# Universally Consistent Prediction

### Introduction

Can one predict the future well, without any subjective assumptions? Indeed, there are actually some prediction strategies that work without any subjective assumption, and approach (arguably) the best possible performance in some sense. This post introduces one such strategy described in an article by V. Vovk.

Consider the following protocol of online prediction. For $t = 1, 2, \ldots$,

1. Reality announces $x_t \in \mathcal{X}$.
2. Predictor announces $\gamma_t \in \Gamma$.
3. Reality announces $y_t \in \mathcal{Y}$.

We assume the perfect information case; that is, all $x_t$’s, $\gamma_t$’s, and $y_t$’s, once announced, are known to both Reality and Predictor.

Exercise. Find some real-life examples of this protocol. Notice that $\mathcal{X}$ can be the space of histories.

We measure the quality of prediction by a given loss function $\lambda: \mathcal{X} \times \Gamma \times \mathcal{Y} \to \mathbb{R}$, known to both Reality and Predictor. We will consider compact-type losses.

Definition. A loss function is called compact-type, if for any compact sets $A \subseteq \mathcal{X}$ and $B \subseteq \mathcal{Y}$ and any $M \in \mathbb{R}$, there exists some compact set $C \subset \Gamma$, such that $\lambda ( x, \gamma, y ) > M$ for any $x \in A$, $y \in B$, and $\gamma \notin C$.

A (possibly randomized) prediction strategy is a function $\psi: \mathcal{X} \to \Delta ( \Gamma )$, where $\Delta ( \Gamma )$ denotes the set of all probabiity measures on $\Gamma$. For each $t$, Predictor computes $Q_t := \psi( x_t )$, and chooses $\gamma_t \in \Gamma$ randomly according to $Q_t$.

Definition. We say that a prediction strategy is continuous, if the associated function $\psi$ is continuous.

Theorem 1 (Vovk). Suppose that $\mathcal{X}$ and $\mathcal{Y}$ are locally compact metric spaces, and $\Gamma$ is a metric space. Suppose that $\lambda$ is continuous and compact-type. Then for any $\varepsilon > 0$, there exists a prediction strategy, such that if $\{ x_t \}$ and $\{ y_t \}$ are precompact,

where $( \tilde{\gamma_t} )_{t \in \mathbb{N}}$ is the sequence of predictions made by any continuous prediction strategy.

Any prediction strategy that achieves the theorem above is called universally consistent.

### Ideas

The prediction strategy proposed by Vovk consists of two parts. Predictor first computes an estimate of the conditional probability distribution of $y_t$ given the history; then Predictor chooses the optimal $\gamma_t$ minimizing the estimated conditional expected loss. Notice that, however, the online prediction protocol is completely deterministic; hence talking about the conditional probability and expected loss, rigorously speaking, is not legal in the sense of measure-theoretic probability.

The first part is called probability forecasting. The protocol of probability forecasting is as follows. For $t = 1, 2, \ldots$,

1. Reality announces $x_t \in \mathcal{X}$.
2. Forecaster announces $P_t \in \Delta ( \mathcal{Y} )$.
3. Reality announces $y_t \in \mathcal{Y}$.

Theorem 2 (Vovk). Suppose that $\mathcal{X}$ and $\mathcal{Y}$ are compact metric spaces. There exists some forecasting strategy, such that

for any continuous function $f: \mathcal{X} \times \Delta ( \mathcal{Y} ) \times \mathcal{Y} \to \mathbb{R}$.

The forecasting strategy behind Theorem 2 is based on the existence of a universal RKHS $\mathcal{H}$ on $\mathcal{X} \times \Delta ( \mathcal{Y} ) \times \mathcal{Y}$, i.e., an RKHS dense in $C ( \mathcal{X} \times \Delta ( \mathcal{Y} ) \times \mathcal{Y} )$. Following the framework of defensive forecasting, one can construct a forecasting strategy such that the inequality in Theorem 2 holds for all functions in $\mathcal{H}$. As $\mathcal{H}$ is dense in $C ( \mathcal{X} \times \Delta ( \mathcal{Y} ) \times \mathcal{Y} )$, one obtains Theorem 2.

Naively speaking, the second part corresponds to finding the randomized action that minimizes the conditional expected loss, i.e., computing

where $P_t$ is the output of probability forecasting, and

Then Predictor chooses $\gamma_t$ randomly according to $\tilde{Q}_t$.

Theorem 2 considers continuous functions, while $\tilde{Q}_t$ may not be continuously dependent on $(x_t, P_t)$. Therefore, we need to slightly modify the second part. What Predictor actually does in the second part is to compute $Q_t := G ( x_t, P_t ) \in \Delta ( \Gamma )$, for some continuous function $G$ satisfying

for some $\delta > 0$.

Lemma 1 (Vovk). Suppose that $\Gamma$ is a compact convex subset of a topological vector space. Such a continuous function $G$ exists for any $\delta > 0$.

In Theorem 1, the conditions on $\mathcal{X}$, $\mathcal{Y}$, and $\Gamma$ are not as strict as those in Theorem 2 and Lemma 1. This gap can be addressed by a doubling-like trick. Roughly speaking, Predictor starts with compact subsets $A \subseteq \mathcal{X}$ and $B \subseteq \mathcal{Y}$, and a compact convex subset $C \subseteq \Gamma$. Predictor implements the prediction strategy described above only on $A$, $B$, and $C$. If Reality announces some $( x_t, y_t ) \notin A \times B$, Predictor enlarges $A$, $B$ and $C$ such that $( x_t, y_t )$ is contained.

1. If Predictor is forced to enlarge the sets for inifinitely many times, $\{ x_t \}$ and $\{ y_t \}$ cannot be both precompact.
2. Otherwise, ultimately Predictor is working with some $A \subseteq \mathcal{X}$, $B \subseteq \mathcal{Y}$, and $C \subseteq \Gamma$, to which Theorem 2 and Lemma 1 apply.