$\require{mathtools}
%
%%% GENERIC MATH %%%
%
% Environments
\newcommand{\al}[1]{\begin{align}#1\end{align}} % need this for \tag{} to work
\renewcommand{\r}{\mathrm}
%
% Greek
\newcommand{\eps}{\epsilon}
\newcommand{\veps}{\varepsilon}
\newcommand{\Om}{\Omega}
\newcommand{\om}{\omega}
\newcommand{\Th}{\Theta}
\let\fi\phi % because it looks like an f
\let\phi\varphi % because it looks like a p
%
% Miscellaneous shortcuts
% .. over and under
\newcommand{\ss}[1]{_{\substack{#1}}}
\newcommand{\ob}{\overbrace}
\newcommand{\ub}{\underbrace}
\newcommand{\ol}{\overline}
\newcommand{\tld}{\widetilde}
\newcommand{\HAT}{\widehat}
\newcommand{\f}{\frac}
\newcommand{\s}[2]{#1 /\mathopen{}#2}
\newcommand{\rt}[1]{ {\sqrt{#1}}}
% .. relations
\newcommand{\sr}{\stackrel}
\newcommand{\sse}{\subseteq}
\newcommand{\ce}{\coloneqq}
\newcommand{\ec}{\eqqcolon}
\newcommand{\ap}{\approx}
\newcommand{\ls}{\lesssim}
\newcommand{\gs}{\gtrsim}
% .. miscer
\newcommand{\q}{\quad}
\newcommand{\qq}{\qquad}
\newcommand{\heart}{\heartsuit}
%
% Delimiters
% (I needed to create my own because the MathJax version of \DeclarePairedDelimiter doesn't have \mathopen{} and that messes up the spacing)
% .. one-part
\newcommand{\p}[1]{\mathopen{}\left( #1 \right)}
\newcommand{\b}[1]{\mathopen{}\left[ #1 \right]}
\newcommand{\set}[1]{\mathopen{}\left\{ #1 \right\}}
\newcommand{\abs}[1]{\mathopen{}\left\lvert #1 \right\rvert}
\newcommand{\floor}[1]{\mathopen{}\left\lfloor #1 \right\rfloor}
\newcommand{\ceil}[1]{\mathopen{}\left\lceil #1 \right\rceil}
\newcommand{\inner}[1]{\mathopen{}\left\langle #1 \right\rangle}
% .... (use phantom to force at least the standard height of double bars)
\newcommand{\norm}[1]{\mathopen{}\left\lVert #1 \vphantom{f} \right\rVert}
\newcommand{\frob}[1]{\norm{#1}_\mathrm{F}}
%% .. two-part
\newcommand{\incond}[2]{#1 \mathop{}\middle|\mathop{} #2}
\newcommand{\cond}[2]{ {\left.\incond{#1}{#2}\right.}}
\newcommand{\pco}[2]{\p{\incond{#1}{#2}}}
\newcommand{\bco}[2]{\b{\incond{#1}{#2}}}
\newcommand{\setco}[2]{\set{\incond{#1}{#2}}}
\newcommand{\at}[2]{ {\left.#1\right|_{#2}}}
\newcommand{\pat}[2]{\p{\at{#1}{#2}}}
\newcommand{\bat}[2]{\b{\at{#1}{#2}}}
% ..... (use phantom to force at least the standard height of double bar)
\newcommand{\oldpara}[2]{#1\vphantom{f} \mathop{}\middle\|\mathop{} #2}
%\newcommand{\para}[2]{#1\vphantom{f} \mathop{}\middle\|\mathop{} #2}
\newcommand{\para}[2]{\mathchoice{\begin{matrix}#1\\\hdashline#2\end{matrix}}{\begin{smallmatrix}#1\\\hdashline#2\end{smallmatrix}}{\begin{smallmatrix}#1\\\hdashline#2\end{smallmatrix}}{\begin{smallmatrix}#1\\\hdashline#2\end{smallmatrix}}}
\newcommand{\ppa}[2]{\p{\para{#1}{#2}}}
\newcommand{\bpa}[2]{\b{\para{#1}{#2}}}
%\newcommand{\bpaco}[4]{\bpa{\incond{#1}{#2}}{\incond{#3}{#4}}}
\newcommand{\bpaco}[4]{\bpa{\cond{#1}{#2}}{\cond{#3}{#4}}}
%
% Levels of closeness
\newcommand{\scirc}[1]{\sr{\circ}{#1}}
\newcommand{\sdot}[1]{\sr{.}{#1}}
\newcommand{\slog}[1]{\sr{\log}{#1}}
\newcommand{\createClosenessLevels}[7]{
\newcommand{#2}{\mathrel{(#1)}}
\newcommand{#3}{\mathrel{#1}}
\newcommand{#4}{\mathrel{#1\!\!#1}}
\newcommand{#5}{\mathrel{#1\!\!#1\!\!#1}}
\newcommand{#6}{\mathrel{(\sdot{#1})}}
\newcommand{#7}{\mathrel{(\slog{#1})}}
}
\let\lt\undefined
\let\gt\undefined
% .. vanilla versions (is it within a constant?)
