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Given a target distribution $\pi$ and an arbitrary Markov infinitesimal generator $L$ on a finite state space $\mathcal{X}$, we develop three structured and inter-related approaches to generate new reversiblizations from $L$. The first approach hinges on a geometric perspective, in which we view reversiblizations as projections onto the space of $\pi$-reversible generators under suitable information divergences such as $f$-divergences. With different choices of functions $f$, we not only recover nearly all established reversiblizations but also unravel and generate new reversiblizations. Along the way, we unveil interesting geometric results such as bisection properties, Pythagorean identities, parallelogram laws and a Markov chain counterpart of the arithmetic-geometric-harmonic mean inequality governing these reversiblizations. This further serves as motivation for introducing the notion of information centroids of a sequence of Markov chains and to give conditions for their existence and uniqueness. Building upon the first approach, we view reversiblizations as generalized means. In this second approach, we construct new reversiblizations via different natural notions of generalized means such as the Cauchy mean or the dual mean. In the third approach, we combine the recently introduced locally-balanced Markov processes framework and the notion of convex $*$-conjugate in the study of $f$-divergence. The latter offers a rich source of balancing functions to generate new reversiblizations.

We consider the problem of computing the Maximal Exact Matches (MEMs) of a given pattern $P[1..m]$ on a large repetitive text collection $T[1..n]$, which is represented as a (hopefully much smaller) run-length context-free grammar of size $g_{rl}$. We show that the problem can be solved in time $O(m^2 \log^\epsilon n)$, for any constant $\epsilon > 0$, on a data structure of size $O(g_{rl})$. Further, on a locally consistent grammar of size $O(\delta\log\frac{n}{\delta})$, the time decreases to $O(m\log m(\log m + \log^\epsilon n))$. The value $\delta$ is a function of the substring complexity of $T$ and $\Omega(\delta\log\frac{n}{\delta})$ is a tight lower bound on the compressibility of repetitive texts $T$, so our structure has optimal size in terms of $n$ and $\delta$. We extend our results to the problem of finding $q$-MEMs, which must appear at least $q$ times in $T$.

For a graph $G = (V, E)$ with vertex set $V$ and edge set $E$, a function $ f : V \rightarrow \{0, 1, 2, . . . , diam(G)\} $ is called a $\textit{broadcast}$ on $G$. For each vertex $u \in V$, if there exists a vertex $v$ in $G$ (possibly, $u = v$) such that $f (v) > 0$ and $d(u, v) \leq f (v)$, then $f$ is called a $\textit{dominating broadcast}$ on $G$. The $\textit{cost}$ of the dominating broadcast $f$ is the quantity $ \sum_{v\in V}f(v)$. The minimum cost of a dominating broadcast is the \textit{broadcast domination number} of $G$, denoted by $ \gamma_{b}(G) $. A $\textit{multipacking}$ is a set $S \subseteq V$ in a graph $G = (V, E)$ such that for every vertex $v \in V$ and for every integer $r \geq 1$, the ball of radius $r$ around $v$ contains at most $r$ vertices of $S$, that is, there are at most $r$ vertices in $S$ at a distance at most $r$ from $v$ in $G$. The $\textit{multipacking number}$ of $G$ is the maximum cardinality of a multipacking of $ G $ and is denoted by $ mp(G) $. We show that, for any cactus graph $G$, $\gamma_b(G)\leq \frac{3}{2}mp(G)+\frac{11}{2}$. We also show that $\gamma_b(G)-mp(G)$ can be arbitrarily large for cactus graphs by constructing an infinite family of cactus graphs such that the ratio $\gamma_b(G)/mp(G)=4/3$, with $mp(G)$ arbitrarily large. This result shows that, for cactus graphs, we cannot improve the bound $\gamma_b(G)\leq \frac{3}{2}mp(G)+\frac{11}{2}$ to a bound in the form $\gamma_b(G)\leq c_1\cdot mp(G)+c_2$, for any constant $c_1<4/3$ and $c_2$. Moreover, we provide an $O(n)$-time algorithm to construct a multipacking of $G$ of size at least $\frac{2}{3}mp(G)-\frac{11}{3}$, where $n$ is the number of vertices of the graph $G$.

