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Found 8 papers, 2 papers with code
Contracting with a Learning Agent
We initiate the study of repeated contracts with a learning agent, focusing on agents who achieve no-regret outcomes.
Functional Interpolation for Relative Positions Improves Long Context Transformers
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models.
A Fourier Approach to Mixture Learning
Regev and Vijayaraghavan (2017) showed that with $\Delta = \Omega(\sqrt{\log k})$ separation, the means can be learned using $\mathrm{poly}(k, d)$ samples, whereas super-polynomially many samples are required if $\Delta = o(\sqrt{\log k})$ and $d = \Omega(\log k)$.
Margin-Independent Online Multiclass Learning via Convex Geometry
We consider the problem of multi-class classification, where a stream of adversarially chosen queries arrive and must be assigned a label online.
Learning to Bid in Contextual First Price Auctions
For binary feedback, when the noise distribution $\mathcal{F}$ is known, we propose a bidding algorithm, by using maximum likelihood estimation (MLE) method to achieve at most $\widetilde{O}(\sqrt{\log(d) T})$ regret.
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
Big Bird: Transformers for Longer Sequences
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
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