Peter Lofgren
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View article: Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming
Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming Open
Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing i…
View article: Dynamic PageRank: Algorithms and Lower Bounds
Dynamic PageRank: Algorithms and Lower Bounds Open
We consider the PageRank problem in the dynamic setting, where the goal is to explicitly maintain an approximate PageRank vector π ∈ ℝⁿ for a graph under a sequence of edge insertions and deletions. Our main result is a complete characteri…
View article: Approximate Personalized PageRank on Dynamic Graphs
Approximate Personalized PageRank on Dynamic Graphs Open
We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted. Our algorithms are natural dynamic versions of two known local variations of power …
View article: Approximate Personalized PageRank on Dynamic Graphs
Approximate Personalized PageRank on Dynamic Graphs Open
We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted. Our algorithms are natural dynamic versions of two known local variations of power …
View article: Personalized PageRank Estimation and Search
Personalized PageRank Estimation and Search Open
We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional e…
View article: Efficient Algorithms for Personalized PageRank
Efficient Algorithms for Personalized PageRank Open
We present new, more efficient algorithms for estimating random walk scores such as Personalized PageRank from a given source node to one or several target nodes. These scores are useful for personalized search and recommendations on netwo…
View article: Bidirectional PageRank Estimation: From Average-Case to Worst-Case
Bidirectional PageRank Estimation: From Average-Case to Worst-Case Open
We present a new algorithm for estimating the Personalized PageRank (PPR) between a source and target node on undirected graphs, with sublinear running-time guarantees over the worst-case choice of source and target nodes. Our work builds …
View article: Fast Bidirectional Probability Estimation in Markov Models
Fast Bidirectional Probability Estimation in Markov Models Open
We develop a new bidirectional algorithm for estimating Markov chain multi-step transition probabilities: given a Markov chain, we want to estimate the probability of hitting a given target state in $\ell$ steps after starting from a given…
View article: Personalized PageRank Estimation and Search: A Bidirectional Approach
Personalized PageRank Estimation and Search: A Bidirectional Approach Open
We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional e…