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1. The Large-Error Approximate Degree of AC0

   https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2019.55  

   Bun, Mark; Thaler, Justin (September 2019, Leibniz international proceedings in informatics)

   We prove two new results about the inability of low-degree polynomials to uniformly approximate constant-depth circuits, even to slightly-better-than-trivial error. First, we prove a tight Omega~(n^{1/2}) lower bound on the threshold degree of the SURJECTIVITY function on n variables. This matches the best known threshold degree bound for any AC^0 function, previously exhibited by a much more complicated circuit of larger depth (Sherstov, FOCS 2015). Our result also extends to a 2^{Omega~(n^{1/2})} lower bound on the sign-rank of an AC^0 function, improving on the previous best bound of 2^{Omega(n^{2/5})} (Bun and Thaler, ICALP 2016). Second, for any delta>0, we exhibit a function f : {-1,1}^n -> {-1,1} that is computed by a circuit of depth O(1/delta) and is hard to approximate by polynomials in the following sense: f cannot be uniformly approximated to error epsilon=1-2^{-Omega(n^{1-delta})}, even by polynomials of degree n^{1-delta}. Our recent prior work (Bun and Thaler, FOCS 2017) proved a similar lower bound, but which held only for error epsilon=1/3. Our result implies 2^{Omega(n^{1-delta})} lower bounds on the complexity of AC^0 under a variety of basic measures such as discrepancy, margin complexity, and threshold weight. This nearly matches the trivial upper bound of 2^{O(n)} that holds for every function. The previous best lower bound on AC^0 for these measures was 2^{Omega(n^{1/2})} (Sherstov, FOCS 2015). Additional applications in learning theory, communication complexity, and cryptography are described. 

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2. The Polynomial Method Strikes Back: Tight Quantum Query Bounds via Dual Polynomials

   https://doi.org/10.1145/3188745.3188784  

   Bun, Mark; Kothari, Robin; Thaler, Justin (October 2020, Theory of computing)

   null (Ed.)

   The approximate degree of a Boolean function f is the least degree of a real polynomial that approximates f pointwise to error at most 1/3. The approximate degree of f is known to be a lower bound on the quantum query complexity of f (Beals et al., FOCS 1998 and J. ACM 2001). We find tight or nearly tight bounds on the approximate degree and quantum query complexities of several basic functions. Specifically, we show the following. k-Distinctness: For any constant k, the approximate degree and quantum query complexity of the k-distinctness function is Ω(n3/4−1/(2k)). This is nearly tight for large k, as Belovs (FOCS 2012) has shown that for any constant k, the approximate degree and quantum query complexity of k-distinctness is O(n3/4−1/(2k+2−4)). Image size testing: The approximate degree and quantum query complexity of testing the size of the image of a function [n]→[n] is Ω~(n1/2). This proves a conjecture of Ambainis et al. (SODA 2016), and it implies tight lower bounds on the approximate degree and quantum query complexity of the following natural problems. k-Junta testing: A tight Ω~(k1/2) lower bound for k-junta testing, answering the main open question of Ambainis et al. (SODA 2016). Statistical distance from uniform: A tight Ω~(n1/2) lower bound for approximating the statistical distance of a distribution from uniform, answering the main question left open by Bravyi et al. (STACS 2010 and IEEE Trans. Inf. Theory 2011). Shannon entropy: A tight Ω~(n1/2) lower bound for approximating Shannon entropy up to a certain additive constant, answering a question of Li and Wu (2017). Surjectivity: The approximate degree of the surjectivity function is Ω~(n3/4). The best prior lower bound was Ω(n2/3). Our result matches an upper bound of O~(n3/4) due to Sherstov (STOC 2018), which we reprove using different techniques. The quantum query complexity of this function is known to be Θ(n) (Beame and Machmouchi, Quantum Inf. Comput. 2012 and Sherstov, FOCS 2015). Our upper bound for surjectivity introduces new techniques for approximating Boolean functions by low-degree polynomials. Our lower bounds are proved by significantly refining techniques recently introduced by Bun and Thaler (FOCS 2017). 

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3. Explicit universal minimal constants for polynomial growth of groups

   https://doi.org/10.1515/jgth-2020-0202  

   Lyons, Russell; Mann, Avinoam; Tessera, Romain; Tointon, Matthew (July 2022, Journal of Group Theory)

   Abstract Shalom and Tao showed that a polynomial upper bound on the size of a single, large enough ball in a Cayley graph implies that the underlying group has a nilpotent subgroup with index and degree of polynomial growth both bounded effectively.The third and fourth authors proved the optimal bound on the degree of polynomial growth of this subgroup, at the expense of making some other parts of the result ineffective.In the present paper, we prove the optimal bound on the degree of polynomial growth without making any losses elsewhere.As a consequence, we show that there exist explicit positive numbers ε d \varepsilon_{d} such that, in any group with growth at least a polynomial of degree 𝑑, the growth is at least ε d ⁢ n d \varepsilon_{d}n^{d} .We indicate some applications in probability; in particular, we show that the gap at 1 for the critical probability for Bernoulli site percolation on a Cayley graph, recently proven to exist by Panagiotis and Severo, is at least exp ⁡ { - exp ⁡ { 17 ⁢ exp ⁡ { 100 ⋅ 8 100 } } } \exp\{-\exp\{17\exp\{100\cdot 8^{100}\}\}\} . 

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4. Memory-Sample Tradeoffs for Linear Regression with Small Error

   Sharan, Vatsal; Sidford, Aaron; Valiant, Gregory (January 2019, Symposium on Theory of Computing (STOC))

   We consider the problem of performing linear regression over a stream of d-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples (a_1,b_1), (a_2,b_2)..., with a_i drawn independently from a d-dimensional isotropic Gaussian, and where b_i = + \eta_i, for a fixed x in R^d with ||x||= 1 and with independent noise \eta_i drawn uniformly from the interval [-2^{-d/5},2^{-d/5}]. We show that any algorithm with at most d^2/4 bits of memory requires at least \Omega(d \log \log \frac{1}{\epsilon}) samples to approximate x to \ell_2 error \epsilon with probability of success at least 2/3, for \epsilon sufficiently small as a function of d. In contrast, for such \epsilon, x can be recovered to error \epsilon with probability 1-o(1) with memory O\left(d^2 \log(1/\epsilon)\right) using d examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization. 

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5. Searching for Regularity in Bounded Functions

   Iyer, Siddharth; Whitmeyer, Michael (July 2022, Electronic colloquium on computational complexity)

   This work studies the problem of finding a large affine subspace over the field F_2 such that a bounded function's nontrivial Fourier coefficients become small. We show that for any function f from F_2^n to [-1,1] with Fourier degree d, there exists an affine subspace of dimension at least roughly n^{1/d!}k^{-2}), wherein all of f's nontrivial Fourier coefficients become smaller than 2^{-k}. To complement this result, we show the existence of degree d functions with coefficients larger than 2^{-d log n} on any subspace of dimension larger than Omega(dn^{1/(d-1)}). In addition, we give explicit examples of functions with analogous but weaker properties. Along the way, we provide multiple characterizations of the Fourier coefficients of functions restricted to subspaces of $$\F_2^n$$ that may be useful in other contexts. Finally, we highlight applications and connections of our results to parity kill number and affine dispersers/extractors. 

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