\newcommand{\ez}{\scirc=}
\newcommand{\eq}{\simeq}
\newcommand{\eqq}{\mathrel{\eq\!\!\eq}}
\newcommand{\eqqq}{\mathrel{\eq\!\!\eq\!\!\eq}}
\newcommand{\lez}{\scirc\le}
\newcommand{\lq}{\preceq}
\newcommand{\lqq}{\mathrel{\lq\!\!\lq}}
\newcommand{\lqqq}{\mathrel{\lq\!\!\lq\!\!\lq}}
\newcommand{\gez}{\scirc\ge}
\newcommand{\gq}{\succeq}
\newcommand{\gqq}{\mathrel{\gq\!\!\gq}}
\newcommand{\gqqq}{\mathrel{\gq\!\!\gq\!\!\gq}}
\newcommand{\lz}{\scirc<}
\newcommand{\lt}{\prec}
\newcommand{\ltt}{\mathrel{\lt\!\!\lt}}
\newcommand{\lttt}{\mathrel{\lt\!\!\lt\!\!\lt}}
\newcommand{\gz}{\scirc>}
\newcommand{\gt}{\succ}
\newcommand{\gtt}{\mathrel{\gt\!\!\gt}}
\newcommand{\gttt}{\mathrel{\gt\!\!\gt\!\!\gt}}
% .. dotted versions (is it equal in the limit?)
\newcommand{\ed}{\sdot=}
\newcommand{\eqd}{\sdot\eq}
\newcommand{\eqqd}{\sdot\eqq}
\newcommand{\eqqqd}{\sdot\eqqq}
\newcommand{\led}{\sdot\le}
\newcommand{\lqd}{\sdot\lq}
\newcommand{\lqqd}{\sdot\lqq}
\newcommand{\lqqqd}{\sdot\lqqq}
\newcommand{\ged}{\sdot\ge}
\newcommand{\gqd}{\sdot\gq}
\newcommand{\gqqd}{\sdot\gqq}
\newcommand{\gqqqd}{\sdot\gqqq}
\newcommand{\ld}{\sdot<}
\newcommand{\ltd}{\sdot\lt}
\newcommand{\lttd}{\sdot\ltt}
\newcommand{\ltttd}{\sdot\lttt}
\newcommand{\gd}{\sdot>}
\newcommand{\gtd}{\sdot\gt}
\newcommand{\gttd}{\sdot\gtt}
\newcommand{\gtttd}{\sdot\gttt}
% .. log versions (is it equal up to log?)
\newcommand{\elog}{\slog=}
\newcommand{\eqlog}{\slog\eq}
\newcommand{\eqqlog}{\slog\eqq}
\newcommand{\eqqqlog}{\slog\eqqq}
\newcommand{\lelog}{\slog\le}
\newcommand{\lqlog}{\slog\lq}
\newcommand{\lqqlog}{\slog\lqq}
\newcommand{\lqqqlog}{\slog\lqqq}
\newcommand{\gelog}{\slog\ge}
\newcommand{\gqlog}{\slog\gq}
\newcommand{\gqqlog}{\slog\gqq}
\newcommand{\gqqqlog}{\slog\gqqq}
\newcommand{\llog}{\slog<}
\newcommand{\ltlog}{\slog\lt}
\newcommand{\lttlog}{\slog\ltt}
\newcommand{\ltttlog}{\slog\lttt}
\newcommand{\glog}{\slog>}
\newcommand{\gtlog}{\slog\gt}
\newcommand{\gttlog}{\slog\gtt}
\newcommand{\gtttlog}{\slog\gttt}
%
% Miscellaneous
\newcommand{\LHS}{\mathrm{LHS}}
\newcommand{\RHS}{\mathrm{RHS}}
% .. operators
\DeclareMathOperator{\poly}{poly}
\DeclareMathOperator{\polylog}{polylog}
\DeclareMathOperator{\quasipoly}{quasipoly}
\DeclareMathOperator{\negl}{negl}
\DeclareMathOperator*{\argmin}{arg\,min}
\DeclareMathOperator*{\argmax}{arg\,max}
% .. functions
\DeclareMathOperator{\id}{id}
\DeclareMathOperator{\sign}{sign}
\DeclareMathOperator{\err}{err}
\DeclareMathOperator{\ReLU}{ReLU}
% .. analysis
\let\d\undefined
\newcommand{\d}{\operatorname{d}\mathopen{}}
\newcommand{\df}[2]{ {\f{\d #1}{\d #2}}}
