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We study the communication complexity of multiplying $k\times t$ elements from the group $H=\text{SL}(2,q)$ in the number-on-forehead model with $k$ parties. We prove a lower bound of $(t\log H)/c^{k}$. This is an exponential improvement over previous work, and matches the state-of-the-art in the area. Relatedly, we show that the convolution of $k^{c}$ independent copies of a 3-uniform distribution over $H^{m}$ is close to a $k$-uniform distribution. This is again an exponential improvement over previous work which needed $c^{k}$ copies. The proofs are remarkably simple; the results extend to other quasirandom groups. We also show that for any group $H$, any distribution over $H^{m}$ whose weight-$k$ Fourier coefficients are small is close to a $k$-uniform distribution. This generalizes previous work in the abelian setting, and the proof is simpler.

We prove several new results on the Hamming weight of bounded uniform and small-bias distributions. We exhibit bounded-uniform distributions whose weight is anti-concentrated, matching existing concentration inequalities. This construction relies on a recent result in approximation theory due to Erd\'eyi (Acta Arithmetica 2016). In particular, we match the classical tail bounds, generalizing a result by Bun and Steinke (RANDOM 2015). Also, we improve on a construction by Benjamini, Gurel-Gurevich, and Peled (2012). We give a generic transformation that converts any bounded uniform distribution to a small-bias distribution that almost preserves its weight distribution. Applying this transformation in conjunction with the above results and others, we construct small-bias distributions with various weight restrictions. In particular, we match the concentration that follows from that of bounded uniformity and the generic closeness of small-bias and bounded-uniform distributions, answering a question by Bun and Steinke (RANDOM 2015). Moreover, these distributions are supported on only a constant number of Hamming weights. We further extend the anti-concentration constructions to small-bias distributions perturbed with noise, a class that has received much attention recently in derandomization. Our results imply (but are not implied by) a recent result of the authors (CCC 2024), and are based on different techniques. In particular, we prove that the standard Gaussian distribution is far from any mixture of Gaussians with bounded variance.

An $n$-bit boolean function is resilient to coalitions of size $q$ if any fixed set of $q$ bits is unlikely to influence the function when the other $n-q$ bits are chosen uniformly. We give explicit constructions of depth-$3$ circuits that are resilient to coalitions of size $cn/\log^{2}n$ with bias $n^{-c}$. Previous explicit constructions with the same resilience had constant bias. Our construction is simpler and we generalize it to biased product distributions. Our proof builds on previous work; the main differences are the use of a tail bound for expander walks in combination with a refined analysis based on Janson's inequality.

The hardness vs.~randomness paradigm aims to explicitly construct pseudorandom generators $G:\{0,1\}^r \rightarrow \{0,1\}^m$ that fool circuits of size $m$, assuming the existence of explicit hard functions. A ``high-end PRG'' with seed length $r=O(\log m)$ (implying BPP=P) was achieved in a seminal work of Impagliazzo and Wigderson (STOC 1997), assuming the high-end hardness assumption: there exist constants $0<\beta < 1< B$, and functions computable in time $2^{B \cdot n}$ that cannot be computed by circuits of size $2^{\beta \cdot n}$. Recently, motivated by fast derandomization of randomized algorithms, Doron et al.~(FOCS 2020) and Chen and Tell (STOC 2021), construct ``extreme high-end PRGs'' with seed length $r=(1+o(1))\cdot \log m$, under qualitatively stronger assumptions. We study whether extreme high-end PRGs can be constructed from the following scaled version of the assumption which we call ``the extreme high-end hardness assumption'', and in which $\beta=1-o(1)$ and $B=1+o(1)$. We give a partial negative answer, showing that certain approaches cannot yield a black-box proof. (A longer abstract with more details appears in the PDF file)

