Posters

Chain of Preference Optimization (CPO) dramatically improves LLM reasoning by leveraging ToT’s search tree for efficient fine-tuning, achieving similar or better performance with significantly reduced…

Chain-of-Agents (CoA) framework uses multi-agent collaboration to efficiently process long contexts for LLMs, significantly improving performance on various tasks.

Accelerate DEQ certification up to 7x with Serialized Random Smoothing (SRS), achieving certified robustness on large-scale datasets without sacrificing accuracy.

This paper introduces a novel machine unlearning method using projected noisy stochastic gradient descent, providing the first approximate unlearning guarantee under convexity, significantly improving…

CemiFace: Generating high-quality synthetic facial data for robust face recognition, while addressing privacy concerns.

CE-NAS: A novel framework minimizes the carbon footprint of Neural Architecture Search by dynamically allocating GPU resources based on predicted carbon intensity, achieving state-of-the-art results w…

CausalStock: A novel framework for accurate news-driven multi-stock movement prediction, using lag-dependent causal discovery and LLMs for enhanced noise reduction and explainability.

CausalDiff leverages causal inference and diffusion models to create a robust AI defense against unseen adversarial attacks, significantly outperforming state-of-the-art methods.

Causal assumptions drastically alter predictor merging, with CMAXENT revealing logistic regression for causal and LDA for anticausal directions.

CtrlNS: A novel framework for causal temporal representation learning tackles the challenge of nonstationary time series by leveraging sparse transition assumptions, achieving improved accuracy in ide…

LLMs surprisingly master complex logic puzzles like Sudoku and Zebra puzzles after training on strategically ordered solution steps, revealing hidden reasoning abilities.

Researchers introduce a non-parametric causal inference framework to analyze closed-loop optogenetics designs, revealing previously hidden causal effects of neural circuit manipulations on behavior.

This paper presents novel causal imitation learning algorithms using partial identification to achieve expert performance even when unobserved confounders affect Markov Decision Processes.

This paper introduces a novel algorithm to accurately compute causal effects within specific sub-populations, even when hidden factors influence the data, advancing causal inference significantly.

CASCADE algorithm unveils hidden causal structures in event sequences by minimizing description length, surpassing existing Granger causality-based methods.

Causal Dependence Plots (CDPs) visualize how machine learning model predictions causally depend on input features, overcoming limitations of existing methods that ignore causal relationships.

CaPaint: a novel causal spatio-temporal prediction framework that uses causal reasoning and diffusion inpainting to boost model accuracy and generalizability, especially in data-scarce settings.

Causal CPC: a novel method for accurate and efficient counterfactual regression over time using RNNs, CPC, and InfoMax, achieving state-of-the-art performance.

Learned image compression gets a boost with a novel Causal Context Adjustment Loss, improving efficiency without sacrificing quality.

Statistical Flow Matching (SFM) uses information geometry to create a new flow-matching framework for generating discrete data, achieving superior sampling quality and likelihood compared to existing …