東京大学 情報理工学系研究科 創造情報学専攻 2013年8月実施 筆記試験 第1問

标签：

• Tokyo-University
• Online-Learning-with-Expert-Advice
• Regret-Bound

Author

itsuitsuki

Description

We consider a weather prediction system in which a single senior predictor predicts a weather probability distribution on the basis of $N$ predictors' prediction results. Below the system is described in details. There are $N$ weather predictors, each of whom outputs a weather probability distribution once a day. Here the weather is a binary random variable taking a value 1 or 0 only (1 means "fine" while 0 means "not fine"). It is assumed that the weather is independent of a day. Let the probability distribution that the $i$-th predictor outputs on the $t$-th day be $P_i^{(t)}(X)$ ($X \in \{1, 0\}$) where we let $0 < P_i^{(t)}(X) < 1$ ($X \in \{1, 0\}$). There is a senior predictor who aggregates the outputs of the $N$ predictors. On the $t$-th day, the senior predictor takes a weighted average over the probability distributions output by the $N$ predictors to output a weather probability distribution $\hat{P}^{(t)}(X)$ ($X \in \{1, 0\}$). Here the weight on the $i$-th predictor on the $t$-th day is denoted as $v_i^{(t)}$ ($\sum_{i=1}^N v_i^{(t)} = 1, v_i(t) > 0$ ($i = 1, \dots, N$)). That is, on the $t$-th day, $\hat{P}^{(t)}(X)$ is given by $\sum_{i=1}^N v_i^{(t)} P_i^{(t)}(X)$ (see Figure 1). On the $t$-th day, after the senior predictor ouputs, the real outcome $x_t \in \{1, 0\}$ of the weather on the day is reported. This process goes on sequentially with respect to $t$. In the above setting, answer the following questions.

(1) Assume that the senior predictor defines the weight $v_i^{(t)}$ of the $i$-th predictor on the $t$-th day so that the ratio of $v_i^{(t)}$s with respect to $i$ is equal to that of their corresponding likelihoods with respect to the sequence of the past $t-1$ data: $x^{t-1} = x_1 \dots x_{t-1}$. That is, when we denote the likelihood of the $i$-th predictor with respect to $x^{t-1}$ as $w_i^{(t-1)}$,

the following equation holds: For each $t$, $v_1^{(t)} : \dots : v_N^{(t)} = w_1^{(t-1)} : \dots : w_N^{(t-1)}.$ The likelihood of the $i$-th predictor with respect to $x^{t-1}$ is calculated as $w_i^{(t-1)} = \prod_{j=1}^{t-1} P_i^{(j)}(X = x_j)$, where we denote $P_i^{(j)}(X = x_j)$ as $P_i^{(j)}(x_j)$ and we set $P_i^{(0)}(x_0) = 1$ ($i = 1, \dots, N$). In this setting, for each $i$, show a relation between $w_i^{(t)}$ and $w_i^{(t-1)}$, and derive a formula for calculating $v_i^{(t+1)}$ using $w_i^{(t-1)}$ and $P_i^{(t)}(x_t)$ ($i = 1, \dots, N$).

(2) Suppose that the senior predictor predicts the weather probability distribution sequentially with respect to $t$ for $T$ days. Then under the setting of $v_i^{(t)}$ as in (1), write an algorithm for the senior predictor to output a weather probability distribution and update the weights for predictors every day, and show the order of its computation time in terms of $N$ and $T$. Here the initial weight for each predictor is set as follows: $v_1^{(0)} = \dots = v_N^{(0)} = 1/N.$

(3) When the prediction is sequentially made for $T$ days, we define the cumulative predictive loss for the senior predictor with respect to the sequence $x^T = x_1 \dots x_T$ of observed real coutcome as follows: $Loss(x^T) = \sum_{t=1}^T \left( -\log \hat{P}^{(t)}(x_t) \right).$ Then write $Loss(x^T)$ as a function of $P_i^{(t)}(x_t)$ ($i = 1, \dots, N, t = 1, \dots, T$) and $N$. Here the logarithm is the natural logarithm.

(4) Prove that the senior predictor's cumulative loss for $T$ days defined in (3) is at most $\log N$ larger than the least cumulative loss over all $i$-th predictors for $T$ days. Here the cumulative loss for the $i$-th predictor for $T$ days is defined as $\sum_{t=1}^T (-\log P_i^{(t)}(x_t))$ ($i = 1, \dots, N$).

题目描述

考虑一个在线天气预测系统：(N) 名预测者每天各输出一次二元天气 (X\in{1,0}) 的概率分布，其中 1 表示晴、0 表示非晴，且假定不同日期的天气相互独立。第 (i) 名预测者在第 (t) 天输出 (P_i^{(t)}(X))，对两个取值均有 (0<P_i^{(t)}(X)<1)。

一名高级预测者汇总这 (N) 个结果。第 (t) 天给第 (i) 人的权重为 (v_i^{(t)}>0)，且 (\sum_{i=1}^Nv_i^{(t)}=1)，输出加权混合分布 [ \hat P^{(t)}(X)=\sum_{i=1}^Nv_i^{(t)}P_i^{(t)}(X). ] 高级预测者输出后，才公布当天真实结果 (x_t\in{1,0})，上述过程随 (t) 依次进行。

1. 权重之比等于各预测者对过去 (t-1) 天结果序列 (x^{t-1}=x_1\cdots x_{t-1}) 的似然之比： [ v_1^{(t)}:\cdots:v_N^{(t)} =w_1^{(t-1)}:\cdots:w_N^{(t-1)}, ] 其中 [ w_i^{(t-1)}=\prod_{j=1}^{t-1}P_i^{(j)}(X=x_j), ] 简记 (P_i^{(j)}(X=x_j)) 为 (P_i^{(j)}(x_j))，并令 (P_i^{(0)}(x_0)=1)。对每个 (i)，写出 (w_i^{(t)}) 与 (w_i^{(t-1)}) 的关系，并推导用 (w_i^{(t-1)}) 和 (P_i^{(t)}(x_t)) 计算 (v_i^{(t+1)}) 的公式。
2. 连续预测 (T) 天，按第 1 问的权重规则写出高级预测者每天输出混合概率并更新各预测者权重的算法，分析关于 (N,T) 的时间复杂度。初始权重均为 [ v_1^{(0)}=\cdots=v_N^{(0)}=\frac1N. ]
3. 对真实序列 (x^T=x_1\cdots x_T)，定义高级预测者的累计对数损失 [ Loss(x^T)=\sum_{t=1}^T-\log\hat P^{(t)}(x_t). ] 用所有 (P_i^{(t)}(x_t))（(i=1,\ldots,N;\ t=1,\ldots,T)）及 (N) 表示 (Loss(x^T))，其中 (\log) 为自然对数。
4. 第 (i) 名预测者的 (T) 日累计损失为 [ \sum_{t=1}^T-\log P_i^{(t)}(x_t). ] 证明高级预测者的累计损失至多比所有单个预测者中最小的累计损失多 (\log N)。

考点

• 专家建议下的在线学习：按历史似然对专家赋权，逐日形成混合概率并用新观测乘法更新权重。
• 对数损失与遗憾界：利用混合模型似然的乘积展开和对数，把总损失写成专家似然之和，并证明相对最佳专家的额外损失不超过 (\log N)。