The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and Berkeley DeepDrive. The seminars are every Thursday afternoon, from 2:10P-3P, and are open to BAIR faculty, students, and sponsors. Talks will take place in person on the 8th floor of Berkeley Way West (pending any new guidelines from the CDC and the University). There will also be a link to join via Zoom in case you are unable to attend in person. For any questions, please email bair-admin@berkeley.edu .
Spring 2022 Schedule
Date Speaker 1: 2:10 PM -3:00 PM or 2:10-2:30 PM Speaker 2: N/A or 2:30-2:50 PM Jan 20 Nick Rhinehart : Decision-Centric Predictive Understanding: Enabling Learning Systems to See and Affect the FutureN/A Jan 27 Andrea Bajcsy : Bridging Safety and Learning in Human-Robot InteractionN/A Feb 3 Xinyun Chen : Learning-Based Program Synthesis N/AFeb 10 Lydia Liu : Rethinking the Machine Learning Life Cycle for Social Good: Education as a Case StudyN/A Feb 17 Chenguang Wang : Towards Trustworthy Knowledge Sharing of Language Models N/AFeb 24 Ashvin Nair : Scalable Robot LearningN/A Mar 3 Jiachen Li : Towards Interactive Autonomy with Relational Reasoning N/AMar 10 Niloufar Salehi : Human-Centered Machine Translation N/AMar 17 Stephen James : Efficient Learning for Visual Robotic Manipulation N/AMar 31 Yunhui Gao : Learning and Visualization in Hyperbolic Space N/AApr 7 Norman Mu: SLIP: Self-supervision meets Language-Image Pre-training N/AApr 14 Smitha Milli : Learning Objective Functions from Many, Diverse Signals N/AApr 21 Kush Bhatia : Learning when objectives are hard to specify N/AApr 28 Frederik Ebert : Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets N/AMay 5 Erin Grant : Computational investigations into human and machine intelligenceN/A
Fall 2021 Schedule
Date Speaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AM Speaker 2: N/A or 11:30-11:50 AM Aug 31 Priya Moorjani : Big Data and Genomics: Insights into Human Evolutionary HistoryN/A Sep 7 Christian Borgs: Network Structure and Epidemics N/A Sep 14 Wenshuo Guo : Towards Adaptive and Robust Learning in Data-Driven Mechanism DesignN/A Sep 21 Ashish Kumar: Rapid Motor Adaptation for Legged Robots N/A Sep 28 Tijana Zrnic: Who leads and who follows in strategic classification? N/A Oct 5 Assaf Shocher : Deep Internal Learning N/AOct 12 Zhongqi Miao: Deep learning and real-world datasets N/A Oct 19 Kirthevasan Kandasamy : Mechanism Design under Bandit Feedback with Unknown Agent PreferencesN/A Oct 26 Jacob Steinhardt : Science of Measurement in Machine LearningN/A Nov 2 Yixin Wang : Representation Learning: A Causal PerspectiveN/A Nov 9 Anil Aswani : Engineering Health Systems using Artificial Intelligence N/ANov 16 Natasha Jaques : Social Reinforcement LearningN/A
Spring 2021 Schedule
Date Speaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AM Speaker 2: N/A or 11:30-11:50 AM Jan 26 Hany Farid : Forensic Photographic IdentificationN/A Feb 2 Stephen Bates : Distribution-Free, Risk-Controlling Prediction SetsN/A Feb 9 Abhishek Gupta : Algorithms and Systems for Real World Robotic LearningN/A Feb 16 Glen Berseth : Developing Autonomous Agents to Learn and Plan in the Real WorldN/A Feb 23 Forrest Laine : Computing Feedback Nash Equilibria in Robotic GamesN/A Mar 2 Ellen Novoseller : Online Learning from Human Preference Feedback and Multi-Fidelity Models with Application to Assistive ExoskeletonsN/A Mar 9 Amir Gholami : Systematic Neural Network Pruning and QuantizationColorado Reed : Practical Self-Supervised LearningMar 16 N/A TBD Mar 23 N/A N/A Mar 30 Adam Gleave : Understanding and Evaluating Learned Reward FunctionsN/A Apr 6 Jiachen Li : Relational Reasoning for Multi-Agent SystemsN/A Apr 13 Taesung Park : Machine Learning for Deep Image ManipulationN/A Apr 20 Samaneh Azadi : Towards Content-Creative AIN/A Apr 27 Daniel Seita : TBDN/A
