A note on the price of bandit feedback for mistake-bounded online
learning
- URL: http://arxiv.org/abs/2101.06891v2
- Date: Sun, 31 Jan 2021 21:08:43 GMT
- Title: A note on the price of bandit feedback for mistake-bounded online
learning
- Abstract summary: The standard model and the bandit model are generalizations of the mistake-bound model to online multiclass classification.
In both models the learner guesses a classification in each round, but in the standard model the learner recieves the correct classification after each guess.
We show that this lemma is false precisely when $s$ and $t$ are multiples of each other mod $p$.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The standard model and the bandit model are two generalizations of the
mistake-bound model to online multiclass classification. In both models the
learner guesses a classification in each round, but in the standard model the
learner recieves the correct classification after each guess, while in the
bandit model the learner is only told whether or not their guess is correct in
each round. For any set $F$ of multiclass classifiers, define $opt_{std}(F)$
and $opt_{bandit}(F)$ to be the optimal worst-case number of prediction
mistakes in the standard and bandit models respectively.
Long (Theoretical Computer Science, 2020) claimed that for all $M > 2$ and
infinitely many $k$, there exists a set $F$ of functions from a set $X$ to a
set $Y$ of size $k$ such that $opt_{std}(F) = M$ and $opt_{bandit}(F) \ge (1 -
o(1))(|Y|\ln{|Y|})opt_{std}(F)$. The proof of this result depended on the
following lemma, which is false e.g. for all prime $p \ge 5$, $s = \mathbf{1}$
(the all $1$ vector), $t = \mathbf{2}$ (the all $2$ vector), and all $z$.
Lemma: Fix $n \ge 2$ and prime $p$, and let $u$ be chosen uniformly at random
from $\left\{0, \dots, p-1\right\}^n$. For any $s, t \in \left\{1, \dots,
p-1\right\}^n$ with $s \neq t$ and for any $z \in \left\{0, \dots,
p-1\right\}$, we have $\Pr(t \cdot u = z \mod p \text{ } | \text{ } s \cdot u =
z \mod p) = \frac{1}{p}$.
We show that this lemma is false precisely when $s$ and $t$ are multiples of
each other mod $p$. Then using a new lemma, we fix Long's proof.
Related papers
- Proofs as Explanations: Short Certificates for Reliable Predictions [25.157401709060064]
We consider a model for explainable AI in which an explanation for a prediction $h(x)=y$ consists of a subset $S'$ of the training data.
A set $S'$ of size $d+1$ could be released as an explanation for a positive prediction, and would serve as a short proof of correctness of the prediction under the assumption of realizability.
arXiv Detail & Related papers (2025-04-11T09:26:37Z) - PREM: Privately Answering Statistical Queries with Relative Error [91.98332694700046]
We introduce $mathsfPREM$ (Private Relative Error Multiplicative weight update), a new framework for generating synthetic data that a relative error guarantee for statistical queries under $(varepsilon, delta)$ differential privacy (DP)
We complement our algorithm with nearly matching lower bounds.
arXiv Detail & Related papers (2025-02-20T18:32:02Z) - LevAttention: Time, Space, and Streaming Efficient Algorithm for Heavy Attentions [54.54897832889028]
We show that for any $K$, there is a universal set" $U subset [n]$ of size independent of $n$, such that for any $Q$ and any row $i$, the large attention scores $A_i,j$ in row $i$ of $A$ all have $jin U$.
We empirically show the benefits of our scheme for vision transformers, showing how to train new models that use our universal set while training as well.
arXiv Detail & Related papers (2024-10-07T19:47:13Z) - $\ell_p$-Regression in the Arbitrary Partition Model of Communication [59.89387020011663]
We consider the randomized communication complexity of the distributed $ell_p$-regression problem in the coordinator model.
For $p = 2$, i.e., least squares regression, we give the first optimal bound of $tildeTheta(sd2 + sd/epsilon)$ bits.
For $p in (1,2)$,we obtain an $tildeO(sd2/epsilon + sd/mathrmpoly(epsilon)$ upper bound.
arXiv Detail & Related papers (2023-07-11T08:51:53Z) - Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products [58.05771390012827]
We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm.
Our main result is an algorithm that uses only $tildeO(k/sqrtepsilon)$ matrix-vector products.
arXiv Detail & Related papers (2022-02-10T16:10:41Z) - Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise [9.378684220920562]
tightest statistical query (SQ) lower bounds for learnining halfspaces in the presence of Massart noise.
We show that for arbitrary $eta in [0,1/2]$ every SQ algorithm achieving misclassification error better than $eta$ requires queries of superpolynomial accuracy.
arXiv Detail & Related papers (2022-01-24T17:33:19Z) - Self-training Converts Weak Learners to Strong Learners in Mixture
Models [86.7137362125503]
We show that a pseudolabeler $boldsymbolbeta_mathrmpl$ can achieve classification error at most $C_mathrmerr$.
We additionally show that by running gradient descent on the logistic loss one can obtain a pseudolabeler $boldsymbolbeta_mathrmpl$ with classification error $C_mathrmerr$ using only $O(d)$ labeled examples.
arXiv Detail & Related papers (2021-06-25T17:59:16Z) - Differentially Private Multi-Armed Bandits in the Shuffle Model [58.22098764071924]
We give an $(varepsilon,delta)$-differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model.
Our upper bound almost matches the regret of the best known algorithms for the centralized model, and significantly outperforms the best known algorithm in the local model.
arXiv Detail & Related papers (2021-06-05T14:11:01Z) - Sharper bounds for online learning of smooth functions of a single
variable [0.0]
We show that $opt_1+epsilon(mathcalF_q) = Theta(epsilon-frac12)$, where the constants in the bound do not depend on $q$.
We also show that $opt_1+epsilon(mathcalF_q) = Theta(epsilon-frac12)$, where the constants in the bound do not depend on $q$.
arXiv Detail & Related papers (2021-05-30T23:06:21Z) - The planted matching problem: Sharp threshold and infinite-order phase
transition [25.41713098167692]
We study the problem of reconstructing a perfect matching $M*$ hidden in a randomly weighted $ntimes n$ bipartite graph.
We show that if $sqrtd B(mathcalP,mathcalQ) ge 1+epsilon$ for an arbitrarily small constant $epsilon>0$, the reconstruction error for any estimator is shown to be bounded away from $0$.
arXiv Detail & Related papers (2021-03-17T00:59:33Z) - Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization [51.23789922123412]
We study online learning with bandit feedback (i.e. learner has access to only zeroth-order oracle) where cost/reward functions admit a "pseudo-1d" structure.
We show a lower bound of $min(sqrtdT, T3/4)$ for the regret of any algorithm, where $T$ is the number of rounds.
We propose a new algorithm sbcalg that combines randomized online gradient descent with a kernelized exponential weights method to exploit the pseudo-1d structure effectively.
arXiv Detail & Related papers (2021-02-15T08:16:51Z) - Learning and Testing Variable Partitions [13.575794982844222]
We show that $mathcalO(k n2)(delta + epsilon)$ can be learned in time $tildemathcalO(n2 mathrmpoly (1/epsilon)$ for any $epsilon > 0$.
We also show that even two-sided testers require $Omega(n)$ queries when $k = 2$.
arXiv Detail & Related papers (2020-03-29T10:12:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.