Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Weaknesses: The paper states that model selection or model averaging approaches will not significantly improve over the best of the models (Alice's or Bob's) used in the assisted learning procedure because they fail to utilize the full data (the union of Alice's and Bob's features). However, ensemble techniques such as stacked regression (Breiman 1996) are often successfully used to improve predictive performance by combining not only different models trained on the same set of features, but also by combining different models trained on different subsets of features. In all experiments performed in the paper, only comparisons between assisted learning and the oracle model were presented. The paper would be considerably stronger if it was able to show that assisted learning compared favorably against (for instance) a stacked model generated with the predictions obtained from the different models on modules M_1, …, M_m (trained with the original public responses). Note that under the assumptions made by the paper, that the labels/response (as well as, some sort of identifier needed to collate the labels/response to the features) are public available, a simpler ensemble approach (such as stacking) could also be directly used to improve learning without sharing the private feature data.

Adversarial Feature Learning) is an interesting extension to GANs, which can be used to train a generative model by learning generator G(z) and inference E(x) functions, where G(z) maps samples from a latent space to data and E(x) is an inference model mapping observed data to the latent space. This model is trained adversarially by jointly training E(x) and G(z) with a discriminator D(x,z) which is trained to distinguish between real (E(x), x) samples and fake (z, G(z)) samples. This is an interesting approach and has been shown to generate latent representations which are useful for semi-supervised learning. The authors highlight an issue with the ALI model, by constructing a small example for which there exist optimal solutions to the ALI loss function which have poor reconstruction, i.e. G(E(x)) can be very different to x.

Neural Information Processing Systems http://nips.cc/

Alan Jern Christopher G. Lucas Charles Kemp Know Alice's preferences Infer Alice's preferences Feature-based models can only match performance of inverse decision-making approach when provided with many features.

We consider the setting of repeated fair division between two players, denoted Alice and Bob, with private valuations over a cake. In each round, a new cake arrives, which is identical to the ones in previous rounds. Alice cuts the cake at a point of her choice, while Bob chooses the left piece or the right piece, leaving the remainder for Alice. We consider two versions: sequential, where Bob observes Alice's cut point before choosing left/right, and simultaneous, where he only observes her cut point after making his choice. The simultaneous version was first considered by Aumann and Maschler (1995). We observe that if Bob is almost myopic and chooses his favorite piece too often, then he can be systematically exploited by Alice through a strategy akin to a binary search. This strategy allows Alice to approximate Bob's preferences with increasing precision, thereby securing a disproportionate share of the resource over time. We analyze the limits of how much a player can exploit the other one and show that fair utility profiles are in fact achievable. Specifically, the players can enforce the equitable utility profile of $(1/2, 1/2)$ in the limit on every trajectory of play, by keeping the other player's utility to approximately $1/2$ on average while guaranteeing they themselves get at least approximately $1/2$ on average. We show this theorem using a connection with Blackwell approachability. Finally, we analyze a natural dynamic known as fictitious play, where players best respond to the empirical distribution of the other player. We show that fictitious play converges to the equitable utility profile of $(1/2, 1/2)$ at a rate of $O(1/\sqrt{T})$.