We study the fundamental challenge of exhibiting explicit functions that have small correlation with low-degree polynomials over $\mathbb{F}_{2}$. Our main contributions include: 1. In STOC 2020, CHHLZ introduced a new technique to prove correlation bounds. Using their technique they established new correlation bounds for low-degree polynomials. They conjectured that their technique generalizes to higher degree polynomials as well. We give a counterexample to their conjecture, in fact ruling out weaker parameters and showing what they prove is essentially the best possible. 2. We propose a new approach for proving correlation bounds with the central "mod functions", consisting of two steps: (I) the polynomials that maximize correlation are symmetric and (II) symmetric polynomials have small correlation. Contrary to related results in the literature, we conjecture that (I) is true. We argue this approach is not affected by existing "barrier results". 3. We prove our conjecture for quadratic polynomials. Specifically, we determine the maximum possible correlation between quadratic polynomials modulo 2 and the functions $(x_{1},\dots,x_{n})\to z^{\sum x_{i}}$ for any $z$ on the complex unit circle; and show that it is achieved by symmetric polynomials. To obtain our results we develop a new proof technique: we express correlation in terms of directional derivatives and analyze it by slowly restricting the direction. 4. We make partial progress on the conjecture for cubic polynomials, in particular proving tight correlation bounds for cubic polynomials whose degree-3 part is symmetric.

Let $G$ be a group such that any non-trivial representation has dimension at least $d$. Let $X=(X_{1},X_{2},\ldots,X_{t})$ and $Y=(Y_{1},Y_{2},\ldots,Y_{t})$ be distributions over $G^{t}$. Suppose that $X$ is independent from $Y$. We show that for any $g\in G$ we have $|\mathbb{P}[X_{1}Y_{1}X_{2}Y_{2}\cdots X_{t}Y_{t}=g]-1/|G||\le\frac{|G|^{2t-1}}{d^{t-1}}\sqrt{\mathbb{E}_{h\in G^{t}}X(h)^{2}}\sqrt{\mathbb{E}_{h\in G^{t}}Y(h)^{2}}.$ Our results generalize, improve, and simplify previous works.

We analyze the Fourier growth, i.e. the $L_1$ Fourier weight at level $k$ (denoted $L_{1,k}$), of various well-studied classes of "structured" $\mathbb{F}_2$-polynomials. This study is motivated by applications in pseudorandomness, in particular recent results and conjectures due to [CHHL19,CHLT19,CGLSS20] which show that upper bounds on Fourier growth (even at level $k=2$) give unconditional pseudorandom generators. Our main structural results on Fourier growth are as follows: - We show that any symmetric degree-$d$ $\mathbb{F}_2$-polynomial $p$ has $L_{1,k}(p) \le \Pr[p=1] \cdot O(d)^k$, and this is tight for any constant $k$. This quadratically strengthens an earlier bound that was implicit in [RSV13]. - We show that any read-$\Delta$ degree-$d$ $\mathbb{F}_2$-polynomial $p$ has $L_{1,k}(p) \le \Pr[p=1] \cdot (k \Delta d)^{O(k)}$. - We establish a composition theorem which gives $L_{1,k}$ bounds on disjoint compositions of functions that are closed under restrictions and admit $L_{1,k}$ bounds. Finally, we apply the above structural results to obtain new unconditional pseudorandom generators and new correlation bounds for various classes of $\mathbb{F}_2$-polynomials., Comment: Corrected a mistake in Lemma 27 in the previous version of the paper

Motivated by storage applications, we study the following data structure problem: An encoder wishes to store a collection of jointly-distributed files $\overline{X}:=(X_1,X_2,\ldots, X_n) \sim \mu$ which are \emph{correlated} ($H_\mu(\overline{X}) \ll \sum_i H_\mu(X_i)$), using as little (expected) memory as possible, such that each individual file $X_i$ can be recovered quickly with few (ideally constant) memory accesses. In the case of independent random files, a dramatic result by \Pat (FOCS'08) and subsequently by Dodis, \Pat and Thorup (STOC'10) shows that it is possible to store $\overline{X}$ using just a \emph{constant} number of extra bits beyond the information-theoretic minimum space, while at the same time decoding each $X_i$ in constant time. However, in the (realistic) case where the files are correlated, much weaker results are known, requiring at least $\Omega(n/poly\lg n)$ extra bits for constant decoding time, even for "simple" joint distributions $\mu$. We focus on the natural case of compressing\emph{Markov chains}, i.e., storing a length-$n$ random walk on any (possibly directed) graph $G$. Denoting by $\kappa(G,n)$ the number of length-$n$ walks on $G$, we show that there is a succinct data structure storing a random walk using $\lg_2 \kappa(G,n) + O(\lg n)$ bits of space, such that any vertex along the walk can be decoded in $O(1)$ time on a word-RAM. For the harder task of matching the \emph{point-wise} optimal space of the walk, i.e., the empirical entropy $\sum_{i=1}^{n-1} \lg (deg(v_i))$, we present a data structure with $O(1)$ extra bits at the price of $O(\lg n)$ decoding time, and show that any improvement on this would lead to an improved solution on the long-standing Dictionary problem. All of our data structures support the \emph{online} version of the problem with constant update and query time.

