BestHub
Sign in
Tagged articles

causal discovery

7 articles · Page 1 of 1
Kuaishou Tech
Kuaishou Tech
Jun 18, 2026 · Artificial Intelligence

Kuaishou Tech Team Highlights Multiple ICML 2026 Papers Across AI Domains

The Kuaishou technology team reports that several of its papers were accepted at the prestigious ICML 2026 conference—including a spotlight paper on metaphor video understanding, works on causal discovery for irregular time series, image super‑resolution, large‑scale notification dispatch, full‑order ranking, phase‑aware MoE for RL, end‑to‑end e‑commerce search, spatial‑reasoning rewards, a unified SWE benchmark, video temporal grounding, and interpretable transformers—while also inviting attendees to visit their booth B101 in Seoul.

ICML 2026KuaishouLarge Language Models
0 likes · 18 min read
Kuaishou Tech Team Highlights Multiple ICML 2026 Papers Across AI Domains
Machine Heart
Machine Heart
Jun 16, 2026 · Artificial Intelligence

From Bayesian to LLMs: A Comprehensive Survey of Recent Temporal Point Process Advances

This article reviews the rapid evolution of Temporal Point Processes, covering Bayesian non‑parametric models, neural architectures—including RNN, Transformer, and ODE‑based designs—and the emerging LLM‑driven approaches, while discussing training methods, benchmarks, applications, and open research challenges.

Bayesian TPPBenchmarkEvent Modeling
0 likes · 17 min read
From Bayesian to LLMs: A Comprehensive Survey of Recent Temporal Point Process Advances
PaperAgent
PaperAgent
Jan 23, 2026 · Artificial Intelligence

Top AAAI 2026 Papers: New Vision‑Language‑Action Model, LLM2CLIP and More

AAAI 2026 in Singapore showcased 23,680 submissions, highlighting breakthrough papers such as ReconVLA’s reconstructive vision‑language‑action model, LLM2CLIP’s language‑enhanced multimodal representation, a sheaflet‑based hypergraph neural network design, advances in description logic modeling, and a novel causal discovery method for dynamical systems.

AAAI 2026AI PapersLLM
0 likes · 7 min read
Top AAAI 2026 Papers: New Vision‑Language‑Action Model, LLM2CLIP and More
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 1, 2025 · Artificial Intelligence

Recent Time-Series Research Summaries (Oct 25‑31 2025)

This article presents concise summaries of five newly released arXiv papers on time‑series forecasting and causal discovery, highlighting each work’s objectives, proposed methods such as FreLE, selective learning, TempoPFN, and DOTS, and the reported experimental improvements.

Time Series Forecastingcausal discoveryselective learning
0 likes · 8 min read
Recent Time-Series Research Summaries (Oct 25‑31 2025)
DataFunTalk
DataFunTalk
Feb 24, 2024 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article introduces causal learning, explains its distinction from traditional correlation‑based machine learning, outlines its three main parts, discusses the two primary paradigms—learning with known causal graphs and learning via causal discovery—and highlights their advantages, challenges, and recent research directions.

Deep LearningDomain Adaptationcausal discovery
0 likes · 11 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
DataFunSummit
DataFunSummit
Dec 9, 2023 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article reviews the growing interest in causal learning within machine learning, explaining what causal learning is, its advantages over purely correlational methods, and detailing two main paradigms—learning with known causal structures and learning via causal discovery—along with examples, challenges, and future directions.

Deep LearningDomain Adaptationcausal discovery
0 likes · 12 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
DataFunTalk
DataFunTalk
Apr 5, 2023 · Artificial Intelligence

Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings

This article reviews recent developments in causal representation learning, explaining why causal reasoning is essential, describing methods for i.i.d. data, time‑series, and multi‑distribution scenarios, and illustrating applications such as domain adaptation, video analysis, and financial data with numerous examples and visualizations.

Domain Adaptationcausal discoverycausal inference
0 likes · 22 min read
Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings