This paper studies the tradeoffs between serial runtime and compute efficiency of stochastic momentum methods for linear regression.

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.

This paper studies the efficiency of allocating indivisible items with multiple attributes to agents with additive valuations.

The paper provides a general lower bound on the noisy query complexity of Boolean functions in the noisy query model.

This paper improves the upper bound for the (μ+1) EA on the Binary Value function BinVal.

The paper proposes a general approach to augmenting exact exponential-time algorithms for NP-hard problems using machine-learned predictions.

The paper develops a new framework for proving lower bounds for the maximum matching problem in the semi-streaming model, improving upon the previous best known bounds.

This paper reviews models of inexact computational oracles in smooth continuous optimization and their implications for optimization algorithms.

The paper proves a lower bound on the number of value-oracle queries required for a certain type of approximation algorithm.

This paper proves space lower bounds for entropy-efficient random sampling using i.i.d. uniform bits.