arXiv subjects
Publications and source records attributed to Colin Tang.
We establish a sharp lower bound on the $L_p$-norm of sums of independent exponential random variables with fixed variance, for $p \geq 2$, thus extending Hunter's positivity theorem (1976) for completely homogeneous polynomials. We determine the exact regime of $p$ where such sums enjoy Schur-monotonicity.
Consider the empirical risk minimization (ERM) problem, which is stated as follows. Let $K_1, \dots, K_m$ be compact convex sets with $K_i \subseteq \mathbb{R}^{n_i}$ for $i \in [m]$, $n = \sum_{i=1}^m n_i$, and $n_i\le C_K$ for some absolute constant $C_K$. Also, consider a matrix $A \in \mathbb{R}^{n \times d}$ and vectors $b \in \mathbb{R}^d$ and $c \in \mathbb{R}^n$. Then the ERM problem asks to find \[ \min_{\substack{x \in K_1 \times \dots \times K_m\\ A^\top x = b}} c^\top x. \] We give an algorithm to solve this to high accuracy in time $\widetilde{O}(nd + d^6\sqrt{n}) \le \widetilde{O} (nd + d^{11})$, which is nearly-linear time in the input size when $A$ is dense and $n \ge d^{10}$. Our result is achieved by implementing an $\widetilde{O}(\sqrt{n})$-iteration interior point method (IPM) efficiently using dynamic data structures. In this direction, our key technical advance is a new algorithm for maintaining leverage score overestimates of matrices undergoing row updates. Formally, given a matrix $A \in \mathbb{R}^{n \times d}$ undergoing $T$ batches of row updates of total size $n$ we give an algorithm which can maintain leverage score overestimates of the rows of $A$ summing to $\widetilde{O}(d)$ in total time $\widetilde{O}(nd + Td^6)$. This data structure is used to sample a spectral sparsifier within a robust IPM framework to establish the main result.
We sharpen the moment comparison inequalities with sharp constants for sums of random vectors uniform on Euclidean spheres, providing a deficit term (optimal in high dimensions).
Simplex slicing (Webb, 1996) is a sharp upper bound on the volume of central hyperplane sections of the regular simplex. We extend this to sharp bounds in the probabilistic framework of negative moments, and beyond, of centred log-concave random variables, establishing a curious phase transition of the extremising distribution for new sharp reverse H\"older-type inequalities.
We show that for the regular n-simplex, the 1-codimensional central slice that's parallel to a facet will achieve the minimum area (up to a 1-o(1) factor) among all 1-codimensional central slices, thus improving the previous best known lower bound (Brzezinski 2013) by a factor of $\frac{2\sqrt{3}}{e} \approx 1.27$. In addition to the standard technique of interpreting geometric problems as problems about probability distributions and standard Fourier-analytic techniques, we rely on a new idea, mainly \emph{changing the contour of integration} of a meromorphic function.
We establish dimension-free stability of Webb's sharp simplex slicing (1996). Incidentally, we investigate Lipschitzness of volume of hyperplane central sections of arbitrary (not necessarily symmetric) convex bodies.
A hinged-ruler is a sequence of line segments in the plane joined end-to-end with hinges, so each hinge joins exactly two segments, the first segment and last segment are adjacent to only one hinge each, and all other segments are adjacent to exactly two hinges. Hopcroft, Joseph, and Whitesides first posed the hinged-ruler-folding problem in their 1985 paper "On the Movement of Robot Arms in 2-Dimensional Bounded Regions": given a hinged-ruler and a real number $K$, can the hinged-ruler be folded so as to fit within a one-dimensional interval of length $K$? We show that if the segment lengths are constrained to be real numbers in the interval $[0,1]$, then we can always fold the hinged-ruler so as to fit within a one-dimensional interval of length $2-\Omega(\frac{1}{2^{n/4}})$. On the other hand, we give a construction for a hinged-ruler which cannot be folded into some one-dimensional interval of length $2-O(\frac{1}{2^{n/4}})$.