Title: Varieties and applications of direct product theorems Speaker: Russell Impagliazzo, UCSD & IAS Abstract: Joint work with Yevginy Dodis, Ragesh Jaiswal, Valentine Kabanets and Avi Wigderson. In many areas of complexity theory, we want to make a {\em somewhat} hard problem into a {\em reliably} hard problem. An immediate motivation for this type of {\em hardness amplification } comes from cryptography and security. For example, CAPTCHAs are now widely used to distinguish human users from artificially intelligent ``bots''. However, current CAPTCHAs are neither impossible for a bot to solve some significant fraction of the time, nor reliably solved by human users. Can we make a CAPTCHA both reliably hard for bots and reliably easy for humans? The main tools for hardness amplification are {\em direct product theorems}, where we show that a feasible solver's probability of solving many instances of a problem is significantly smaller than their probability of solving a single instance. In other words, if $f(x)$ is hard for a constant fraction of $x$'s, $f^{k}(x_1,..x_k) = f(x_1) \circ f(x_2)..\circ f(x_k)$ is hard on almost every tuple $x_1,...x_k$. While intuitive, direct product theorems are not always true and do not not always have the intuitive parameters even if true. In this talk, we will give an overview of the many varieties of direct product theorems from complexity, such as xor lemma, thresholded direct product theorem, derandomized direct product theorem, uniform direct product theorems, and direct product testing. We will sketch why these different varieties are needed to address a wide range of issues in complexity theory, such as: cryptography, average-case complexity, derandomization, error-correcting codes, and probabilistically checkable proofs. We will then show that there are {\em direct reductions} between three of the different varieties: the xor lemma, standard direct product and thresholded direct product. (This builds on recent work of Unger). These reductions can also be interpretted combinatorially, and gives new, more combinatorial, proofs of many known concentration results, such as Chernoff bounds, Azuma's Lemma, Chernoff bounds for expander walks, and generalizations. We give quantitative bounds on how well such reductions can preserve the adversary's advantage. If time permits, we will also briefly survey the use of direct product {\em testing} in probabilistically checkable proofs. In direct product testing, the goal is to determine, given very few oracle calls to a function $F$, whether there exists a function $f$ whose direct product $f^k$ is close to $F$. Direct product testing, introduced by Goldreich and Safra, was motivated by the problem of boosting the soundness of a probabilistically checkable proof. The parallel repetition of a PCP has queries that are tuples of queries in the original, and gets a label that is supposed to be the tuple of labels. Raz (later improved by Holenstein and Rao) showed that parallel repetition does increase soundness exponentially, but not at the rate one might expect intuitively. Intuitively, adding direct product testing to the parallel repetition enforces consistency between the answers of the prover, not allowing them to change the supposed label of $x_i$ based on the other queries made, which might lead to optimal improvements in soundness. Goldreich and Safra's direct product test, later improved by Dinur and Reingold, worked only when there was high correlation between $f^k$ and $F$. In contrast, Dinur and Goldenberg give a 2-query direct product test when the correlation is $1/k^{\alpha}$ for a constant $\alpha >0$ and show that no 2-query test is possible below correlation $1/k2$. In contrast, we give a 3-query test for an exponentially small correlation. Somewhat paradoxically, we also use the same techniques to design 2 query PCP's with exponentially small soundness. We also give derandomized direct product tests, which were subsequently used by Dinur and Meir to give fixed polynomial size PCP's with subconstant soundness. We will give just an intuitive overview of these results.