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KDD 2025

The annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share ideas, research results and experiences.

Information and knowledge management

August 3 - 7, 2025

Toronto, Ontario

Website

ICRA 2025

The flagship conference of the IEEE Robotics and Automation Society (RAS), ICRA brings together the world’s top researchers and industry leaders to share ideas, exchange knowledge, and advance the field of robotics for the benefit of humanity.

Robotics

May 19 - 23, 2025

Atlanta, GA

Website

NAACL 2025

The North American Chapter of the Association for Computational Linguistics (NAACL) provides a regional focus for ACL members in North, Central, and South America, organizes annual conferences, promotes cooperation and information exchange among related scientific and professional societies, encourages and facilitates ACL membership by people and institutions in the Americas, and provides a source of information on regional… Read more

Conversational AI

April 29 - May 4, 2025

Albuquerque, New Mexico

Website

The Web Conference 2025

Since the invention of the World Wide Web in 1989, The Web Conference is a yearly international academic conference on the topic of the future direction of the World Wide Web. This conference has been the premier venue to present and discuss progress in research, development, standards, and applications of the topics related to the Web.

Information and knowledge management

April 28 - May 2, 2025

Sydney, Australia

ICLR 2025

The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

Machine learning

April 24 - 28, 2025

Singapore

Website

Training large language models more efficiently

Dhananjay Ram, Nikolaos Pappas

March 27, 2025

Training separate models on different datasets and then merging them reduces computational costs by as much as 91%.

Conversational AI

UTFix: Change aware unit test repairing using LLM

Shanto Rahman, Sachit Kuhar, Berk Cirisci, Pranav Garg, Shiqi Wang, Xiaofei Ma, Anoop Deoras, Baishakhi Ray

OOPSLA 2025

2025

Software updates, including bug repair and feature additions, are frequent in modern applications but they often leave test suites outdated, resulting in undetected bugs and increased chances of system failures. A recent study by Meta revealed that 14%-22% of software failures stem from outdated tests that fail to reflect changes in the codebase. This highlights the need to keep tests in sync with code

Conversational AI

Yuanzhuo (Joy) Liu

Research Scientist

Aparna Sundar

Research Scientist

Parakeet: Emission factor recommendation for life cycle assessments with generative AI

Bharathan Balaji, Fahimeh Ebrahimi, Nina Domingo, Gargeya Vunnava, Abu-Zaher Faridee, Soma Ramalingam, Shikha Gupta, Anran Wang, Harsh Gupta, Domenic Belcastro, Kellen Axten, Jeremie Hakian, Jared Kramer, Aravind Srinivasan, Qingshi Tu

NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning, Environmental Science and Technology

2025

Accurately quantifying greenhouse gas (GHG) emissions is crucial for organizations to measure and mitigate their environmental impact. Life cycle assessment (LCA) estimates the environmental impacts throughout a product’s entire lifecycle, from raw material extraction to end-of-life. Measuring the emissions outside of a product owner’s control is challenging, and practitioners rely on emission factors (

Sustainability

CoLLM: A large language model for composed image retrieval

Chuong Huynh, Jinyu Yang, Ashish Tawari, Son Tran, Raffay Hamid, Trishul Chilimbi, Abhinav Shrivastava

2025

Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplets containing a reference image, a textual description of desired modifications, and the target image, which are expensive and time-consuming to acquire. The scarcity of CIR datasets has led to zero-shot approaches utilizing synthetic triplets or leveraging vision-language

Computer vision

Open domain question answering with conflicting contexts

Siyi Liu, Qiang Ning, Kishaloy Halder, Wei Xiao, Zheng Qi, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth

2025

Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depending on this information may result in untruthful and inaccurate answers. To understand the gravity of this problem, we collect a human-annotated dataset, Question

Conversational AI

Open domain question answering with conflicting contexts

Siyi Liu, Qiang Ning, Kishaloy Halder, Wei Xiao, Zheng Qi, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth

NAACL 2025

2025

Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depending on this information may result in untruthful and inaccurate answers. To understand the gravity of this problem, we collect a human-annotated dataset, Question

Conversational AI

Adapting to online distribution shifts in deep learning: A black-box approach

Dheeraj Baby, Boran Han, Shuai Zhang, Cuixiong Hu, Yuyang (Bernie) Wang, Yu-Xiang Wang

AISTATS 2025

2025

We study the well-motivated problem of online distribution shift in which the data arrive in batches and the distribution of each batch can change arbitrarily over time. Since the shifts can be large or small, abrupt or gradual, the length of the relevant historical data to learn from may vary over time, which poses a major challenge in designing algorithms that can automatically adapt to the best “attention

Machine learning

AnoLLM: Large language models for tabular anomaly detection

Che-Ping Tsai, Ganyu Teng, Phil Wallis, Wei Ding

ICLR 2025

2025

We introduce AnoLLM, a novel framework that leverages large language models (LLMs) for unsupervised tabular anomaly detection. By converting tabular data into a standardized text format, we further adapt a pre-trained LLM with this serialized data, and assign anomaly scores based on the negative log likelihood generated by the LLM. Unlike traditional methods that can require extensive feature engineering

Machine learning

Cross-validating causal discovery via Leave-One-Variable-Out

Daniela Schkoda, Philipp M. Faller, Patrick Blöbaum, Dominik Janzing

CLeaR 2025

2025

We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the causal model. Specifically, given data on X,Y,Z = X,Y,Z1,...,Zk, we apply the causal discovery algorithm separately to the ’leave-one-out’ data sets X,Z and Y,Z. We demonstrate that the two resulting causal models, in the form of

Machine learning

New user event prediction through the lens of causal inference

Henry Shaowu Yuchi, Shixiang Zhu, Li Dong, Yigit Arisoy, Matthew Spencer

AISTATS 2025

2025

Modeling and analysis for event series generated by users of heterogeneous behavioral patterns are closely involved in our daily lives, including credit card fraud detection, online platform user recommendation, and social network analysis. The most commonly adopted approach to this task is to assign users to behavior-based categories and analyze each of them separately. However, this requires extensive

Machine learning

M2AD: Multi-sensor multi-system anomaly detection through global scoring and calibrated thresholding

Sarah Alnegheimish, Zelin He, Matthew Reimherr, Akash Chandrayan, Abhinav Pradhan, Luca D'Angelo

AISTATS 2025

2025

With the widespread availability of sensor data across industrial and operational systems, we frequently encounter heterogeneous time series from multiple systems. Anomaly detection is crucial for such systems to facilitate predictive maintenance. However, most existing anomaly detection methods are designed for either univariate or single-system multivariate data, making them insufficient for these complex

Machine learning

Learning to rewrite negation queries in product search

Mengtian Guo, Mutasem Al-Darabsah, Choon Hui Teo, Jonathan May, Tarun Agarwal, Rahul Bhagat

COLING 2025

2025

In product search, negation is frequently used to articulate unwanted product features or components. Modern search engines often struggle to comprehend negations, resulting in suboptimal user experiences. While various methods have been proposed to tackle negations in search, none of them took the vocabulary gap between query keywords and product text into consideration. In this work, we introduced a query

Search and information retrieval

ELF-Gym: Evaluating large language models generated features for tabular prediction

Yanlin Zhang, Ning Li, Quan Gan, Weinan Zhang, David Paul Wipf, Minjie Wang

KDD 2025

2025

Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, including feature engineering. But despite this potential, evaluations thus far are primarily based on the end performance of a complete ML pipeline, providing

Conversational AI

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