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Found 25 papers, 4 papers with code
Limits of KV Cache Compression for Tensor Attention based Autoregressive Transformers
The key-value (KV) cache in autoregressive transformers presents a significant bottleneck during inference, which restricts the context length capabilities of large language models (LLMs).
Fast Gradient Computation for RoPE Attention in Almost Linear Time
The Rotary Position Embedding (RoPE) mechanism has become a powerful enhancement to the Transformer architecture, which enables models to capture token relationships when encoding positional information.
The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity
In this paper, we analyze the computational limitations of Mamba and State-space Models (SSMs) by using the circuit complexity framework.
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation
Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data.
I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction
Visual Language Models (VLMs) are essential for various tasks, particularly visual reasoning tasks, due to their robust multi-modal information integration, visual reasoning capabilities, and contextual awareness.
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning
Reinforcement learning with human feedback (RLHF), as a widely adopted approach in current large language model pipelines, is \textit{bottlenecked by the size of human preference data}.
Decoding-Time Language Model Alignment with Multiple Objectives
Unlike traditional methods that require careful curation of a mixture of datasets to achieve comprehensive improvement, we can quickly experiment with preference weightings using MOD to find the best combination of models.
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning
Three main data selection approaches are: (1) leveraging external non-CLIP models to aid data selection, (2) training new CLIP-style embedding models that are more effective at selecting high-quality data than the original OpenAI CLIP model, and (3) designing better metrics or strategies universally applicable to any CLIP embedding without requiring specific model properties (e. g., CLIPScore is one popular metric).
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning
In recent years, data selection has emerged as a core issue for large-scale visual-language model pretraining, especially on noisy web-curated datasets.
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
To mitigate the annotation cost of SFT and circumvent the computational bottlenecks of active learning, we propose using experimental design.
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive.
Improved Active Multi-Task Representation Learning via Lasso
In addition to our sample complexity results, we also characterize the potential of our $\nu^1$-based strategy in sample-cost-sensitive settings.
Causal Bandits: Online Decision-Making in Endogenous Settings
To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates.
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler
We design a novel algorithmic template, Weak Labeler Active Cover (WL-AC), that is able to robustly leverage the lower quality weak labelers to reduce the query complexity while retaining the desired level of accuracy.
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior
Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival.
Active Multi-Task Representation Learning
To leverage the power of big data from source tasks and overcome the scarcity of the target task samples, representation learning based on multi-task pretraining has become a standard approach in many applications.
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
We first develop a computationally efficient algorithm for reward-free RL in a $d$-dimensional linear MDP with sample complexity scaling as $\widetilde{\mathcal{O}}(d^2 H^5/\epsilon^2)$.
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Obtaining first-order regret bounds -- regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance -- is a core question in sequential decision-making.
Corruption Robust Active Learning
We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions.
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
We study episodic reinforcement learning under unknown adversarial corruptions in both the rewards and the transition probabilities of the underlying system.
More Practical and Adaptive Algorithms for Online Quantum State Learning
This regret bound depends only on the maximum rank $M$ of measurements rather than the number of qubits, which takes advantage of low-rank measurements.
Fair Contextual Multi-Armed Bandits: Theory and Experiments
We introduce a Multi-Armed Bandit algorithm with fairness constraints, where fairness is defined as a minimum rate that a task or a resource is assigned to a user.
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization
We establish sub-linear regret bounds on the proposed notions of regret in both the online and bandit setting.
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates
How should a robot that collaborates with multiple people decide upon the distribution of resources (e. g. social attention, or parts needed for an assembly)?
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret.
