Search Results for author: Bhaskar Mitra

Found 60 papers, 14 papers with code

Sociotechnical Implications of Generative Artificial Intelligence for Information Access

no code implementations19 May 2024 Bhaskar Mitra, Henriette Cramer, Olya Gurevich

Robust access to trustworthy information is a critical need for society with implications for knowledge production, public health education, and promoting informed citizenry in democratic societies.

Information Retrieval Retrieval

Synthetic Test Collections for Retrieval Evaluation

no code implementations13 May 2024 Hossein A. Rahmani, Nick Craswell, Emine Yilmaz, Bhaskar Mitra, Daniel Campos

Previous studies demonstrate that LLMs have the potential to generate synthetic relevance judgments for use in the evaluation of IR systems.

Information Retrieval Retrieval

Towards Group-aware Search Success

no code implementations26 Apr 2024 Haolun Wu, Bhaskar Mitra, Nick Craswell

Traditional measures of search success often overlook the varying information needs of different demographic groups.

Search and Society: Reimagining Information Access for Radical Futures

no code implementations26 Mar 2024 Bhaskar Mitra

Information retrieval (IR) technologies and research are undergoing transformative changes.

Ethics Fairness +1

Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models

no code implementations28 Feb 2024 Dewei Wang, Bhaskar Mitra, Sameer Nekkalapu, Sohom Datta, Bibi Matthew, Rounak Meyur, Heng Wang, Slaven Kincic

As the power system continues to be flooded with intermittent resources, it becomes more important to accurately assess the role of hydro and its impact on the power grid.

Learning to Extract Structured Entities Using Language Models

no code implementations6 Feb 2024 Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text.

DiSK: A Diffusion Model for Structured Knowledge

no code implementations8 Dec 2023 Ouail Kitouni, Niklas Nolte, James Hensman, Bhaskar Mitra

We introduce Diffusion Models of Structured Knowledge (DiSK) - a new architecture and training approach specialized for structured data.

Imputation Inductive Bias

Gaps in Representations of Hydropower Generation in Steady-State and Dynamic Models

no code implementations6 Nov 2023 Bhaskar Mitra, Sohom Datta, Slaven Kincic, Nader Samaan, Abhishek Somani

In the evolving power system, where new renewable resources continually displace conventional generation, conventional hydropower resources can be an important asset that helps to maintain reliability and flexibility.

Large language models can accurately predict searcher preferences

1 code implementation19 Sep 2023 Paul Thomas, Seth Spielman, Nick Craswell, Bhaskar Mitra

It takes careful feedback from real users, which by definition is the highest-quality first-party gold data that can be derived, and develops an large language model prompt that agrees with that data.

Language Modelling Large Language Model

Patterns of gender-specializing query reformulation

no code implementations25 Apr 2023 Amifa Raj, Bhaskar Mitra, Nick Craswell, Michael D. Ekstrand

There are many ways a query, the search results, and a demographic attribute such as gender may relate, leading us to hypothesize different causes for these reformulation patterns, such as under-representation on the original result page or based on the linguistic theory of markedness.

Attribute

Recall, Robustness, and Lexicographic Evaluation

1 code implementation22 Feb 2023 Fernando Diaz, Bhaskar Mitra

In light of this debate, we reflect on the measurement of recall in rankings from a formal perspective.

Fairness Information Retrieval +2

Taking Search to Task

no code implementations12 Jan 2023 Chirag Shah, Ryen W. White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, Nicholas Belkin

For decades, scholars made a case for the role that a user's task plays in how and why that user engages in search and what a search system should do to assist.

Information Retrieval Retrieval

Result Diversification in Search and Recommendation: A Survey

1 code implementation29 Dec 2022 Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.

Retrieval

Analyzing Distribution Transformer Degradation with Increased Power Electronic Loads

no code implementations26 Oct 2022 Bhaskar Mitra, Ankit Singhal, Soumya Kundu, James P. Ogle

To have a good understanding of current standing challenges, a knowledge of the generation and load mix as well as the current harmonic estimations are essential for designing transformers and evaluating their performance.

Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems

no code implementations8 Sep 2022 Ida Larsen-Ledet, Bhaskar Mitra, Siân Lindley

When these knowledge bases begin to actively bring attention to people and the content they work on, especially as that work is still ongoing, we run into important challenges at the intersection of work and the social.

Management Recommendation Systems

Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems

no code implementations26 Jun 2022 Sebastian Hofstätter, Nick Craswell, Bhaskar Mitra, Hamed Zamani, Allan Hanbury

Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems.

Retrieval

Strategies to Maintain Voltage on Long, Lightly Loaded Feeders with Widespread Residential Level 2 Plug-in Electric Vehicle Charging

no code implementations11 Jun 2022 Don Scoffield, John Smart, Timothy Pennington, C. Birk Jones, Matthew Lave, Anudeep Medam, Bhaskar Mitra

Long, lightly loaded feeders serving residential loads may begin to experience voltage excursions as plug-in electric vehicle (PEV) penetration increases.

Joint Multisided Exposure Fairness for Recommendation

1 code implementation29 Apr 2022 Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu

Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.

Exposure Fairness Information Retrieval +2

Bayesian Ridge Regression Based Model to Predict Fault Location in HVdc Network

no code implementations26 Feb 2022 Timothy Flavin, Thomas Steiner, Bhaskar Mitra, Vidhyashree Nagaraju

This paper discusses a method for accurately estimating the fault location in multi-terminal High Voltage direct current (HVdc) transmission network using single ended current and voltage measurements.

regression

Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?

no code implementations21 Jan 2022 Gabriella Kazai, Bhaskar Mitra, Anlei Dong, Nick Craswell, Linjun Yang

This raises questions about when such summaries are sufficient for relevance estimation by the ranking model or the human assessor, and whether humans and machines benefit from the document's full text in similar ways.

Information Retrieval Retrieval

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

no code implementations20 Jan 2022 Timothy Flavin, Bhaskar Mitra, Vidhyashree Nagaraju, Rounak Meyur

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process.

Ensemble Learning

Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness

no code implementations15 Oct 2021 Nicola Neophytou, Bhaskar Mitra, Catherine Stinson

We find statistically significant differences in recommender performance by both age and gender.

Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking

1 code implementation20 May 2021 Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani, Nick Craswell, Allan Hanbury

An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e. g., BERT - to evaluate all individual passages in the document and then aggregating the outputs by pooling or additional Transformer layers.

Document Ranking Knowledge Distillation +1

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models

1 code implementation10 May 2021 Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff

In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query.

Retrieval

MS MARCO: Benchmarking Ranking Models in the Large-Data Regime

no code implementations9 May 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin

Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field.

Benchmarking

Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation

no code implementations6 May 2021 Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu

To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.

Fairness Recommendation Systems

Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence

no code implementations19 Apr 2021 Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less effective) alternative to other Transformer-based architectures that employ (i) large-scale pretraining (high training cost), (ii) joint encoding of query and document (high inference cost), and (iii) larger number of Transformer layers (both high training and high inference costs).

Document Ranking Retrieval

TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime

no code implementations19 Apr 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees, Ian Soboroff

The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available.

Selection bias

Overview of the TREC 2020 deep learning track

1 code implementation15 Feb 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos

This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime.

Passage Retrieval Retrieval

Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification

no code implementations18 Jan 2021 Jaime Arguello, Adam Ferguson, Emery Fine, Bhaskar Mitra, Hamed Zamani, Fernando Diaz

Using movie search as a case study, we explore the characteristics of questions posed by searchers in TOT states in a community question answering website.

Community Question Answering Information Retrieval +1

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

no code implementations21 Dec 2020 Bhaskar Mitra

In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents.

Information Retrieval Retrieval +2

Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track

no code implementations14 Nov 2020 Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell

We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track.

Retrieval

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

no code implementations18 Aug 2020 Jason Ingyu Choi, Surya Kallumadi, Bhaskar Mitra, Eugene Agichtein, Faizan Javed

Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce.

Conformer-Kernel with Query Term Independence for Document Retrieval

1 code implementation20 Jul 2020 Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell

In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption.

Retrieval

ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search

no code implementations9 Jun 2020 Nick Craswell, Daniel Campos, Bhaskar Mitra, Emine Yilmaz, Bodo Billerbeck

Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval.