We revisit the main result of Carmosino et al \cite{CILM18} which shows that an $\Omega(n^{\omega/2+\epsilon})$ size noncommutative arithmetic circuit size lower bound (where $\omega$ is the matrix multiplication exponent) for a constant-degree $n$-variate polynomial family $(g_n)_n$, where each $g_n$ is a noncommutative polynomial, can be ``lifted'' to an exponential size circuit size lower bound for another polynomial family $(f_n)$ obtained from $(g_n)$ by a lifting process. In this paper, we present a simpler and more conceptual automata-theoretic proof of their result.

A graph $G$ contains a graph $H$ as an induced minor if $H$ can be obtained from $G$ by vertex deletions and edge contractions. The class of $H$-induced-minor-free graphs generalizes the class of $H$-minor-free graphs, but unlike $H$-minor-free graphs, it can contain dense graphs. We show that if an $n$-vertex $m$-edge graph $G$ does not contain a graph $H$ as an induced minor, then it has a balanced vertex separator of size $O_{H}(\sqrt{m})$, where the $O_{H}(\cdot)$-notation hides factors depending on $H$. More precisely, our upper bound for the size of the balanced separator is $O(\min(|V(H)|^2, \log n) \cdot \sqrt{|V(H)|+|E(H)|} \cdot \sqrt{m})$. We give an algorithm for finding either an induced minor model of $H$ in $G$ or such a separator in randomized polynomial-time. We apply this to obtain subexponential $2^{O_{H}(n^{2/3} \log n)}$ time algorithms on $H$-induced-minor-free graphs for a large class of problems including maximum independent set, minimum feedback vertex set, 3-coloring, and planarization. For graphs $H$ where every edge is incident to a vertex of degree at most 2, our results imply a $2^{O_{H}(n^{2/3} \log n)}$ time algorithm for testing if $G$ contains $H$ as an induced minor. Our second main result is that there exists a fixed tree $T$, so that there is no $2^{o(n/\log^3 n)}$ time algorithm for testing if a given $n$-vertex graph contains $T$ as an induced minor unless the Exponential Time Hypothesis (ETH) fails. Our reduction also gives NP-hardness, which solves an open problem asked by Fellows, Kratochv\'il, Middendorf, and Pfeiffer [Algorithmica, 1995], who asked if there exists a fixed planar graph $H$ so that testing for $H$ as an induced minor is NP-hard.

This paper presents a framework for smooth optimization of objectives with $\ell_q$ and $\ell_{p,q}$ regularization for (structured) sparsity. Finding solutions to these non-smooth and possibly non-convex problems typically relies on specialized optimization routines. In contrast, the method studied here is compatible with off-the-shelf (stochastic) gradient descent that is ubiquitous in deep learning, thereby enabling differentiable sparse regularization without approximations. The proposed optimization transfer comprises an overparametrization of selected model parameters followed by a change of penalties. In the overparametrized problem, smooth and convex $\ell_2$ regularization induces non-smooth and non-convex regularization in the original parametrization. We show that the resulting surrogate problem not only has an identical global optimum but also exactly preserves the local minima. This is particularly useful in non-convex regularization, where finding global solutions is NP-hard and local minima often generalize well. We provide an integrative overview that consolidates various literature strands on sparsity-inducing parametrizations in a general setting and meaningfully extend existing approaches. The feasibility of our approach is evaluated through numerical experiments, demonstrating its effectiveness by matching or outperforming common implementations of convex and non-convex regularizers.

