Alert button
Picture for Krishnateja Killamsetty

Krishnateja Killamsetty

Alert button

Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification

Jun 02, 2023
Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade, Rishabh Iyer

Figure 1 for Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Figure 2 for Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Figure 3 for Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Figure 4 for Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Viaarxiv icon

INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models

May 11, 2023
H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer, Balaji Krishnamurthy

Figure 1 for INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models
Figure 2 for INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models
Figure 3 for INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models
Figure 4 for INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models
Viaarxiv icon

MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning

Feb 05, 2023
Krishnateja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati, Kiran Kate, Lucian Popa, Rishabh Iyer

Figure 1 for MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Figure 2 for MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Figure 3 for MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Figure 4 for MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Viaarxiv icon

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Mar 15, 2022
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti, Alexandre V. Evfimievski, Lucian Popa, Ganesh Ramakrishnan, Rishabh Iyer

Figure 1 for AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Figure 2 for AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Figure 3 for AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Figure 4 for AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Viaarxiv icon

GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning

Nov 18, 2021
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, Pradeep Shenoy

Figure 1 for GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Figure 2 for GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Figure 3 for GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Figure 4 for GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Viaarxiv icon

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

Sep 23, 2021
Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa

Figure 1 for Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Figure 2 for Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Figure 3 for Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Figure 4 for Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Viaarxiv icon

SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios

Jul 01, 2021
Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty, Rishabh Iyer

Figure 1 for SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Figure 2 for SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Figure 3 for SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Figure 4 for SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Viaarxiv icon

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

Jun 14, 2021
Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer

Figure 1 for RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Figure 2 for RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Figure 3 for RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Figure 4 for RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Viaarxiv icon

GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning

Feb 27, 2021
Krishnateja Killamsetty, Durga Sivasubramanian, Baharan Mirzasoleiman, Ganesh Ramakrishnan, Abir De, Rishabh Iyer

Figure 1 for GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning
Figure 2 for GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning
Figure 3 for GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning
Figure 4 for GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning
Viaarxiv icon

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

Jan 15, 2021
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer

Figure 1 for GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Figure 2 for GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Figure 3 for GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Figure 4 for GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Viaarxiv icon