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Found 19 papers, 1 papers with code
Improving Legal Case Retrieval with Brain Signals
Traditional feedback signal such as clicks is too coarse to use as they do not reflect any fine-grained relevance information.
Evaluation Ethics of LLMs in Legal Domain
In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains.
Gender Biased Legal Case Retrieval System on Users' Decision Process
Gender of the defendants in both the task and relevant cases was edited to statistically measure the effect of gender bias in the legal case search results on participants' perceptions.
Quantum Speedup for Spectral Approximation of Kronecker Products
Given its widespread application in machine learning and optimization, the Kronecker product emerges as a pivotal linear algebra operator.
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years.
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs).
Revisiting Quantum Algorithms for Linear Regressions: Quadratic Speedups without Data-Dependent Parameters
However, the running times of these algorithms depend on some quantum linear algebra-related parameters, such as $\kappa(A)$, the condition number of $A$.
Investigating the Influence of Legal Case Retrieval Systems on Users' Decision Process
Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand.
Fast Quantum Algorithm for Attention Computation
It is well-known that quantum machine has certain computational advantages compared to the classical machine.
A General Algorithm for Solving Rank-one Matrix Sensing
In this paper, we relax that rank-$k$ assumption and solve a much more general matrix sensing problem.
Diverse legal case search
In the process of searching, legal practitioners often need the search results under several different causes of cases as reference.
Convergence and Generalization of Wide Neural Networks with Large Bias
This work studies training one-hidden-layer overparameterized ReLU networks via gradient descent in the neural tangent kernel (NTK) regime, where the networks' biases are initialized to some constant rather than zero.
Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants
We also prove a $1/\epsilon^{1-o(1)}$ quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in $\epsilon$.
Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits
As an application, we give a quantum algorithm for zeroth-order stochastic convex bandits with $\tilde{O}(n^{5}\log^{2} T)$ regret, an exponential speedup in $T$ compared to the classical $\Omega(\sqrt{T})$ lower bound.
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time
We consider the problem of training a multi-layer over-parametrized neural network to minimize the empirical risk induced by a loss function.
Does Preprocessing Help Training Over-parameterized Neural Networks?
The classical training method requires paying $\Omega(mnd)$ cost for both forward computation and backward computation, where $m$ is the width of the neural network, and we are given $n$ training points in $d$-dimensional space.
InstaHide’s Sample Complexity When Mixing Two Private Images
Inspired by InstaHide challenge [Huang, Song, Li and Arora'20], [Chen, Song and Zhuo'20] recently provides one mathematical formulation of InstaHide attack problem under Gaussian images distribution.
Symmetric Sparse Boolean Matrix Factorization and Applications
As this problem is hard in the worst-case, we study a natural average-case variant that arises in the context of these reconstruction attacks: $\mathbf{M} = \mathbf{W}\mathbf{W}^{\top}$ for $\mathbf{W}$ a random Boolean matrix with $k$-sparse rows, and the goal is to recover $\mathbf{W}$ up to column permutation.
InstaHide's Sample Complexity When Mixing Two Private Images
On the current InstaHide challenge setup, where each InstaHide image is a mixture of two private images, we present a new algorithm to recover all the private images with a provable guarantee and optimal sample complexity.