\newcommand{\ds}[2]{ {\s{\d #1}{\d #2}}}
\newcommand{\part}{\partial}
\newcommand{\partf}[2]{\f{\part #1}{\part #2}}
\newcommand{\parts}[2]{\s{\part #1}{\part #2}}
\newcommand{\grad}[1]{\mathop{\nabla\!_{#1}}}
% .. sets of numbers
\newcommand{\N}{\mathbb{N}}
\newcommand{\Z}{\mathbb{Z}}
\newcommand{\R}{\mathbb{R}}
\newcommand{\C}{\mathbb{C}}
\newcommand{\F}{\mathbb{F}}
%
%%% SPECIALIZED MATH %%%
%
% Logic
\renewcommand{\and}{\wedge}
\newcommand{\AND}{\bigwedge}
\newcommand{\or}{\vee}
\newcommand{\OR}{\bigvee}
\newcommand{\xor}{\oplus}
\newcommand{\XOR}{\bigoplus}
\newcommand{\union}{\cup}
\newcommand{\inter}{\cap}
\newcommand{\UNION}{\bigcup}
\newcommand{\INTER}{\bigcap}
\newcommand{\comp}{\overline}
\newcommand{\true}{\r{true}}
\newcommand{\false}{\r{false}}
\newcommand{\tf}{\set{\true,\false}}
\DeclareMathOperator{\One}{\mathbb{1}}
\DeclareMathOperator{\1}{\mathbb{1}}
%
% Linear algebra
\renewcommand{\span}{\mathrm{span}}
\DeclareMathOperator{\rank}{rank}
\DeclareMathOperator{\proj}{proj}
\DeclareMathOperator{\dom}{dom}
\DeclareMathOperator{\Img}{Im}
\newcommand{\transp}{\mathsf{T}}
\renewcommand{\t}{^\transp}
% ... named tensors
\newcommand{\namedtensorstrut}{\vphantom{fg}} % milder than \mathstrut
\newcommand{\name}[1]{\mathsf{\namedtensorstrut #1}}
\newcommand{\nbin}[2]{\mathbin{\underset{\substack{#1}}{\namedtensorstrut #2}}}
\newcommand{\ndot}[1]{\nbin{#1}{\odot}}
\newcommand{\ncat}[1]{\nbin{#1}{\oplus}}
\newcommand{\nsum}[1]{\sum\limits_{\substack{#1}}}
\newcommand{\nfun}[2]{\mathop{\underset{\substack{#1}}{\namedtensorstrut\mathrm{#2}}}}
\newcommand{\ndef}[2]{\newcommand{#1}{\name{#2}}}
\newcommand{\nt}[1]{^{\transp(#1)}}
%
% Probability
\newcommand{\Normal}{\mathcal{N}}
\let\Pr\undefined
\DeclareMathOperator*{\Pr}{Pr}
\DeclareMathOperator*{\G}{\mathbb{G}}
\DeclareMathOperator*{\Odds}{Od}
\DeclareMathOperator*{\E}{E}
\DeclareMathOperator*{\Var}{Var}
\DeclareMathOperator*{\Cov}{Cov}
\DeclareMathOperator*{\corr}{corr}
\DeclareMathOperator*{\median}{median}
\newcommand{\dTV}{d_{\mathrm{TV}}}
\newcommand{\dHel}{d_{\mathrm{Hel}}}
\newcommand{\dJS}{d_{\mathrm{JS}}}
% ... information theory
\let\H\undefined
\DeclareMathOperator*{\H}{H}
\DeclareMathOperator*{\I}{I}
\DeclareMathOperator*{\D}{D}
%
%%% SPECIALIZED COMPUTER SCIENCE %%%
%
% Complexity classes
% .. classical
\newcommand{\Poly}{\mathsf{P}}
\newcommand{\NP}{\mathsf{NP}}
\newcommand{\PH}{\mathsf{PH}}
\newcommand{\PSPACE}{\mathsf{PSPACE}}
\renewcommand{\L}{\mathsf{L}}
% .. probabilistic
\newcommand{\formost}{\mathsf{Я}}
\newcommand{\RP}{\mathsf{RP}}
\newcommand{\BPP}{\mathsf{BPP}}
\newcommand{\MA}{\mathsf{MA}}