Fall 2020 Schedule
Date Speaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AM Speaker 2: N/A or 11:30-11:50 AM Sep 1 Alvin Wan : What Explainable AI Fails to Explain (and how we fix that)Amir Gholami , Zhewei Yao : ADAHESSIAN: An Adaptive Second Order Optimizer for Machine LearningSep 8 Nikita Kitaev : Is Unstructured Computation All You Need for Natural Language Processing?Lucy Li : Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History TextbooksSep 15 Georgios Pavlakos: Learning to Reconstruct 3D Humans N/A Sep 22 Lerrel Pinto : Rich Robotic Supervision through Cheap Human DemonstrationsN/A Sep 29 Philippe Laban : Text Summarization Without The SummariesKatie Stasaski : More Diverse Dialogue Datasets via Diversity-Informed Data CollectionOct 6 Aldo Pacchiano : Learning to Score Behaviors for Guided Policy OptimizationN/A Oct 13 Paria Rashidinejad: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory Zhen Dong : Efficient Neural Networks Through Systematic QuantizationOct 20 Eugene Vinitsky : Optimizing Energy Efficiency of Traffic at Scale via Multi-agent Deep RLAnastasios Angelopoulos: Uncertainty for Black Box Models Oct 27 Misha Laskin : Escaping the Simulator: Accelerating Real Robot Learning through Unsupervised LearningN/A Nov 3 Wei Zhan : Close-loop Evaluation of Prediction Algorithms with Complex Scenarios from Naturalistic Driving DataLiting Sun : Modeling Human Behaviors in Human-robot InteractionsNov 10 Daniel Brown : Safe and Efficient Imitation LearningN/A Nov 17 Edward Kim: Scenic: Dynamic Scenario Description Language for Autonomous Systems Kimin Lee : Ensemble methods in reinforcement learningNov 24 Xin Wang : Last-Mile Delivery of Computer Vision with Test-Time AdaptationN/A Dec 1 Huijuan Xu : Temporal Action Detection in Videos with Multi-Level SupervisionN/A
Spring 2020 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50 Jan. 24 Dylan Hadfield-Menell : The Principal-Agent Value Alignment ProblemN/A Jan. 31 Somil Bansal : Safe and Data-efficient Learning for Physical SystemsN/A Feb. 7 Nick Antipa : Lensless Computational Imaging: Seeing More with LessN/A Feb. 14 Ruoxi Jia : Towards a Responsible Data Economy: Fairness, Privacy, and SecurityN/A Feb. 21 Chandan Singh : Interpreting and Improving Neural Networks via Disentangled AttributionsN/A Feb. 28 Alexei (Alyosha) Efros : Image Manipulation… And Ways to Detect ItN/A Mar. 6 Brijen Thananjeyan and Ashwin Balakrishna : Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic TasksJeff Ichnowski : Fog-Robotics Serverless and Deep Grasp-Optimized Robot Motion PlanningMar. 13 Aravind Srinivas : Self-Supervised Visual Representation LearningN/A Mar. 20 Ronghang Hu : Structured Models for Vision-and-Language ReasoningN/A Mar. 27 No Seminar N/A Apr. 3 Misha Laskin: Improving Reinforcement Learning with Unsupervised and Self-Supervised Learning N/A Apr. 10 Dequan Wang : Object-Centric Representation for Perception, Prediction, and PlanningN/A Apr. 17 Coline Devin: Learning with Modularity and Compositionality for Robotics Apr. 24 Cecilia Zhang : Bringing Cinema Quality Rendering into Casual Photos and Videos.N/A May 1 Sylvia Herbert : Safe Real-World Autonomy in Uncertain and Unstructured EnvironmentsN/A May 8 Sasha Sax: Robust Learning Through Cross-Task Consistency N/A May 15 Carlos Florensa : What Supervision Scales? Practical Learning Through InteractionN/A
