounding

Efficient Transductive Online Learning via Randomized Rounding

Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, which combines “random playout” and randomized rounding of loss subgradients. As an application of our approach, we provide the first computationally efficient online algorithm for collaborativ...

Large-Scale Learning with Less RAM via Randomization

We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to standard 32-bit float encodings, this reduces RAM usage by more than 50% during training and by up to 95% when making predictions from a fixed model, with almost no loss in accuracy. We also show that randomized counting ...

2 Randomized rounding approach for UFL

Universal Algorithm for Online Trading Based on the Method of Calibration

We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space). Using a universal kernel, we extend this result for any continuous stationary strategy. In th...

Online Linear Optimization over Permutations

This paper proposes an algorithm for online linear optimization problem over permutations; the objective of the online algorithm is to find a permutation of {1, . . . , n} at each trial so as to minimize the “regret” for T trials. The regret of our algorithm is O(n √ T lnn) in expectation for any input sequence. A naive implementation requires more than exponential time. On the other hand, our ...