\newcommand{\AM}{\mathsf{AM}}
\newcommand{\IP}{\mathsf{IP}}
\newcommand{\RL}{\mathsf{RL}}
% .. circuits
\newcommand{\NC}{\mathsf{NC}}
\newcommand{\AC}{\mathsf{AC}}
\newcommand{\ACC}{\mathsf{ACC}}
\newcommand{\TC}{\mathsf{TC}}
\newcommand{\Ppoly}{\mathsf{P}/\poly}
\newcommand{\Lpoly}{\mathsf{L}/\poly}
% .. resources
\newcommand{\TIME}{\mathsf{TIME}}
\newcommand{\SPACE}{\mathsf{SPACE}}
\newcommand{\TISP}{\mathsf{TISP}}
\newcommand{\SIZE}{\mathsf{SIZE}}
% .. keywords
\newcommand{\co}{\mathsf{co}}
\newcommand{\Prom}{\mathsf{Promise}}
%
% Boolean analysis
\newcommand{\zo}{\set{0,1}}
\newcommand{\pmo}{\set{\pm 1}}
\newcommand{\zpmo}{\set{0,\pm 1}}
\newcommand{\harpoon}{\!\upharpoonright\!}
\newcommand{\rr}[2]{#1\harpoon_{#2}}
\newcommand{\Fou}[1]{\widehat{#1}}
\DeclareMathOperator{\Ind}{\mathrm{Ind}}
\DeclareMathOperator{\Inf}{\mathrm{Inf}}
\newcommand{\Der}[1]{\operatorname{D}_{#1}\mathopen{}}
\newcommand{\Exp}[1]{\operatorname{E}_{#1}\mathopen{}}
\DeclareMathOperator{\Stab}{\mathrm{Stab}}
\DeclareMathOperator{\T}{T}
\DeclareMathOperator{\sens}{\mathrm{s}}
\DeclareMathOperator{\bsens}{\mathrm{bs}}
\DeclareMathOperator{\fbsens}{\mathrm{fbs}}
\DeclareMathOperator{\Cert}{\mathrm{C}}
\DeclareMathOperator{\DT}{\mathrm{DT}}
\DeclareMathOperator{\CDT}{\mathrm{CDT}} % canonical
\DeclareMathOperator{\ECDT}{\mathrm{ECDT}}
\DeclareMathOperator{\CDTv}{\mathrm{CDT_{vars}}}
\DeclareMathOperator{\ECDTv}{\mathrm{ECDT_{vars}}}
\DeclareMathOperator{\CDTt}{\mathrm{CDT_{terms}}}
\DeclareMathOperator{\ECDTt}{\mathrm{ECDT_{terms}}}
\DeclareMathOperator{\CDTw}{\mathrm{CDT_{weighted}}}
\DeclareMathOperator{\ECDTw}{\mathrm{ECDT_{weighted}}}
\DeclareMathOperator{\AvgDT}{\mathrm{AvgDT}}
\DeclareMathOperator{\PDT}{\mathrm{PDT}} % partial decision tree
\DeclareMathOperator{\DTsize}{\mathrm{DT_{size}}}
\DeclareMathOperator{\W}{\mathbf{W}}
% .. functions (small caps sadly doesn't work)
\DeclareMathOperator{\Par}{\mathrm{Par}}
\DeclareMathOperator{\Maj}{\mathrm{Maj}}
\DeclareMathOperator{\HW}{\mathrm{HW}}
\DeclareMathOperator{\Thr}{\mathrm{Thr}}
\DeclareMathOperator{\Tribes}{\mathrm{Tribes}}
\DeclareMathOperator{\RotTribes}{\mathrm{RotTribes}}
\DeclareMathOperator{\CycleRun}{\mathrm{CycleRun}}
\DeclareMathOperator{\SAT}{\mathrm{SAT}}
\DeclareMathOperator{\UniqueSAT}{\mathrm{UniqueSAT}}
%
% Dynamic optimality
\newcommand{\OPT}{\mathsf{OPT}}
\newcommand{\Alt}{\mathsf{Alt}}
\newcommand{\Funnel}{\mathsf{Funnel}}
%
% Alignment
\DeclareMathOperator{\Amp}{\mathrm{Amp}}
%
%%% TYPESETTING %%%
%
% In text
\renewcommand{\th}{^{\mathrm{th}}}
\newcommand{\degree}{^\circ}
%
% Fonts
% .. bold
\newcommand{\BA}{\boldsymbol{A}}
