Timezone: »
Poster
Better Full-Matrix Regret via Parameter-Free Online Learning
Ashok Cutkosky
We provide online convex optimization algorithms that guarantee improved full-matrix regret bounds. These algorithms extend prior work in several ways. First, we seamlessly allow for the incorporation of constraints without requiring unknown oracle-tuning for any learning rate parameters. Second, we improve the regret of the full-matrix AdaGrad algorithm by suggesting a better learning rate value and showing how to tune the learning rate to this value on-the-fly. Third, all our bounds are obtained via a general framework for constructing regret bounds that depend on an arbitrary sequence of norms.
Author Information
Ashok Cutkosky (Boston University)
More from the Same Authors
-
2020 Poster: Online Linear Optimization with Many Hints »
Aditya Bhaskara · Ashok Cutkosky · Ravi Kumar · Manish Purohit -
2020 Poster: Comparator-Adaptive Convex Bandits »
Dirk van der Hoeven · Ashok Cutkosky · Haipeng Luo -
2019 Poster: Momentum-Based Variance Reduction in Non-Convex SGD »
Ashok Cutkosky · Francesco Orabona -
2019 Poster: Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration »
Kwang-Sung Jun · Ashok Cutkosky · Francesco Orabona