Spring 2019 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50 Jan. 18 Eric Jonas : Structured Prediction via Machine Learning for Inverse ProblemsN/A Jan. 25 Dinesh Jayaraman: Towards Embodied Visual Intelligence N/A Feb. 1 Ke Li : Advances in Machine Learning: Learning to Optimize, Generative Modelling and Nearest Neighbour SearchN/A Feb. 8 Deepak Pathak : Self-Directed LearningN/A Feb. 15 Samaneh Azadi : Rectify the GAN Generator Distribution by Rejecting Bad SamplesSasha Sax: On Perception for Robotics: Mid-level Visual Representations Improve Generalization and Sample Complexity for Learning Active Tasks Feb. 22 Jaime Fisac : Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are WrongN/A Mar. 1 Fisher Yu : Towards Human-Level Recognition via Contextual, Dynamic, and Predictive RepresentationsN/A Mar. 8 Andrew Owens : Sight and SoundN/A Mar. 15 Daniel Fried : Pragmatic Models for Generating and Following Grounded InstructionsNikita Kitaev : Syntactic Parsing with Self-AttentionMar. 22 **Oriol Vinyals : AlphaStar: Mastering the Real-Time Strategy Game StarCraft II (**This will be held 11am-12pm in 250 SDH**) N/A Mar. 22 Jacob Steinhardt : What Makes Neural Networks (Non-)Robust?N/A Mar. 29 Holiday Holiday Apr. 5 Alex Lee : Visual Dynamics Models for Robotic Planning and ControlN/A Apr. 12 Lisa Anne Hendricks: Visual Understanding through Natural LanguageN/A Apr. 19 Evan Shelhamer : Blurring the Line between Structure and Learning for Adaptive Local RecognitionN/A Apr. 26 Yian Ma : Bridging MCMC and OptimizationN/A May 3 Sandy Huang : Optimizing for Robot TransparencyN/A May 10 Jeffrey Regier: Statistical Inference for Cataloging the Visible Universe N/A
Fall 2018 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50 Aug. 24 Jaime Fisac , Andrea Bajcsy , Sylvia Herbert : Probabilistically Safe Robot Planning with Confidence-Based Human PredictionsN/A Aug. 31 Aviv Tamar : Learning Representations for PlanningN/A Sept. 7 Chi Jin: Is Q-learning Provably Efficient? N/A Sept. 14 Wojciech Zaremba : Learning dexterityN/A Sept. 21 Tuomas Haarnoja: Acquiring Diverse Robot Skills via Maximum Entropy Reinforcement Learning N/A Sept. 28 Professor Ruzena Bajcsy : Data Driven vs. Model Driven Analysis of Human Ability for Human Robot InteractionN/A Oct. 5 Ke Li : Implicit Maximum Likelihood EstimationN/A Oct. 12 Roy Fox : Multi-Task Hierarchical Imitation Learning of Robot SkillsN/A Oct. 19 Ankush Desai: DRONA: A Framework for Programming Safe Robotics Systems N/A Oct. 26 Ajay Tanwani : A Fog Robotics Approach to Large Scale Robot LearningLaura Hallock : Human Muscle Force Modeling for Enhanced Assistive Device ControlNov. 2 Abhishek Gupta : Unsupervised (Meta) RLAnja Rohrbach : Diagnosing and correcting bias in captioning modelsNov. 9 Holiday Holiday Nov. 16 Daniel Fried : Pragmatic Models for Generating and Following Grounded InstructionsNikita Kitaev : Syntactic Parsing with Self-AttentionNov. 23 Holiday Holiday Dec. 7 Jiantao Jiao : Deconstructing Generative Adversarial NetworksN/A
Spring 2018 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50 Jan. 19 Pulkit Agrawal : Continually Evolving Machines: Learning by ExperimentingN/A Jan. 26 Sanjay Krishnan: Dirty Data, Robotics, and Artificial IntelligenceN/A Feb. 2 David Fouhey : Towards a 3D World of InteractionN/A Feb. 9 Saurabh Gupta : Visual Perception and Navigation in 3D ScenesN/A Feb. 16 Jacob Andreas: Learning from Language N/A Feb. 23 Chelsea Finn: Generalization and Self-Supervision in Deep Robotic Learning N/A Mar. 2 Jennifer Listgarten : Where genetics and biology meet machine learningN/A Mar. 9 Yi Ma: Low-dimensional Structures and Deep Models for High-dimensional Data TBD Mar. 16 Cathy Wu : Mixed-autonomy mobility: scalable learning and optimizationN/A Mar. 23 Xiang Cheng: Langevin MCMC as gradient flow over the probability space Eric Tzeng and Andreea Bobu : Domain Adaptation for Fixed and Continuously Varying DomainsApr. 