\newcommand{\BB}{\boldsymbol{B}}
\newcommand{\BC}{\boldsymbol{C}}
\newcommand{\BD}{\boldsymbol{D}}
\newcommand{\BE}{\boldsymbol{E}}
\newcommand{\BF}{\boldsymbol{F}}
\newcommand{\BG}{\boldsymbol{G}}
\newcommand{\BH}{\boldsymbol{H}}
\newcommand{\BI}{\boldsymbol{I}}
\newcommand{\BJ}{\boldsymbol{J}}
\newcommand{\BK}{\boldsymbol{K}}
\newcommand{\BL}{\boldsymbol{L}}
\newcommand{\BM}{\boldsymbol{M}}
\newcommand{\BN}{\boldsymbol{N}}
\newcommand{\BO}{\boldsymbol{O}}
\newcommand{\BP}{\boldsymbol{P}}
\newcommand{\BQ}{\boldsymbol{Q}}
\newcommand{\BR}{\boldsymbol{R}}
\newcommand{\BS}{\boldsymbol{S}}
\newcommand{\BT}{\boldsymbol{T}}
\newcommand{\BU}{\boldsymbol{U}}
\newcommand{\BV}{\boldsymbol{V}}
\newcommand{\BW}{\boldsymbol{W}}
\newcommand{\BX}{\boldsymbol{X}}
\newcommand{\BY}{\boldsymbol{Y}}
\newcommand{\BZ}{\boldsymbol{Z}}
\newcommand{\Ba}{\boldsymbol{a}}
\newcommand{\Bb}{\boldsymbol{b}}
\newcommand{\Bc}{\boldsymbol{c}}
\newcommand{\Bd}{\boldsymbol{d}}
\newcommand{\Be}{\boldsymbol{e}}
\newcommand{\Bf}{\boldsymbol{f}}
\newcommand{\Bg}{\boldsymbol{g}}
\newcommand{\Bh}{\boldsymbol{h}}
\newcommand{\Bi}{\boldsymbol{i}}
\newcommand{\Bj}{\boldsymbol{j}}
\newcommand{\Bk}{\boldsymbol{k}}
\newcommand{\Bp}{\boldsymbol{p}}
\newcommand{\Bq}{\boldsymbol{q}}
\newcommand{\Br}{\boldsymbol{r}}
\newcommand{\Bs}{\boldsymbol{s}}
\newcommand{\Bt}{\boldsymbol{t}}
\newcommand{\Bu}{\boldsymbol{u}}
\newcommand{\Bv}{\boldsymbol{v}}
\newcommand{\Bw}{\boldsymbol{w}}
\newcommand{\Bx}{\boldsymbol{x}}
\newcommand{\By}{\boldsymbol{y}}
\newcommand{\Bz}{\boldsymbol{z}}
\newcommand{\Balpha}{\boldsymbol{\alpha}}
\newcommand{\Bbeta}{\boldsymbol{\beta}}
\newcommand{\Bgamma}{\boldsymbol{\gamma}}
\newcommand{\Bdelta}{\boldsymbol{\delta}}
\newcommand{\Beps}{\boldsymbol{\eps}}
\newcommand{\Bveps}{\boldsymbol{\veps}}
\newcommand{\Bzeta}{\boldsymbol{\zeta}}
\newcommand{\Beta}{\boldsymbol{\eta}}
\newcommand{\Btheta}{\boldsymbol{\theta}}
\newcommand{\Biota}{\boldsymbol{\iota}}
\newcommand{\Bkappa}{\boldsymbol{\kappa}}
\newcommand{\Blambda}{\boldsymbol{\lambda}}
\newcommand{\Bmu}{\boldsymbol{\mu}}
\newcommand{\Bnu}{\boldsymbol{\nu}}
\newcommand{\Bxi}{\boldsymbol{\xi}}
\newcommand{\Bomicron}{\boldsymbol{\omicron}}
\newcommand{\Bpi}{\boldsymbol{\pi}}
\newcommand{\Brho}{\boldsymbol{\rho}}
\newcommand{\Bsigma}{\boldsymbol{\sigma}}
\newcommand{\Btau}{\boldsymbol{\tau}}
\newcommand{\Bupsilon}{\boldsymbol{\upsilon}}
\newcommand{\Bphi}{\boldsymbol{\phi}}
\newcommand{\Bfi}{\boldsymbol{\fi}}
\newcommand{\Bchi}{\boldsymbol{\chi}}
\newcommand{\Bpsi}{\boldsymbol{\psi}}
\newcommand{\Bomega}{\boldsymbol{\omega}}
% .. calligraphic
