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Found 18 papers, 2 papers with code
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
In this paper, we give query and regret optimal bandit algorithms under the strict notion of strongly adaptive regret, which measures the maximum regret over any contiguous interval $I$.
Non-uniform Online Learning: Towards Understanding Induction
This setting assumes a predetermined ground-truth hypothesis and considers non-uniform, hypothesis-wise error bounds.
On the Computational Benefit of Multimodal Learning
Specifically, we present a learning task that is NP-hard for unimodal learning but is solvable in polynomial time by a multimodal algorithm.
A Theory of Multimodal Learning
Human perception of the empirical world involves recognizing the diverse appearances, or 'modalities', of underlying objects.
Projection-free Adaptive Regret with Membership Oracles
The most common approach is based on the Frank-Wolfe method, that uses linear optimization computation in lieu of projections.
On the Computational Efficiency of Adaptive and Dynamic Regret Minimization
In online convex optimization, the player aims to minimize regret, or the difference between her loss and that of the best fixed decision in hindsight over the entire repeated game.
Adaptive Online Learning of Quantum States
In the fundamental problem of shadow tomography, the goal is to efficiently learn an unknown $d$-dimensional quantum state using projective measurements.
Non-convex online learning via algorithmic equivalence
We prove an $O(T^{\frac{2}{3}})$ regret bound for non-convex online gradient descent in this setting, answering this open problem.
Adaptive Gradient Methods with Local Guarantees
Adaptive gradient methods are the method of choice for optimization in machine learning and used to train the largest deep models.
Tight lower bounds for Differentially Private ERM
We consider the lower bounds of differentially private ERM for general convex functions.
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
The best known lower bounds, however, are worse than the upper bounds by a factor of $\log T$.
Lower Bounds for Differentially Private ERM: Unconstrained and Non-Euclidean
We consider the lower bounds of differentially private empirical risk minimization (DP-ERM) for convex functions in constrained/unconstrained cases with respect to the general $\ell_p$ norm beyond the $\ell_2$ norm considered by most of the previous works.
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
This directly provides a rigorous guarantee of the certified robustness based on the margin of prediction outputs.
A Note on the Representation Power of GHHs
In this note we prove a sharp lower bound on the necessary number of nestings of nested absolute-value functions of generalized hinging hyperplanes (GHH) to represent arbitrary CPWL functions.
A Tight Lower Bound for Uniformly Stable Algorithms
In this paper we fill the gap by proving a tight generalization lower bound of order $\Omega(\gamma+\frac{L}{\sqrt{n}})$, which matches the best known upper bound up to logarithmic factors
A Note on John Simplex with Positive Dilation
We prove a Johns theorem for simplices in $R^d$ with positive dilation factor $d+2$, which improves the previously known $d^2$ upper bound.
Boosting for Control of Dynamical Systems
We study the question of how to aggregate controllers for dynamical systems in order to improve their performance.
The Expressive Power of Neural Networks: A View from the Width
That is, there are classes of deep networks which cannot be realized by any shallow network whose size is no more than an exponential bound.