6 Shubham Tulsiani: Learning Single-view 3D Reconstruction of Objects and ScenesTBD Apr. 13 Tinghui Zhou: Beyond Direct Supervision: Visual Learning via Data-driven Consistency Consistency (Location: SWARM Lab 490 Cory)N/A Apr. 20 Richard Zhang : Image Synthesis for Self-Supervised Representation LearningN/A Apr. 27 Reza Abbasi-Asl: Structural compression of Convolutional Neural Networks Fereshteh Sadeghi : Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control
Fall 2017 Schedule
Date Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10 Aug. 25 Jitendra Malik : What have we learned from datasets in computer vision?Angjoo Kanazawa : Single-View 3D Reconstruction of Deformable Objects like Animals and PeopleSept. 1 Chang Liu: Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection Claire Tomlin : Safe LearningSept. 8 Andrew Critch : Open-source game theory is weirdAnna Rohrbach : Generation and grounding of natural language descriptions for visual dataSept. 15 Bo Li : Secure learning in adversarial environmentsChing-Yao Chan: Safety of Automated Driving Systems (ADS) and AI/ML – A DialogueSept. 22 Alex Anderson : The High-Dimensional Geometry of Binary Neural NetworksJeff Mahler: Learning Deep Policies for Robot Bin Picking using Discrete-Event Simulation of Robust Grasping Sequences Sept. 29 Fisher Yu : Towards Universal Representation for Image RecognitionMax Rabinovich: Abstract Syntax Networks for Code Generation and Semantic Parsing Oct. 6 Michael Laskey: Learning Home Robotics Manipulation Tasks from Remote Supervision Pulkit Agrawal: Continually Evolving Agents: Curiosity & Experimentation Oct. 13 Ron Fearing : Dextrous LocomotionSylvia Herbert and David Fridovich : Planning, Fast and Slow with FaSTrack: A Framework for Adaptive Real-Time Safe Trajectory PlanningOct. 20 Emrah Bostan : Learning Convex Regularizers for Optimal Bayesian DenoisingSomil Bansal : Overcoming Model Bias in Model-based LearningOct. 27 Roberto Calandra Aaditya Ramdas: Sequential testing and online false discovery rate control Nov. 3 Roy Fox : Discovery of Hierarchical Structures for Robot Learning and Neural ProgrammingLeila Wehbe : Modeling brain responses to natural language stimuliNov. 10 Holiday Holiday Nov. 17 Jacob Andreas: Learning with Latent Language Niladri Chatterji: Alternating minimization for dictionary learning with random initialization Nov. 24 Holiday Holiday Dec. 1 Eric Jonas : Could a neuroscientist understand a microprocessorTuomas Haarnoja: Soft Q-Learning
Spring 2017 Schedule
Date Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10 Jan. 20 Jack Gallant: Melding neuroscience and computer scienceJavad Lavaei: On optimization theory, numerical algorithms and machine learning for nationwide energy systemsJan. 27 Trevor Darrell: Adaptive Learning of Driving Models from Large-scale Video DatasetsColine Devin: Transfer learning for robotics Feb. 3 Phillip Isola: Image-to-Image Translation with Conditional Adversarial NetworksAnil Aswani: Human Modeling Using Inverse OptimizationFeb. 10 Zeynep Akata: Generating Fine-Grained Visual Explanations and Realistic ImagesDeirdre Mulligan: Hand-offs in AI: Functional Fidelity, Values, and GovernanceFeb. 17 (3:10- 3:25) Jaime Fernandez Fisac: Generating plans that predict themselves/ (3:25-3:40) Aaron Bestick: Implicitly Assisting Humans to Choose Good Grasps in Robot to Human Handovers William Guss: WaveLayers: Neural Topology Inspired by TopologyFeb. 