The Cover Suffix Tree (CST) of a string $T$ is the suffix tree of $T$ with additional explicit nodes corresponding to halves of square substrings of $T$. In the CST an explicit node corresponding to a substring $C$ of $T$ is annotated with two numbers: the number of non-overlapping consecutive occurrences of $C$ and the total number of positions in $T$ that are covered by occurrences of $C$ in $T$. Kociumaka et al. (Algorithmica, 2015) have shown how to compute the CST of a length-$n$ string in $O(n \log n)$ time. We show how to compute the CST in $O(n)$ time assuming that $T$ is over an integer alphabet. Kociumaka et al. (Algorithmica, 2015; Theor. Comput. Sci., 2018) have shown that knowing the CST of a length-$n$ string $T$, one can compute a linear-sized representation of all seeds of $T$ as well as all shortest $\alpha$-partial covers and seeds in $T$ for a given $\alpha$ in $O(n)$ time. Thus our result implies linear-time algorithms computing these notions of quasiperiodicity. The resulting algorithm computing seeds is substantially different from the previous one (Kociumaka et al., SODA 2012, ACM Trans. Algorithms, 2020). Kociumaka et al. (Algorithmica, 2015) proposed an $O(n \log n)$-time algorithm for computing a shortest $\alpha$-partial cover for each $\alpha=1,\ldots,n$; we improve this complexity to $O(n)$. Our results are based on a new characterization of consecutive overlapping occurrences of a substring $S$ of $T$ in terms of the set of runs (see Kolpakov and Kucherov, FOCS 1999) in $T$. This new insight also leads to an $O(n)$-sized index for reporting overlapping consecutive occurrences of a given pattern $P$ of length $m$ in $O(m+output)$ time, where $output$ is the number of occurrences reported. In comparison, a general index for reporting bounded-gap consecutive occurrences of Navarro and Thankachan (Theor. Comput. Sci., 2016) uses $O(n \log n)$ space.

We study the tolerant testing problem for high-dimensional samplers. Given as input two samplers $\mathcal{P}$ and $\mathcal{Q}$ over the $n$-dimensional space $\{0,1\}^n$, and two parameters $\varepsilon_2 > \varepsilon_1$, the goal of tolerant testing is to test whether the distributions generated by $\mathcal{P}$ and $\mathcal{Q}$ are $\varepsilon_1$-close or $\varepsilon_2$-far. Since exponential lower bounds (in $n$) are known for the problem in the standard sampling model, research has focused on models where one can draw \textit{conditional} samples. Among these models, \textit{subcube conditioning} ($\mathsf{SUBCOND}$), which allows conditioning on arbitrary subcubes of the domain, holds the promise of widespread adoption in practice owing to its ability to capture the natural behavior of samplers in constrained domains. To translate the promise into practice, we need to overcome two crucial roadblocks for tests based on $\mathsf{SUBCOND}$: the prohibitively large number of queries ($\tilde{\mathcal{O}}(n^5/\varepsilon_2^5)$) and limitation to non-tolerant testing (i.e., $\varepsilon_1 = 0$). The primary contribution of this work is to overcome the above challenges: we design a new tolerant testing methodology (i.e., $\varepsilon_1 \geq 0$) that allows us to significantly improve the upper bound to $\tilde{\mathcal{O}}(n^3/(\varepsilon_2-\varepsilon_1)^5)$.

In the misspecified spectral algorithms problem, researchers usually assume the underground true function $f_{\rho}^{*} \in [\mathcal{H}]^{s}$, a less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS) $\mathcal{H}$ for some $s\in (0,1)$. The existing minimax optimal results require $\|f_{\rho}^{*}\|_{L^{\infty}}<\infty$ which implicitly requires $s > \alpha_{0}$ where $\alpha_{0}\in (0,1)$ is the embedding index, a constant depending on $\mathcal{H}$. Whether the spectral algorithms are optimal for all $s\in (0,1)$ is an outstanding problem lasting for years. In this paper, we show that spectral algorithms are minimax optimal for any $\alpha_{0}-\frac{1}{\beta} < s < 1$, where $\beta$ is the eigenvalue decay rate of $\mathcal{H}$. We also give several classes of RKHSs whose embedding index satisfies $ \alpha_0 = \frac{1}{\beta} $. Thus, the spectral algorithms are minimax optimal for all $s\in (0,1)$ on these RKHSs.

While existing work in robust deep learning has focused on small pixel-level $\ell_p$ norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations -- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.