Let $G$ be the special linear group $\mathrm{SL}(2,q)$. We show that if $(a_1,\ldots,a_t)$ and $(b_1,\ldots,b_t)$ are sampled uniformly from large subsets $A$ and $B$ of $G^t$ then their interleaved product $a_1 b_1 a_2 b_2 \cdots a_t b_t$ is nearly uniform over $G$. This extends a result of the first author, which corresponds to the independent case where $A$ and $B$ are product sets. We obtain a number of other results. For example, we show that if $X$ is a probability distribution on $G^m$ such that any two coordinates are uniform in $G^2$, then a pointwise product of $s$ independent copies of $X$ is nearly uniform in $G^m$, where $s$ depends on $m$ only. Extensions to other groups are also discussed. We obtain closely related results in communication complexity, which is the setting where some of these questions were first asked by Miles and Viola. For example, suppose party $A_i$ of $k$ parties $A_1,\dots,A_k$ receives on its forehead a $t$-tuple $(a_{i1},\dots,a_{it})$ of elements from $G$. The parties are promised that the interleaved product $a_{11}\dots a_{k1}a_{12}\dots a_{k2}\dots a_{1t}\dots a_{kt}$ is equal either to the identity $e$ or to some other fixed element $g\in G$, and their goal is to determine which of the two the product is equal to. We show that for all fixed $k$ and all sufficiently large $t$ the communication is $\Omega(t \log |G|)$, which is tight. Even for $k=2$ the previous best lower bound was $\Omega(t)$. As an application, we establish the security of the leakage-resilient circuits studied by Miles and Viola in the "only computation leaks" model., Comment: 30 pages, to appear SICOMP FOCS special issue

We revisit one of the classic problems in the data stream literature, namely, that of estimating the frequency moments $F_p$ for $0 < p < 2$ of an underlying $n$-dimensional vector presented as a sequence of additive updates in a stream. It is well-known that using $p$-stable distributions one can approximate any of these moments up to a multiplicative $(1+\epsilon)$-factor using $O(\epsilon^{-2} \log n)$ bits of space, and this space bound is optimal up to a constant factor in the turnstile streaming model. We show that surprisingly, if one instead considers the popular random-order model of insertion-only streams, in which the updates to the underlying vector arrive in a random order, then one can beat this space bound and achieve $\tilde{O}(\epsilon^{-2} + \log n)$ bits of space, where the $\tilde{O}$ hides poly$(\log(1/\epsilon) + \log \log n)$ factors. If $\epsilon^{-2} \approx \log n$, this represents a roughly quadratic improvement in the space achievable in turnstile streams. Our algorithm is in fact deterministic, and we show our space bound is optimal up to poly$(\log(1/\epsilon) + \log \log n)$ factors for deterministic algorithms in the random order model. We also obtain a similar improvement in space for $p = 2$ whenever $F_2 \gtrsim \log n\cdot F_1$., Comment: 36 pages

We study how to spread $k$ tokens of information to every node on an $n$-node dynamic network, the edges of which are changing at each round. This basic {\em gossip problem} can be completed in $O(n + k)$ rounds in any static network, and determining its complexity in dynamic networks is central to understanding the algorithmic limits and capabilities of various dynamic network models. Our focus is on token-forwarding algorithms, which do not manipulate tokens in any way other than storing, copying and forwarding them. We first consider the {\em strongly adaptive} adversary model where in each round, each node first chooses a token to broadcast to all its neighbors (without knowing who they are), and then an adversary chooses an arbitrary connected communication network for that round with the knowledge of the tokens chosen by each node. We show that $\Omega(nk/\log n + n)$ rounds are needed for any randomized (centralized or distributed) token-forwarding algorithm to disseminate the $k$ tokens, thus resolving an open problem raised in~\cite{kuhn+lo:dynamic}. The bound applies to a wide class of initial token distributions, including those in which each token is held by exactly one node and {\em well-mixed} ones in which each node has each token independently with a constant probability. We also show several upper bounds in varying models., Comment: arXiv admin note: substantial text overlap with arXiv:1112.0384

We draw two incomplete, biased maps of challenges in computational complexity lower bounds.

