New algorithms solve convex optimization problems with limited memory.
problem Solving convex optimization problems with constrained memory.
method Recursive cutting-plane algorithms dividing variables into blocks.
result Achieves optimal memory usage and oracle complexity in certain regimes.
Paper settles sample complexity for learning from multiple distributions.
problem Learning from multiple data distributions with a hypothesis class of bounded VC dimension.
method Introduced an algorithm with sample complexity of O((d+k)ε^-2)·(k/ε)^o(1).
result Algorithm matches lower bound up to sub-polynomial factor.
Paper proves convergence rates for Gaussian kernel ridge regression.
problem Understanding convergence rates for Gaussian kernel ridge regression.
method Establishes polynomial convergence rates for KRR with fixed hyperparameters.
result First polynomial convergence rates for Gaussian kernel ridge regression.
The study finds polynomial upper bounds for singularities in Einstein-scalar field system.
problem Understanding the strength of singularities in gravitational collapse.
method Analyzing geometric quantities, focusing on the Kretschmann scalar.
result Polynomial blow-up upper bounds O(1/rN) for the Kretschmann scalar, improving previous bounds. Study shows that ridgeless Gaussian kernel regression overfits even with varying bandwidth or dimensionality.
problem Analyzing overfitting in Gaussian kernel ridgeless regression with varying bandwidth or dimensionality.
method Examined the behavior of minimum norm interpolating solutions for fixed and increasing dimensions under varying bandwidth and sample size.
result Ridgeless solutions are never consistent and can be worse than null predictor with large enough noise, even with varying bandwidth or dimensionality.
Let Mm be a minimal properly immersed submanifold in an ambient space close, in a suitable sense, to the space form Nkn of curvature −k≤0. In this paper, we are interested in the relation between the density function Θ(r) of Mm and the spectrum of the Laplace-Beltrami operator. In particular, …
Deep neural networks can accurately approximate option prices in stochastic volatility models.
problem Approximating option prices in complex stochastic volatility models.
method Use deep neural networks to approximate option prices for a general class of stochastic volatility models.
result Deep neural networks can approximate option prices up to small error ε with sub-polynomial network size growth.
Optimizes sample and round complexity in adaptive sampling from multiple distributions.
problem Adaptive sampling from multiple distributions with limited rounds and samples.
method Introduces OODS framework and analyzes tradeoffs between sample and round complexity.
result Achieves near-optimal sample complexity and sub-polynomial round complexity.
Near-logarithmic regret per switch achieved for mixable/exp-concave losses.
problem Online optimization of mixable loss functions with dynamic environments.
method Online mixture framework using static solvers and hyper-expert creations.
result Near-logarithmic regret per switch with sub-polynomial complexity.
MCMC complexity matches optimization for large n and d.
problem Lack of theoretical understanding of MCMC complexity for large n and d. method Comparison of MCMC, LA, and VI complexities for linear, logistic, and Poisson regression.
result MCMC complexity matches optimization complexity for n≳d. Paper improves algorithms for convex-concave minimax optimization problems.
problem Minimizing convex-concave functions with strong convexity and concavity properties.
method Proposes a new algorithm with improved gradient complexity.
result Improves gradient complexity upper bound for minimax optimization.