In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed. A common strategy is to first train a model that uses all training information, then use its predictions on unlabeled examples to train a second model that only uses the inputs available at test time. However, when the extra training-only information is weak or noisy, this Two-Stage approach can mislead the deployment model and even hurt accuracy. We propose a joint training method that learns the two models together, so the deployment model can benefit from the extra information only when it actually helps, instead of inheriting its mistakes. We provide guarantees that describe when joint training improves prediction accuracy and analyze a simple alternating training algorithm for large, high-dimensional models. Experiments on synthetic data and real-world prediction tasks show that our approach avoids these failures and robustly outperforms standard Two-Stage baselines.

We thank all the reviewers (R1,R2,R3) for their feedback and suggestions.1 Table A: Multi-task comparison across task weights. We have per-2 formed loss balancing with five different weights t3 in the multi-task loss Lm = t Lc +(1 t) Lr for4 the classification and regression losses. The results5 on OmniArt are reported in Table A. Our proposal6 is robust to the weight value, tuning the task weight7 is not vital. We obtain a moderate gain for both clas-8 sification and regression with a weight of t = 0.25.9 For the multi-task baseline, emphasizing regression10 reduces the regression error, as the gradient magnitude of the regression loss is much lower than the one for the11 classification loss.

Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL). In practice, certain, e.g., the access to states from simulators, has been exploited in training and achieved prominent empirical successes. To better understand the benefits of privileged information, we revisit and examine several simple and practically used paradigms in this setting, with both computation and sample efficiency analyses. Specifically, we first formalize the empirical paradigm of (also known as learning), demonstrating its pitfall in finding near-optimal policies. We then identify a condition of the partially observable environment, the deterministic filter condition, under which expert distillation achieves sample and computational complexities that are polynomial. Furthermore, we investigate another successful empirical paradigm of, and focus on the more challenging setting of observable partially observable Markov decision processes. We develop a belief-weighted optimistic asymmetric actor-critic algorithm with polynomial sample and quasi-polynomial computational complexities, where one key component is a new provable oracle for learning belief states that preserve under a misspecified model, which may be of independent interest. Finally, we also investigate the provable efficiency of partially observable multi-agent RL (MARL) with privileged information.

Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).

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Moreover, we highlight the benefits of our approach in two practically relevant settings (a) expertcompression,i.e.