Information Retrieval Retrieval

Analyzing and Learning from User Interactions for Search Clarification

no code implementations30 May 2020 Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell, Susan T. Dumais

We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback.

Re-Ranking Retrieval

Local Self-Attention over Long Text for Efficient Document Retrieval

1 code implementation11 May 2020 Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra, Nick Craswell, Allan Hanbury

In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window.

Document Ranking Retrieval

On the Reliability of Test Collections for Evaluating Systems of Different Types

no code implementations28 Apr 2020 Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Daniel Campos

As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their quality.

Fairness Information Retrieval +2

Evaluating Stochastic Rankings with Expected Exposure

no code implementations27 Apr 2020 Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query.

Information Retrieval Retrieval

Fault Location Using the Natural Frequency of Oscillation of Current Discharge in MTdc Networks

no code implementations13 Apr 2020 Bhaskar Mitra, Suman Debanth, Badrul Chowdhury

A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established in this paper.

Overview of the TREC 2019 deep learning track

2 code implementations17 Mar 2020 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees

The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime.

Passage Retrieval Retrieval +1

Duet at TREC 2019 Deep Learning Track

1 code implementation10 Dec 2019 Bhaskar Mitra, Nick Craswell

This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019.

Learning-To-Rank Passage Retrieval +1

An Axiomatic Approach to Regularizing Neural Ranking Models

no code implementations15 Apr 2019 Corby Rosset, Bhaskar Mitra, Chenyan Xiong, Nick Craswell, Xia Song, Saurabh Tiwary

The training of these models involve a search for appropriate parameter values based on large quantities of labeled examples.

Information Retrieval Retrieval

An Updated Duet Model for Passage Re-ranking

1 code implementation18 Mar 2019 Bhaskar Mitra, Nick Craswell

We propose several small modifications to Duet---a deep neural ranking model---and evaluate the updated model on the MS MARCO passage ranking task.

Passage Ranking Passage Re-Ranking +1

Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

no code implementations9 May 2018 Daniel Cohen, Bhaskar Mitra, Katja Hofmann, W. Bruce Croft

We use an adversarial discriminator and train our neural ranking model on a small set of domains.

Information Retrieval

Optimizing Query Evaluations using Reinforcement Learning for Web Search

no code implementations12 Apr 2018 Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary

In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked and pruned before being presented to the user.

reinforcement-learning Reinforcement Learning (RL)

Reply With: Proactive Recommendation of Email Attachments

no code implementations17 Oct 2017 Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, Nicola Cancedda

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message.

Weakly-supervised Learning

Toward Incorporation of Relevant Documents in word2vec

no code implementations20 Jul 2017 Navid Rekabsaz, Bhaskar Mitra, Mihai Lupu, Allan Hanbury

As an alternative, explicit word representations propose vectors whose dimensions are easily interpretable, and recent methods show competitive performance to the dense vectors.

Information Retrieval Retrieval +1

Neural Networks for Information Retrieval

no code implementations13 Jul 2017 Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.

Information Retrieval Retrieval

Neural Models for Information Retrieval

no code implementations3 May 2017 Bhaskar Mitra, Nick Craswell

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query.

BIG-bench Machine Learning Information Retrieval +2

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

12 code implementations28 Nov 2016 Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang

The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.

Benchmarking Machine Reading Comprehension +1

Learning to Match Using Local and Distributed Representations of Text for Web Search

1 code implementation Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 Bhaskar Mitra, Fernando Diaz, Nick Craswell

Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space.

Document Ranking Information Retrieval +1

Query Expansion with Locally-Trained Word Embeddings

no code implementations ACL 2016 Fernando Diaz, Bhaskar Mitra, Nick Craswell

Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships.

Ad-Hoc Information Retrieval BIG-bench Machine Learning +3

A Dual Embedding Space Model for Document Ranking

no code implementations2 Feb 2016 Bhaskar Mitra, Eric Nalisnick, Nick Craswell, Rich Caruana

A fundamental goal of search engines is to identify, given a query, documents that have relevant text.

Document Ranking Word Embeddings

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