Paper:
Real-Time Bidding with Side Information
We consider the problem of repeated bidding in online advertising auctions when some side information (e.g. browser cookies) is available ahead of submitting a bid in the form of a d-dimensional vector. The goal for the advertiser is to maximize the total utility (e.g. the total number of clicks) derived from displaying ads given that a limited budget B is allocated for a given time horizon T . Optimizing the bids is modeled as a contextual Multi-Armed Bandit (MAB) problem with a knapsack constraint and a continuum of arms. We develop UCB-type algorithms that combine two streams of literature: the confidence-set approach to linear contextual MABs and the probabilistic bisection search method for stochastic root-finding. Under mild assumptions on the underlying unknown distribution, we establish distributionindependent regret bounds of orderÕ(d · √ T ) when either B = ∞ or when B scales linearly with T .

Review:
The paper studies the problem of repeated bidding in online ad auctions where the goal is to maximize the accumulated reward over a fixed horizon and in the presence of budget constraint. It is also assumed that a context vector is available at each auctions which summarizes the specific information about the targeted person and the website. The expected value of an ad is considered to be a linear function of this context vector. This is a generalization of the setting considered in Weed et al (2016) in two directions: first having a budget constraint and second the availability of the side information. The paper proposes a bidding strategy in such a setting and provide a regret bound for it. 
While the paper technically sounds and is well-written, here are a few comments on the formulated problem.
1. It has been assumed in lines 51-53 that p_t is an iid process. This seems to be far from reality as p_t is the maximum bid of the competitors each of which might be a learning agent (just like us). In this case, competitors select their bids based on his observed history. This suggests that competitors bid (and hence p_t) is not a stationary process and more importantly is correlated with our bids. This is true even if each competitors values the ad differently and in a way specific to him. This adversarial correlation is actually one of the main differences between auctions and conventional bandit problems which has been ruled out in the paper by assuming that p_t is generated from a stationary distribution in an iid manner. 
2. Assumption 1 which plays an important role in the results of the paper mentions that p_t and v_t are conditionally independent given the side information x_t. Aside from the above reason (comment 1) and even if we assume that the competitors are not learning agents, this seems a very restrictive assumption. x_t is commonly shared between all the bidders and all of them select their bids based on it. Consider for example a scenario that a group of the bidders (including us) are interested in a specific type of (target person, website) pair and they will bid higher when  the associated x_t is reported. Thus, there is a correlation between those competitors bids (and hence p_t) and our bids given x_t. There is clearly a correlation between our bid and our value v_t (because we are a smart bidder) and hence, v_t and p_t cannot be independent given x_t. 
A few typos:
1. Assumption 1 in the Supplementary Materials is i fact Assumption 2 of the main paper.
2. There is a . missing at the end of line 313 and one at the end of Lemma 8. 
3. At the end of Section C in the Supplementary Materials it should be Lemma 3 of [2] not Lemma 11 of [2].