24 Laurent El Ghaoui: Safe feature elimination in sparse learningLaura Waller: Computational Microscopy with sparsityMar. 3 Chi Jin: How to Escape Saddle Points Efficiently Horia Mania: Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings Mar. 10 Matthew Matl: Automated Grasp Transfer with Mesh Segmentation and Point Cloud RegistrationPaul Grigas : An Extended Frank-Wolfe Method with “In-Face” Directions, and its Application to Low-Rank Matrix CompletionMar. 17 Roy Fox : Multi-Level Discovery of Deep OptionsParvez Ahammad : Beyond navigation metrics: Teaching computers how users perceive web application performanceMar. 24 Ke Li : Learning to Optimize and Fast k-Nearest Neighbour SearchJessica Hamrick: Metacontrol for Imagination-Based OptimizationApr. 7 Deepak Pathak : Exploring Four Axes of Self-SupervisionSaurabh Gupta : Cognitive Mapping and Planning for Visual NavigationApr. 14 Jeff Mahler : Dex-Net: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp MetricsJudy Hoffman : A General Framework for Domain Adversarial LearningApr. 21 Fereshteh Sadeghi : Sim2Real Collision Avoidance for Indoor Navigation of Mobile Robots via Deep Reinforcement LearningEvan Shelhamer : Loss is its own Reward: Self-Supervision for Reinforcement LearningApr. 28 Roberto Calandra : Robustness in Multi-objective Bayesian optimizationRichard Zhang: Cross-Channel Visual PredictionMay 5 Abhishek Gupta : Imitation Learning for Dexterous Manipulation and Transfer in Reinforcement LearningTinghui Zhou : 3D Visual Synthesis and Understanding from 2D ViewsMay 12 Karl Zipser : New Trajectories in the Autonomous Model Car ProjectDaniel Drew : Silent Swarms: Flying Microrobots Using Atmospheric Ion Thrusters
Fall 2016 Schedule
Date Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10 Aug. 26 Ken Goldberg: Deep Dexterity Sanjay Krishnan: Inverse Reinforcement Learning For Sequential Robotic Tasks Sep 2 Anca Dragan: How Robots Influence Our Actions Jeff Donahue: Adversarial Feature Learning Sep 9 Robert Nishihara & Philipp Moritz: Distributed Machine Learning with Ray John DeNero: Interactive machine translation Sep 16 Joseph Gonzalez: Prediction Serving and RISE Kevin Jamieson: Bayesian Optimization and other bad ideas for hyperparameter tuning Sep 23 Stuart Russell: Human-Compatible AI Marcus Rohrbach: Explain and Answer: Intelligent systems which can communicate about what they see. Sep 30 Richard Zhang: Colorful Image Colorization Virginia Smith: A General Framework for Communication-Efficient Distributed Optimization Oct 7 Gregory Kahn: Learning Control Policies for Partially Observable Safety-Critical Systems John Canny: Accountable Deep Networks Oct 14 Ruzena Bajcsy: Individualized Human Models for Cyberphysical Interaction Jacob Andreas: Neural module networks Oct 21 Dave Moore: Bayesian seismic monitoring from raw waveforms Greg Durrett: Data-Driven Text Analysis with Joint Models Oct 28 Jun-Yan Zhu: Visual Manipulation and Synthesis on the Natural Image Manifold Dylan Hadfield-Menell: The Off-Switch Nov 4 Bin Yu: Artificial neurons meet real neurons: building stable interpretations of V4 neurons from CNN+regression models Pulkit Agrawal: Forecasting from Pixels: Intuitive Physics and Intuitive Behavior Dec 02 Chelsea Finn: Adversarial Inverse Reinforcement Learning Aviv Tamar: Deep Policy Representations based on a Planning Computation Dec 09 Michael Laskey: A Human-Centric Approach to Deep Robotic Learning from Demonstrations. Michael Oliver : Using artificial neural networks to model visual neuronsDec 16 Lisa Anne Hendricks: Localizing Moments in Video with Natural Language Andrew Owens: Learning visual models from paired audio-visual examples