\newcommand{\CA}{\mathcal{A}}
\newcommand{\CB}{\mathcal{B}}
\newcommand{\CC}{\mathcal{C}}
\newcommand{\CD}{\mathcal{D}}
\newcommand{\CE}{\mathcal{E}}
\newcommand{\CF}{\mathcal{F}}
\newcommand{\CG}{\mathcal{G}}
\newcommand{\CH}{\mathcal{H}}
\newcommand{\CI}{\mathcal{I}}
\newcommand{\CJ}{\mathcal{J}}
\newcommand{\CK}{\mathcal{K}}
\newcommand{\CL}{\mathcal{L}}
\newcommand{\CM}{\mathcal{M}}
\newcommand{\CN}{\mathcal{N}}
\newcommand{\CO}{\mathcal{O}}
\newcommand{\CP}{\mathcal{P}}
\newcommand{\CQ}{\mathcal{Q}}
\newcommand{\CR}{\mathcal{R}}
\newcommand{\CS}{\mathcal{S}}
\newcommand{\CT}{\mathcal{T}}
\newcommand{\CU}{\mathcal{U}}
\newcommand{\CV}{\mathcal{V}}
\newcommand{\CW}{\mathcal{W}}
\newcommand{\CX}{\mathcal{X}}
\newcommand{\CY}{\mathcal{Y}}
\newcommand{\CZ}{\mathcal{Z}}
% .. typewriter
\newcommand{\TA}{\mathtt{A}}
\newcommand{\TB}{\mathtt{B}}
\newcommand{\TC}{\mathtt{C}}
\newcommand{\TD}{\mathtt{D}}
\newcommand{\TE}{\mathtt{E}}
\newcommand{\TF}{\mathtt{F}}
\newcommand{\TG}{\mathtt{G}}
\newcommand{\TH}{\mathtt{H}}
\newcommand{\TI}{\mathtt{I}}
\newcommand{\TJ}{\mathtt{J}}
\newcommand{\TK}{\mathtt{K}}
\newcommand{\TL}{\mathtt{L}}
\newcommand{\TM}{\mathtt{M}}
\newcommand{\TN}{\mathtt{N}}
\newcommand{\TO}{\mathtt{O}}
\newcommand{\TP}{\mathtt{P}}
\newcommand{\TQ}{\mathtt{Q}}
\newcommand{\TR}{\mathtt{R}}
\newcommand{\TS}{\mathtt{S}}
\newcommand{\TT}{\mathtt{T}}
\newcommand{\TU}{\mathtt{U}}
\newcommand{\TV}{\mathtt{V}}
\newcommand{\TW}{\mathtt{W}}
\newcommand{\TX}{\mathtt{X}}
\newcommand{\TY}{\mathtt{Y}}
\newcommand{\TZ}{\mathtt{Z}}$
This note uses Named tensor notation.
Suppose we have a regression problem with
- inputs: $\ndef{\batch}{batch}\ndef{\coords}{coords}X \ce (x^{(1)}, \ldots, x^{(n)}) \in \R^{\batch \times\coords}$,
- outputs: $y \ce (y^{(1)}, \ldots, y^{(n)}) \in \R^{\batch}$,
and we want to learn some weights $w \in \R^{\coords}$ to linearly approximate the relationship between inputs and outputs:
\[X \ndot\coords w \approx y.\]
More precisely, say we want to minimize the square loss
\[
\HAT\CL = \sum_\batch \p{X \ndot\coords w - y}^2 = \norm{X \ndot\coords w - y}^2_\batch.
\]
Let $d \ce |\coords|$ be the number of input features, and $n \ce |\batch|$ be the number of data points. There are two main regimes, which lead to (at least on the surface) two completely different ways to solve this problem:
- overdetermined regime ($n \gg d$):
- many data points but few input features and parameters (underparametrized);
- usually can’t fit the data perfectly;
- the inputs usually span the whole space $\R^{\coords}$, in which case the loss has a unique minimum.
