Training Deep Networks without Learning Rates Through Coin Betting

Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning rate free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms

A Contextual Bandit Bake-off

Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally efficient methods, the practical behavior of these algorithms is still poorly understood. We leverage the availability of large numbers of supervised learning datasets to empirically evaluate contextual bandit algorithms, focusing on practical methods that learn by relying on optimization oracles from supervised learning. We find that a recent method (Foster et al., 2018) using optimism under uncertainty works the best overall. A surprisingly close second is a simple greedy baseline that only explores implicitly through the diversity of contexts, followed by a variant of Online Cover (Agarwal et al., 2014) which tends to be more conservative but robust to problem specification by design. Along the way, we also evaluate various components of contextual bandit algorithm design such as loss estimators. Overall, this is a thorough study and review of contextual bandit methodology

The Professional Identity Development of Counseling Students During Extreme Stressors: Lessons Learned in the COVID-19 Pandemic

Based on Bronfenbrenner’s bioecological framework and current literature, we discussed the impact of the COVID-19 crisis may have shaped the professional identity development (PID) of counseling students and the ecosystems of counselor education. While the discipline recognizes the importance of paying attention to counseling students’ PID, the discourse on the topic in the context of extreme environmental stressors such a pandemic appears to be lacking. We discussed in this paper the opportunity the COVID-19 pandemic has presented to counselor educators and supervisors (CES) to frame extreme challenging moments like theses as times to facilitate the strengthening and internalizing of counselor profession identity among counseling trainees. We further shared lessons learned as CES and offered suggestions to various stakeholders in counselor education for consideration. We concluded the paper by exploring implications, technological possibilities, and research possibilities in counselor training