We reduce non-deterministic time $T \ge 2^n$ to a 3SAT instance $\phi$ of quasilinear size $|\phi| = T \cdot \log^{O(1)} T$ such that there is an explicit circuit $C$ that on input an index $i$ of $\log |\phi|$ bits outputs the $i$th clause, and each output bit of $C$ depends on $O(1)$ input bits. The previous best result was $C$ in NC$^1$. Even in the simpler setting of polynomial size $|\phi| = \poly(T)$ the previous best result was $C$ in AC$^0$. More generally, for any time $T \ge n$ and parameter $r \leq n$ we obtain $\log_2 |\phi| = \max(\log T, n/r) + O(\log n) + O(\log\log T)$ and each output bit of $C$ is a decision tree of depth $O(\log r)$. As an application, we tighten Williams' connection between satisfiability algorithms and circuit lower bounds (STOC 2010; SIAM J. Comput. 2013).

We show that if one can solve 3SUM on a set of size n in time n^{1+\e} then one can list t triangles in a graph with m edges in time O(m^{1+\e}t^{1/3-\e/3}). This is a reversal of Patrascu's reduction from 3SUM to listing triangles (STOC '10). Our result builds on and extends works by the Paghs (PODS '06) and by Vassilevska and Williams (FOCS '10). We make our reductions deterministic using tools from pseudorandomness. We then re-execute both Patrascu's reduction and our reversal for the variant 3XOR of 3SUM where integer summation is replaced by bit-wise xor. As a corollary we obtain that if 3XOR is solvable in linear time but 3SUM requires quadratic randomized time, or vice versa, then the randomized time complexity of listing m triangles in a graph with $m$ edges is m^{4/3} up to a factor m^\alpha for any \alpha > 0.

We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online video-game (http://arora.ccs.neu.edu) where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random." A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot., Comment: 12 pages, 6 figures

We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be \emph{efficient with respect to resources $S$} (e.g., time, memory) if no strategy using resources $S$ can make a profit. As a first step, we consider memory-$m$ strategies whose action at time $t$ depends only on the $m$ previous observations at times $t-m,...,t-1$. We introduce and study a simple model of market evolution, where strategies impact the market by their decision to buy or sell. We show that the effect of optimal strategies using memory $m$ can lead to "market conditions" that were not present initially, such as (1) market bubbles and (2) the possibility for a strategy using memory $m' > m$ to make a bigger profit than was initially possible. We suggest ours as a framework to rationalize the technological arms race of quantitative trading firms.

We prove that to store n bits x so that each prefix-sum query Sum(i) := sum_{k < i} x_k can be answered by non-adaptively probing q cells of log n bits, one needs memory > n + n/log^{O(q)} n. Our bound matches a recent upper bound of n + n/log^{Omega(q)} n by Patrascu (FOCS 2008), also non-adaptive. We also obtain a n + n/log^{2^{O(q)}} n lower bound for storing a string of balanced brackets so that each Match(i) query can be answered by non-adaptively probing q cells. To obtain these bounds we show that a too efficient data structure allows us to break the correlations between query answers.

We show that any distribution on {-1,1}^n that is k-wise independent fools any halfspace h with error \eps for k = O(\log^2(1/\eps) /\eps^2). Up to logarithmic factors, our result matches a lower bound by Benjamini, Gurel-Gurevich, and Peled (2007) showing that k = \Omega(1/(\eps^2 \cdot \log(1/\eps))). Using standard constructions of k-wise independent distributions, we obtain the first explicit pseudorandom generators G: {-1,1}^s --> {-1,1}^n that fool halfspaces. Specifically, we fool halfspaces with error eps and seed length s = k \log n = O(\log n \cdot \log^2(1/\eps) /\eps^2). Our approach combines classical tools from real approximation theory with structural results on halfspaces by Servedio (Computational Complexity 2007).