- underdetermined regime ($n \ll d$):
- few data points but many input features and parameters (overparametrized);
- usually can fit the data perfectly;
- the inputs don’t span the whole space $\R^{\coords}$, so the minimum is not unique, and we need to break ties somehow.
Overdetermined regime
In the overdetermined regime ($n \gg d$), as long as $X$ has full rank $d$, the loss $\CL$ will be strictly convex, so we only need to find the value of $w$ which makes the gradient zero. We have
\[
\d \HAT\CL = 2\p{X\ndot\coords w-y} \ndot\batch X \ndot\coords \d w,
\]
so
\[
\frac{\partial \HAT\CL}{\partial w}
= 2\p{X\ndot\coords w-y} \ndot\batch X.
\]
Setting it to zero, we have
\[
X \ndot\batch \p{X \ndot\coords w} = X \ndot\batch y
\]
so by associativity we get
\[
\ub{X\nt\coords \ndot\batch X}_\text{second-moment matrix} \ndot\coords w = \ub{X\nt\coords \ndot\batch y}_\text{``input-output correlations''},
\]
where the second-moment matrix $X \ndot\batch X\nt\coords \in \R^{\coords' \times \coords}$ represents how different input coordinates correlate over the input data. As long as the input data spans $\R^\coords$, it is invertible, so we get
\[
w = \p{X \ndot\batch X\nt\coords}^{-1} \ndot{\coords'} X\nt\coords \ndot\batch y.
\]
Underdetermined regime
In the underdetermined regime ($n \ll d$), there are many more parameters than constraints, so the loss doesn’t have a unique minimum, and we need to break ties somehow.
For this, let’s impose the inductive bias of gradient descent. Since all gradients are of the form
\[
X \ndot\batch \text{something},
\]
assuming you start at $w = 0^\coords$, the final value of the weights will be
\[
w = X \ndot\batch \psi
\]
for some $\psi \in \R^{\batch}$. That is, $w$ will be a linear combination of the input vectors, with coefficients given by $\psi$. This makes sense, since the input vectors $x^{(1)}, \ldots, x^{(n)}$ are the only directions in $\R^{\coords}$ that the learning algorithm even “knows about”. And as it turns out, enforcing this is equivalent to minimizing the norm $\norm{w}_\coords^2$.
Assuming we can get perfect loss, this gives
\[
y = X \ndot\coords w
= X \ndot\coords \p{X \ndot\batch \psi},
%= X \ndot\coords \p{X\nt\batch \ndot{\batch'} \psi\nt\batch}
\]
so by associativity we get
\[
\ub{X\nt\batch \ndot\coords X}_\text{Gram matrix} \ndot\batch \psi = y\nt\batch,
%\ub{X \ndot\coords X\nt\batch}_\text{Gram matrix} \ndot\batch \psi\nt\batch = y,
\]
where the Gram matrix $X \ndot\coords X\nt\batch \in \R^{\batch \times \batch'}$ contains the inner products between the input vectors. As long as the input vectors are linearly independent, the Gram matrix is invertible, and we get
\[
\psi = \p{X \ndot\coords X\nt\batch}^{-1} \ndot{\batch'} y\nt\batch,
%\psi\nt\batch = \p{X \ndot\coords X\nt\batch}^{-1} \ndot{\batch'} y,
\]
and thus
\[
w = X \ndot\batch \psi = X \ndot\batch \p{X \ndot\coords X\nt\batch}^{-1} \ndot{\batch'} y\nt\batch.
%w = X\nt\batch \ndot{\batch'} \psi\nt\batch = X\nt\batch \ndot\batch \p{X \ndot\coords X\nt\batch}^{-1} \ndot{\batch'} y.
\]
General case
It could be that neither the second-moment matrix $X \ndot\batch X\nt\coords$ nor the Gram matrix $X \ndot\coords X\nt\batch$ is invertible. This happens when $X$ doesn’t have full-rank (for example if we have $n=2$ inputs in $d=3$ dimensions, but the inputs are linearly dependent).
To deal with the general case, we use the singular value decomposition of $X$
\[
\ndef{\sing}{sing}
X = \sum_\sing \tld{x}U V,
\]
where
- $|\sing|= \rank(X)$;
- $\tld{x} \in \R^{\sing}$ contains the nonzero singular values of $X$;
- $U \in \R^{\sing \times \coords}$ is an orthonormal basis of the span of $X$ within the input space $\R^{\coords}$;
- $V\in \R^{\sing \times \batch}$ is an orthonormal basis of the span of $X$ within $\R^{\batch}$.
