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Found 11 papers, 1 papers with code
Approaching Human-Level Forecasting with Language Models
In this work, we study whether language models (LMs) can forecast at the level of competitive human forecasters.
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 $I$.
Towards Best Practices of Activation Patching in Language Models: Metrics and Methods
Mechanistic interpretability seeks to understand the internal mechanisms of machine learning models, where localization -- identifying the important model components -- is a key step.
Streaming Algorithms for Learning with Experts: Deterministic Versus Robust
In the online learning with experts problem, an algorithm must make a prediction about an outcome on each of $T$ days (or times), given a set of $n$ experts who make predictions on each day (or time).
Privately Estimating a Gaussian: Efficient, Robust and Optimal
We prove a new lower bound on differentially private covariance estimation to show that the dependence on the condition number $\kappa$ in the above sample bound is also tight.
Optimal Query Complexities for Dynamic Trace Estimation
Specifically, for any $m$ matrices $A_1,..., A_m$ with consecutive differences bounded in Schatten-$1$ norm by $\alpha$, we provide a novel binary tree summation procedure that simultaneously estimates all $m$ traces up to $\epsilon$ error with $\delta$ failure probability with an optimal query complexity of $\widetilde{O}\left(m \alpha\sqrt{\log(1/\delta)}/\epsilon + m\log(1/\delta)\right)$, improving the dependence on both $\alpha$ and $\delta$ from Dharangutte and Musco (NeurIPS, 2021).
Online Prediction in Sub-linear Space
We provide the first sub-linear space and sub-linear regret algorithm for online learning with expert advice (against an oblivious adversary), addressing an open question raised recently by Srinivas, Woodruff, Xu and Zhou (STOC 2022).
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
They provide robustness-consistency trade-offs for a variety of online problems.
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
In this paper, we provide a meta-problem and a duality theorem that lead to a new unified view on robust and heavy-tailed mean estimation in high dimensions.
A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates
The goal is to design an efficient estimator that attains the optimal sub-gaussian error bound, only assuming that the random vector has bounded mean and covariance.
SGD on Neural Networks Learns Functions of Increasing Complexity
We perform an experimental study of the dynamics of Stochastic Gradient Descent (SGD) in learning deep neural networks for several real and synthetic classification tasks.
