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Found 11 papers, 0 papers with code
A/B testing under Interference with Partial Network Information
A/B tests are often required to be conducted on subjects that might have social connections.
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances
To generate images with realistic hands, we propose a novel diffusion-based architecture called HanDiffuser that achieves realism by injecting hand embeddings in the generative process.
SODA: Protecting Proprietary Information in On-Device Machine Learning Models
The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications.
Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information
Our work aims to address this concern by introducing a novel approach to detecting adversarial prompts at a token level, leveraging the LLM's capability to predict the next token's probability.
Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training
In this paper, we consider a heavy inner product identification problem, which generalizes the Light Bulb problem~(\cite{prr89}): Given two sets $A \subset \{-1,+1\}^d$ and $B \subset \{-1,+1\}^d$ with $|A|=|B| = n$, if there are exact $k$ pairs whose inner product passes a certain threshold, i. e., $\{(a_1, b_1), \cdots, (a_k, b_k)\} \subset A \times B$ such that $\forall i \in [k], \langle a_i, b_i \rangle \geq \rho \cdot d$, for a threshold $\rho \in (0, 1)$, the goal is to identify those $k$ heavy inner products.
Decentralized Personalized Online Federated Learning
Therefore, we propose a new learning setting \textit{Decentralized Personalized Online Federated Learning} that considers all the three aspects at the same time.
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan.
Privacy Aware Experiments without Cookies
We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences.
DiPS: Differentiable Policy for Sketching in Recommender Systems
In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with.
Optimal Bidding Strategy without Exploration in Real-time Bidding
Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint.
Scalable Bid Landscape Forecasting in Real-time Bidding
The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price).
