Search Results for author:
Found 9 papers, 3 papers with code
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search
However, it is challenging to update such graph index at a high rate, while supporting stable recall after many updates.
Statistical-Computational Trade-offs for Density Estimation
In particular, if an algorithm uses $O(n/\log^c k)$ samples for some constant $c>0$ and polynomial space, then the query time of the data structure must be at least $k^{1-O(1)/\log \log k}$, i. e., close to linear in the number of distributions $k$.
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval
Our experiments demonstrate the efficacy of our approach not only in contradiction retrieval with more than 30% accuracy improvements on MSMARCO and HotpotQA across different model architectures but also in applications such as cleaning corrupted corpora to restore high-quality QA retrieval.
A Bi-metric Framework for Fast Similarity Search
In both cases we show that, as long as the proxy metric used to construct the data structure approximates the ground-truth metric up to a bounded factor, our data structure achieves arbitrarily good approximation guarantees with respect to the ground-truth metric.
Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations
Graph-based approaches to nearest neighbor search are popular and powerful tools for handling large datasets in practice, but they have limited theoretical guarantees.
A Universal Discriminator for Zero-Shot Generalization
In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods.
Embeddings and labeling schemes for A*
In particular, we consider heuristics induced by norm embeddings and distance labeling schemes, and provide lower bounds for the tradeoffs between the number of dimensions or bits used to represent each graph node, and the running time of the A* algorithm.
Simple Combinatorial Algorithms for Combinatorial Bandits: Corruptions and Approximations
Our algorithm achieves an (approximation) regret bound of $\tilde{O}\left(d\sqrt{KT}\right)$.
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap
We further show that for general MDPs, AMB suffers an additional $\frac{|Z_{mul}|}{\Delta_{min}}$ regret, where $Z_{mul}$ is the set of state-action pairs $(s, a)$'s satisfying $a$ is a non-unique optimal action for $s$.