Keeping the inductive bias that $w$ must be in the span of $X$ (within $\R^{\coords}$), we can express it as
\[
w = U \ndot\sing \tld{w}.
\]
Also, we can decompose $y$ into its projection onto the span of $X$ (within $\R^\batch$) and the perpendicular part:
\[
y = V \ndot \sing \tld{y} + y_\perp,
\]
where $\tld{y} \ce V \ndot\batch y$ and $V \ndot\batch y_\perp = 0^\coords$. Then we can rewrite the loss as
\[
\al{
\HAT\CL
&= \norm{X \ndot\coords w - y}_\batch^2\\
&= \norm{V \ndot\sing \p{\tld{x}\tld{w}} - V \ndot\sing\tld{y} - y_\perp}_\batch^2\tag{$U$ orthonormal}\\
&= \norm{V \ndot\sing \p{\tld{x}\tld{w} - \tld{y}}}_\batch^2 +\norm{y_\perp}_\batch^2\\
&= \norm{\tld{x}\tld{w} - \tld{y}}_\sing^2 + \norm{y_\perp}_\batch^2.\tag{$V$ orthonormal}
}
\]
So the minimum loss is $\norm{y_\perp}_\batch^2$, and it is achieved when
\[
\tld{x} \tld{w} = \tld{y} \Leftrightarrow \tld{w} = \frac{\tld{y}}{\tld{x}}
\]
(where both the multiplication and the division are elementwise). Note how we made the nonsensical dream “$X w = y \Leftrightarrow w = \frac{y}{X}$” come true just by expressing $X$, $w$ and $y$ in the right way!
How well does it learn truly linear relationships?
Suppose that the data is from some distribution $\CD$ where
- the inputs $x$ are from a uniform $d$-dimensional uniform gaussian $\Normal(0, I)$,
- the relationship is truly linear: $y = x \ndot\coords w^*$ for some fixed $w^*$.
Then how does the loss over the whole distribution
\[
\CL(w) \ce \E_{\Bx,\By \sim \CD}\b{\p{\Bx \ndot\coords w - \By}^2}
\]
evolve as we increase the number $n$ of samples?
First let’s figure what got learned. We have
\[
\tld{y}
= V \ndot\batch y
= V \ndot\batch \p{X \ndot \coords w^*} = \tld{x}\p{U \ndot\coords w^*},
\]
so
\[
w = U \ndot\sing \tld{w} = U \ndot\sing \frac{\tld{y}}{\tld{x}} = U \ndot\sing \p{U \ndot\coords w^*} = \proj_U (w^*),
\]
where $\proj_U(\cdot)$ denotes the orthogonal projection on the space spanned by $U$.
Therefore,
\[
\al{
\CL(w)
&= \E_{\Bx}\b{\p{\Bx \ndot\coords \proj_U(w^*) - \Bx \ndot\coords w^*}^2}\\
&= \E_{\Bx}\b{\p{\Bx \ndot\coords \p{\proj_U(w^*) - w^*}}^2}\\
&= \norm{\proj_U(w^*) - w^*}_\coords^2\tag{$\Bx \sim \Normal(0, I)$}\\
&= \norm{w^*}_\coords^2 - \norm{\proj_U(w^*)}_\coords^2\tag{perpendicularity},
}
\]
and since $U$ is distributed like a random subspace of dimension $\min(n, d)$, this loss has expectation
\[
\al{
\E_{X,y}\b{\CL(w)}
&=\norm{w^*}_\coords^2 - \E_{X,y}\b{\norm{\proj_U(w^*)}_\coords^2}\\
&=\p{1-\f{\min(n,d)}{d}}\norm{w^*}_\coords^2\\
&=
\begin{cases}
\p{1-\f{n}{d}}\norm{w^*}_\coords^2 & \text{if $n < d$}\\
0 & \text{otherwise.}
\end{cases}
}
\]
over the randomness of the sample $X,y$.
Stability
Since we’re dividing by $\tld{x}$, this can cause trouble if we perturb $\tld{y}$ in directions that have particularly small (but nonzero) singular values. This is ultimately what causes double descent.
#to-write
- natural derivation of the affine case
- either see it as an extra feature
- or see it as “do regression on $X$ and $Y$ minus their averages”
- connect to Correlation
- variance unexplained
- (revisit the “screening-off” property)
- phrase directions of the correlations as features?
- actually the learned weights are just a rescaling of the correlation!
- they’re essentially $\f{\Cov[X,Y]}{\Var[X]}$
- actually the correlation matrix is the linear regression between the whitened versions of $X$ and $Y$?