Covert Quantum Learning: Privately and Verifiably Learning from Quantum Data

Quantum learning from remotely accessed quantum compute and data must address two key challenges: verifying the correctness of data and ensuring the privacy of the learner's data-collection strategies and resulting conclusions. The covert (verifiable) learning model of Canetti and Karchmer (TCC 2021) provides a framework for endowing classical learning algorithms with such guarantees. In this work, we propose models of covert verifiable learning in quantum learning theory and realize them without computational hardness assumptions for remote data access scenarios motivated by established quantum data advantages. We consider two privacy notions: (i) strategy-covertness, where the eavesdropper does not gain information about the learner's strategy; and (ii) target-covertness, where the eavesdropper does not gain information about the unknown object being learned. We show: Strategy-covert algorithms for making quantum statistical queries via classical shadows; Target-covert algorithms for learning quadratic functions from public quantum examples and private quantum statistical queries, for Pauli shadow tomography and stabilizer state learning from public multi-copy and private single-copy quantum measurements, and for solving Forrelation and Simon's problem from public quantum queries and private classical queries, where the adversary is a unidirectional or i.i.d. ancilla-free eavesdropper. The lattermost results in particular establish that the exponential separation between classical and quantum queries for Forrelation and Simon's problem survives under covertness constraints. Along the way, we design covert verifiable protocols for quantum data acquisition from public quantum queries which may be of independent interest. Overall, our models and corresponding algorithms demonstrate that quantum advantages are privately and verifiably achievable even with untrusted, remote data.

The Power of Random Features and the Limits of Distribution-Free Gradient Descent

We study the relationship between gradient-based optimization of parametric models (e.g., neural networks) and optimization of linear combinations of random features. Our main result shows that if a parametric model can be learned using mini-batch stochastic gradient descent (bSGD) without making assumptions about the data distribution, then with high probability, the target function can also be approximated using a polynomial-sized combination of random features. The size of this combination depends on the number of gradient steps and numerical precision used in the bSGD process. This finding reveals fundamental limitations of distribution-free learning in neural networks trained by gradient descent, highlighting why making assumptions about data distributions is often crucial in practice. Along the way, we also introduce a new theoretical framework called average probabilistic dimension complexity (adc), which extends the probabilistic dimension complexity developed by Kamath et al. (2020). We prove that adc has a polynomial relationship with statistical query dimension, and use this relationship to demonstrate an infinite separation between adc and standard dimension complexity.

On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning

In multimodal machine learning, multiple modalities of data (e.g., text and images) are combined to facilitate the learning of a better machine learning model, which remains applicable to a corresponding unimodal task (e.g., text generation). Recently, multimodal machine learning has enjoyed huge empirical success (e.g. GPT-4). Motivated to develop theoretical justification for this empirical success, Lu (NeurIPS '23, ALT '24) introduces a theory of multimodal learning, and considers possible separations between theoretical models of multimodal and unimodal learning. In particular, Lu (ALT '24) shows a computational separation, which is relevant to worst-case instances of the learning task. In this paper, we give a stronger average-case computational separation, where for "typical" instances of the learning task, unimodal learning is computationally hard, but multimodal learning is easy. We then question how "organic" the average-case separation is. Would it be encountered in practice? To this end, we prove that under natural conditions, any given computational separation between average-case unimodal and multimodal learning tasks implies a corresponding cryptographic key agreement protocol. We suggest to interpret this as evidence that very strong computational advantages of multimodal learning may arise infrequently in practice, since they exist only for the "pathological" case of inherently cryptographic distributions. However, this does not apply to possible (super-polynomial) statistical advantages.

Agnostic Membership Query Learning with Nontrivial Savings: New Results, Techniques

(Abridged) Designing computationally efficient algorithms in the agnostic learning model (Haussler, 1992; Kearns et al., 1994) is notoriously difficult. In this work, we consider agnostic learning with membership queries for touchstone classes at the frontier of agnostic learning, with a focus on how much computation can be saved over the trivial runtime of 2^n$. This approach is inspired by and continues the study of ``learning with nontrivial savings'' (Servedio and Tan, 2017). To this end, we establish multiple agnostic learning algorithms, highlighted by: 1. An agnostic learning algorithm for circuits consisting of a sublinear number of gates, which can each be any function computable by a sublogarithmic degree k polynomial threshold function (the depth of the circuit is bounded only by size). This algorithm runs in time 2^{n -s(n)} for s(n) \approx n/(k+1), and learns over the uniform distribution over unlabelled examples on \{0,1\}^n. 2. An agnostic learning algorithm for circuits consisting of a sublinear number of gates, where each can be any function computable by a \sym^+ circuit of subexponential size and sublogarithmic degree k. This algorithm runs in time 2^{n-s(n)} for s(n) \approx n/(k+1), and learns over distributions of unlabelled examples that are products of k+1 arbitrary and unknown distributions, each over \{0,1\}^{n/(k+1)} (assume without loss of generality that k+1 divides n).

Distributional PAC-Learning from Nisan's Natural Proofs

(Abridged) Carmosino et al. (2016) demonstrated that natural proofs of circuit lower bounds for \Lambda imply efficient algorithms for learning \Lambda-circuits, but only over the uniform distribution, with membership queries, and provided \AC^0[p] \subseteq \Lambda. We consider whether this implication can be generalized to \Lambda \not\supseteq \AC^0[p], and to learning algorithms in Valiant's PAC model, which use only random examples and learn over arbitrary example distributions. We give results of both positive and negative flavor. On the negative side, we observe that if, for every circuit class \Lambda, the implication from natural proofs for \Lambda to learning \Lambda-circuits in Valiant's PAC model holds, then there is a polynomial time solution to O(n^{1.5})-uSVP (unique Shortest Vector Problem), and polynomial time quantum solutions to O(n^{1.5})-SVP (Shortest Vector Problem) and O(n^{1.5})-SIVP (Shortest Independent Vector Problem). This indicates that whether natural proofs for \Lambda imply efficient learning algorithms for \Lambda in Valiant's PAC model may depend on \Lambda. On the positive side, our main result is that specific natural proofs arising from a type of communication complexity argument (e.g., Nisan (1993), for depth-2 majority circuits) imply PAC-learning algorithms in a new distributional variant of Valiant's model. Our distributional PAC model is stronger than the average-case prediction model of Blum et al (1993) and the heuristic PAC model of Nanashima (2021), and has several important properties which make it of independent interest, such as being boosting-friendly. The main applications of our result are new distributional PAC-learning algorithms for depth-2 majority circuits, polytopes and DNFs over natural target distributions, as well as the nonexistence of encoded-input weak PRFs that can be evaluated by depth-2 majority circuits.