Covering Irrep(Sn)Irrep(S_n) With Tensor Products and Powers

We study when a tensor product of irreducible representations of the symmetric group $S_n$ contains all irreducibles as subrepresentations - we say such a tensor product covers $Irrep(S_n)$. Our results show that this behavior is typical. We first give a general criterion for such a tensor product to have this property. Using this criterion we show that the tensor product of a constant number of random irreducibles covers $Irrep(S_n)$ asymptotically almost surely. We also consider, for a fixed irreducible representation, the degree of tensor power needed to cover $Irrep(S_n)$. We show that the simple lower bound based on dimension is tight up to a universal constant factor for every irreducible representation, as was recently conjectured by Liebeck, Shalev, and Tiep

Free Energy Subadditivity for Symmetric Random Hamiltonians

We consider a random Hamiltonian $H:\Sigma\to\mathbb R$ defined on a compact space $\Sigma$ that admits a transitive action by a compact group $\mathcal G$. When the law of $H$ is $\mathcal G$-invariant, we show its expected free energy relative to the unique $\mathcal G$-invariant probability measure on $\Sigma$ obeys a subadditivity property in the law of $H$ itself. The bound is often tight for weak disorder and relates free energies at different temperatures when $H$ is a Gaussian process. Many examples are discussed including branching random walk, several spin glasses, random constraint satisfaction problems, and the random field Ising model. We also provide a generalization to quantum Hamiltonians with applications to the quantum SK and SYK models

Approximate Ground States of Hypercube Spin Glasses are Near Corners

We show that with probability exponentially close to $1$, all near-maximizers of any mean-field mixed $p$-spin glass Hamiltonian on the hypercube $[-1,1]^N$ are near a corner. This confirms a recent conjecture of Gamarnik and Jagannath. The proof is elementary and generalizes to arbitrary polytopes with $e^{o(N^2)}$ faces

Incentivizing Exploration with Linear Contexts and Combinatorial Actions

We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work has shown under certain independence assumptions that after collecting enough initial samples, the popular Thompson sampling algorithm becomes incentive compatible. We give an analog of this result for linear bandits, where the independence of the prior is replaced by a natural convexity condition. This opens up the possibility of efficient and regret-optimal incentivized exploration in high-dimensional action spaces. In the semibandit model, we also improve the sample complexity for the pre-Thompson sampling phase of initial data collection.Comment: International Conference on Machine Learning (ICML) 202

On Size-Independent Sample Complexity of ReLU Networks

We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously Golowich-Rakhlin-Shamir (2020) obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.Comment: 4 page

The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity

We consider incentivized exploration: a version of multi-armed bandits where the choice of arms is controlled by self-interested agents, and the algorithm can only issue recommendations. The algorithm controls the flow of information, and the information asymmetry can incentivize the agents to explore. Prior work achieves optimal regret rates up to multiplicative factors that become arbitrarily large depending on the Bayesian priors, and scale exponentially in the number of arms. A more basic problem of sampling each arm once runs into similar factors. We focus on the price of incentives: the loss in performance, broadly construed, incurred for the sake of incentive-compatibility. We prove that Thompson Sampling, a standard bandit algorithm, is incentive-compatible if initialized with sufficiently many data points. The performance loss due to incentives is therefore limited to the initial rounds when these data points are collected. The problem is largely reduced to that of sample complexity: how many rounds are needed? We address this question, providing matching upper and lower bounds and instantiating them in various corollaries. Typically, the optimal sample complexity is polynomial in the number of arms and exponential in the "strength of beliefs"

Strong Topological Trivialization of Multi-Species Spherical Spin Glasses

We study the landscapes of multi-species spherical spin glasses. Our results determine the phase boundary for annealed trivialization of the number of critical points, and establish its equivalence with a quenched \emph{strong topological trivialization} property. Namely in the "trivial" regime, the number of critical points is constant, all are well-conditioned, and all approximate critical points are close to a true critical point. As a consequence, we deduce that Langevin dynamics at sufficiently low temperature has logarithmic mixing time. Our approach begins with the Kac--Rice formula. We derive closed form expressions for some asymptotic determinants studied in (Ben Arous-Bourgade-McKenna 2023, McKenna 2021), and characterize the annealed trivialization phase by explicitly solving a suitable multi-dimensional variational problem. To obtain more precise quenched results, we develop general purpose techniques to avoid sub-exponential correction factors and show non-existence of \emph{approximate} critical points. Many of the results are new even in the $1$-species case.Comment: 57 pages, 4 figures. Updated reference