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Found 19 papers, 5 papers with code
How to Inverting the Leverage Score Distribution?
Leverage score is a fundamental problem in machine learning and theoretical computer science.
VL-Mamba: Exploring State Space Models for Multimodal Learning
The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.
Ranked #63 on
Visual Question Answering
on MM-Vet
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
Remarkably, GPTFuzz achieves over 90% attack success rates against ChatGPT and Llama-2 models, even with suboptimal initial seed templates.
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable consequence of rationing.
VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation
The performance of the Vision-and-Language Navigation~(VLN) tasks has witnessed rapid progress recently thanks to the use of large pre-trained vision-and-language models.
March in Chat: Interactive Prompting for Remote Embodied Referring Expression
Nevertheless, this poses more challenges than other VLN tasks since it requires agents to infer a navigation plan only based on a short instruction.
HOP+: History-enhanced and Order-aware Pre-training for Vision-and-Language Navigation
To address these problems, we present a history-enhanced and order-aware pre-training with the complementing fine-tuning paradigm (HOP+) for VLN.
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost.
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
In this paper, we propose a novel sketching scheme for the first order method in large-scale distributed learning setting, such that the communication costs between distributed agents are saved while the convergence of the algorithms is still guaranteed.
HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation
Pre-training has been adopted in a few of recent works for Vision-and-Language Navigation (VLN).
Ranked #4 on
Visual Navigation
on R2R
Fast Graph Neural Tangent Kernel via Kronecker Sketching
Given a kernel matrix of $n$ graphs, using sketching in solving kernel regression can reduce the running time to $o(n^3)$.
Iterative Sketching and its Application to Federated Learning
Though most federated learning frameworks only require clients and the server to send gradient information over the network, they still face the challenges of communication efficiency and data privacy.
Fast Sketching of Polynomial Kernels of Polynomial Degree
Recent techniques in oblivious sketching reduce the dependence in the running time on the degree $q$ of the polynomial kernel from exponential to polynomial, which is useful for the Gaussian kernel, for which $q$ can be chosen to be polylogarithmic.
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
By assuming the overparameterizaiton of policy and exploiting the hidden convexity of the problem, we further show that TSIVR-PG converges to global $\epsilon$-optimal policy with $\tilde{\mathcal{O}}(\epsilon^{-2})$ samples.
Oblivious Sketching-based Central Path Method for Solving Linear Programming Problems
In this work, we propose a sketching-based central path method for solving linear programmings, whose running time matches the state of art results [Cohen, Lee, Song STOC 19; Lee, Song, Zhang COLT 19].
Generalized Leverage Score Sampling for Neural Networks
Leverage score sampling is a powerful technique that originates from theoretical computer science, which can be used to speed up a large number of fundamental questions, e. g. linear regression, linear programming, semi-definite programming, cutting plane method, graph sparsification, maximum matching and max-flow.
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.
Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel
Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP).
Scalable Influence Maximization with General Marketing Strategies
In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$.
Social and Information Networks Data Structures and Algorithms
